A paper written by my classmate and I. Using Euromonitor reports, recent press releases, and more widely available resouces, we attempted to make sense out the specialty coffee house industry. In our paper, we imagined that we were preparing this report for International Coffee & Tea, the parent company of Coffee Bean & Tea Leaf stores. [Note that footnotes and references, including tables and graphics, in our paper seemed to have disappeared once I transferred the paper to this blog. Will try and rectify that as soon as possible. In the meantime, if you need the sources asap, you can insert it in your comment or send me an email or tweet me.]

Coffee Bean & Tea Leaf – Specialty Coffee House

INTRODUCTION

International Coffee & Tea is the parent company of Coffee Bean & Tea Leaf. Founded in 1963, The Coffee Bean & Tea Leaf is a chain of specialty coffee and tea stores. Through its more than 315 stores in California, Arizona, Nevada, Singapore, Malaysia, Korea, Brunei, Indonesia, the United Arab Emirates, Israel, Australia, China, Kuwait, Egypt, Qatar and the Philippines, the company serves more than 100 million coffee and tea beverages throughout the world annually, with annual sales of US$73.6.

The company’s products include coffees, teas, powders and extracts, brewing equipment and grinders, tea accessories, mugs and tumblers, cookies and sweets and the Ice Blended brand of drinks and drink-making kits. International Coffee & Tea is headquartered in Los Angeles.

Over the past five years, The Coffee Bean & Tea Leaf® has grown internationally (in 22 countries) from 217 stores to over 750 at the end of 2008, serving more than 100 million beverages each year, including over 20,000 Ice Blended® drinks each day. Outside of the United States, The Coffee Bean & Tea Leaf has presence (outside USA) in Brunei (4 outlets), Malaysia (10 outlets in East Malaysia; 40 outlets in Peninsula Malaysia), China (23 outlets), India (5 outlets), Indonesia (30 outlets), South Korea (163 outlets), Philippines (25 outlets), Singapore (45 outlets), Sri Lanka (3 outlets), Vietnam (3 outlets), Bahrain (3 outlets), Egypt (11 outlets), Kuwait (11 outlets), Oman (1 outlet), Qatar (5 outlets), Saudi Arabia (7 outlets), United Arab Emirates (16 outlets), Israel (14 outlets) and Mexico (5 outlets). Stores in Peninsula Malaysia and Singapore are company owned and operated. Stores in other territories are run by franchise operators. Our international expansion out of USA has been done via franchising and the opening of company-operated stores. For growth in number of stores annually, see Appendix A.

The Coffee Bean & Tea Leaf® can be found in neighborhood locations and other nontraditional locations such as – grocery stores, shopping malls, military bases, airports and university campuses. Potential for expansion still available for franchising opportunities in Europe, parts of Middle East, parts of Asia, Australia, New Zealand, South America, Central America (other than Mexico) and Canada.

With the global financial crisis impacting our business in 2008, how can we at Coffee Bean & Tea Leaf, maintain our markets, increase our revenues and expand our markets?

CURRENT STATE OF THE SPECIALIST COFFEE SHOP INDUSTRY

Although accounting for only 7.5% of sales in the US$395 billion cafés/bars sector, specialist coffee shops have been the growth leader over the year 2008, as consumers demand a caffeine jolt in a relaxed, up market “third place” between home and work/school. Although value sales growth in specialist coffee shops has outrun unit expansion in most years, it trended downwards in 2007, amid slowing consumer spending in many major markets, as well as further expansion into lower-income emerging markets. In 2008, it dropped further, brought down by a severe slowdown at Starbucks, by far the largest chained operator, which closed hundreds of outlets due to falling sales and consumer traffic.

In Singapore, chained specialist coffee shops was the fastest growing subsector in 2008 with value sales rising 15%, due mainly to major chains proliferating in both city and suburban areas. Coffee Bean & Tea Leaf and its closest rival, Starbucks, expanded aggressively into heartland malls to compete against each other. See Appendix B for more details of the industry in Singapore.

Western Europe and Asia Pacific continue to be the primary sales drivers in cafés/bars, owing to well-entrenched café and drinking cultures, with just five markets (Spain, Japan, Italy, the UK and South Korea) accounting for half of 2008 global sales, while together the two regions combined for well over half the world’s outlets. Growth collapsed in North America in 2008, with the US housing meltdown and its subsequent effect on consumer spending having a hugely negative impact on discretionary cafés/bar purchases. Starbucks saw sales fall by US$470 million compared with 2007, a far cry from the double-digit annual growth the chain has enjoyed over the last 10 years. As they have elsewhere, consumers in the US and Canada have cut back on visits to cafés and bars, spending less when they choose to indulge, and purchasing more from supermarkets and other retailers. The strong push into coffee led by fast food retailers such as McDonald’s and Dunkin’ Donuts has siphoned traffic away from the previously booming specialist coffee shops sector. With the lackluster results posted by the industry, Starbucks’ posting of it latest financials were eagerly awaited.

On 5 November 2009, Starbucks announced strong 4th quarter 2009 results. Although net revenues were $1.7 billion in Q409 compared to $1.8 billion in Q408, with the decline due to decreased revenues from fewer company-operated retail stores; non-GAAP U.S. operating income for Q409 was $206.8 million compared to $79.5 million for the same period a year ago. Non-GAAP operating margin expanded to 12.0% in Q409 compared to 4.4% in the corresponding period of fiscal 2008. As illustrated by the Starbucks’ results, survival in the industry will depend on higher productivity (lower costs, increased labour efficiency and reducing product waste); brand differentiation; an increased focus on food is key as more operators look to drive traffic at all times of day; and the continuation of the “third place” principle as experience will remain all-important, even in a time of economic difficulty, consumers will still demand a “third place” for relaxation and socializing.

COMPETITORS: INDUSTRY RIVALRY

Within the territories in which the Coffee Bean & Tea Leaf operates, the competitive landscape is summarized below.

US

Despite its recent decline, Starbucks Corp is still the leader in the US chained coffee specialists subsector, with an 88% value share in 2008. Next on the list is Caribou Coffee with 3% share of total outlets in 2008 (about 60% of its outlets are located in places posing little threat to Starbucks – Minnesota or Illinois). Market saturation and maturity will affect this sector over 2009. Although some chained and independent coffee shops, cafés and bars will close, there is still an overabundance of specialty coffee outlets, and currently demand does not equal supply. More outlets are expected to close in the coming years, with the number predicted to decline by 3% over the forecast period.

Singapore

In Singapore, the three largest chain specialist coffee shops are Starbucks, The Coffee Bean & Tea Leaf, The Coffee Connoisseur (owned by Sarika Coffee Co (S) Pte Ltd. Other smaller competitors are Coffee Club, Spinelli’s, Pacific Coffee, Gloria Jeans, Trung Nguyen Cafe Chain, San Francisco Coffee and Coffee Connection. See Appendix B for more of their brand shares in Singapore. Competition is high between the players. Leading companies are Starbucks Coffee Singapore Pte Ltd and Coffee Bean & Tea Leaf (Singapore) Pte Ltd, both of which compete against emerging cafés/bars which have started offering similar ranges of premium quality roasted coffee along with a wide menu of desirable food choices.

 The latest newcomer to the scene is Trung Nguyen Coffee, a Vietnamese coffee chain with over 1,000 retail establishments in Vietnam is an emerging franchise brand that focuses solely on brewing Vietnamese coffee. At the end of 2008, it had three outlets in Singapore and is slated to open another four more in 2009. Competition between specialist coffee operators is expected to intensify over 2009 as each will drive for high-traffic locations in upcoming malls and integrated resort projects.

Malaysia

Smaller international as well as local chains such as San Francisco Coffee Co, Gloria Jean’s and Hoca Coffee registered weaker performance than large international chains such as Starbucks and The Coffee Bean & Tea Leaf. In 2008, we, Coffee Bean & Tea Leaf advertised aggressively through print media as well as in-store advertisements during its launch of The Coffee Bean Card in January 2008. Both Starbucks and The Coffee Bean & Tea Leaf invested sizeable resources in advertising and promotions to boost their businesses and enhance their brand names. Over the forecast period, chained specialist coffee shops are likely to expand to office buildings so as to target the working class, who are more willing to splurge on high-end coffee to indulge, comfort and energise themselves while being hard at work.

Rivalry is high but scope for expansion for the brands is high as theyare likely to expand to East Malaysia in line with the rapid development of retailing landscape in this second-tier region of the country. In keeping with the high rivalry, prices of items at cafés/bars are likely to drop gradually over the forecast period with competition intensifying, with players likely to offer more promotions or even value set meals to attract consumers.

Philippines

In 2008, Starbucks saw the largest leap in shares in terms of value sales from 45% in 2007 to 49% in 2008, and remains at the top. Other competitors are Seattle’s Best, Gloria Jeans, Coffee Bean and Tea Leaf, local outfits Figaro Coffee Co and Bo’s Coffee. Emerging local brands like Café de Lipa are keen to diversify away from the Western coffee shop image in terms of pricing and product line (using local coffee brews). Prospect for the next few years is this: specialty coffee shops will continue to improve their performance in the market by pushing strategies aimed at younger age groups, focusing more on the young professional, mid rank employees and private school students, besides their typical market of accomplished professionals and executives. The aggressive expansion strategy of players breeds greater competition among domestic and local players.

South Korea

In South Korea, the biggest competitor remains Starbucks as it continued to dominate the specialist coffee shop segment during 2008. In recent years, we have seen new competitors in local chained brands like Holly’s and Angel-in-us. Starbucks has 271 outlets, while we, Coffee Bean & Tea Leaf have 163 outlets . Prospects for this sector for period 2008 to 2009: Demand for espresso coffee will increase over the forecast period at an annual average rate of 2%. However, owing to increasing maturity of this sector, the growth in the number of new specialty coffee stores will be lower over the forecast. Local specialty coffee chains are expected to continue to increase their value shares at the expense of leading international chains.

India

Domestic brands, Café Coffee Day, Coffee Day Express, Barista and Java Green, dominate the specialist coffee shop sector in India. Multinational player, Costa Coffee (sixth largest in brand share) , Gloria Jeans and Coffee Bean & Tea Leaf will provide strong competition in the period 2008 to 2009. The rivalry will be intensifying as Gloria Jean’s Coffee and The Coffee Bean & Tea Leaf are expected to give tough competition to brands such as Barista and Café Coffee Day which have lost their novelty factor in the big cities. As such, the competition in this category is expected to intensify over 2008 to 2009. International brands like Starbucks and Aroma Café are set to enter into the Indian market in the period 2008 to 2009. As the specialist coffee shops landscape gets more saturated, outlet expansion is expected to slow over the next few years. The brands will find it increasingly difficult to differentiate themselves and find it increasingly difficult to compete head-on for lucrative locations and the same urban youth consumer base.

Indonesia

Global brands Starbucks and The Coffee Bean & Tea Leaf, occupied the first and third positions in chained specialist coffee shops, respectively, in 2008, while a local brand Excelso Café managed to rank second. Starbucks and The Coffee Bean & Tea Leaf are found mainly in major cities, Excelso Café (operated by Excelso Multirasa PT), on the other hand is also found in smaller cities like Bogor, Yogyakarta and Malang,. Prices of products offered by Excelso Café are more affordable compared to Starbucks and The Coffee Bean & Tea Leaf. Therefore, it is able to reach a wider consumer base. Specialist coffee shops will be expected to continue to show the fastest CAGR within café/bars in terms of outlets, volume transactions and constant value sales over in 2008 to 2009. As it is emerging from a smaller base, companies are expected to be more aggressive in terms of promotions and expansion. One area international chained coffee shops could attract market share is in ‘localizing’ a few of their menu items to suit local palates. Apart from this, these outlets could attract middle- and upper-income consumers to meet communally in their outlets.

China/Hong Kong

Chained specialist coffee shops continued to be led by Starbucks, which opened its 100th outlet in April 2008. Behind it is Pacific Coffee. As the coffee consumption market reaches maturity, both Starbucks and Pacific Coffee are digging deep into their traditional market of coffee drinkers. This is evidenced by the location of their stores: across Hong Kong Island and Kowloon, targeting the working class and those willing to pay a premium price for coffee.

Israel

All of the specialist coffee shops in Israel carry local brands, with the exception of Coffee Bean & Tea Leaf (who seem to be the only foreign multinational player); with Starbucks clearly missing in the lineup of specialist coffee brand names. Local specialist coffee set ups are, to name a few: Aroma Espresso Bar, Café Café, Café Joe and Arcafe. Chained specialist coffee shops are expected to continue to expand over the forecast period despite the recession. Nevertheless, Aroma Israel and Cafe Cafe claim that in 2009 they will need to be more careful in location choice and amount of outlet expansion. Smaller chain Café Joe prefers to focus and improve on its existing facilities. The need for company differentiation will continue to be a strong company driver, as the focus on health and healthy lifestyles will continue to play a strong roll in company differentiation. Consumers are becoming more concerned with issues such as fair trade, recycling, organic and social responsibility.

In summary, for the territories within which Coffee Bean and Tea Leaf operates, the rivalry among competitors in the industry is high. The global economic crisis has raised the level of competition between the competitors even more. Companies are required to compete on all fronts – price, selection of products (which includes not only espresso items, but also teas, pastries and other food items), brand differentiation (e.g. in terms of promotion of a healthy lifestyle or social responsibility), and location.

For territories within which Coffee Bean & Tea Leaf does not operate, the situation is more diverse. The paragraphs below summarise the competition between companies in a selection of those territories. Four have been chosen, UK and France as a representative sampling of Europe; Brazil and Argentina as a representative sampling of South America.

UK

The rivalry between specialist coffee houses is high. With a few chains competing against eachc other for a share of the market. Starbucks had increased the number of outlets by 40 locations in 2008 (rising to 650 stores) but had performed poorly over the year. Increased competition from brands such as Costa Coffee and Caffè Nero, and poor macroeconomic conditions overall lowered the Starbuck’s average sales per outlet. Costa Coffee brand achieved the strongest growth in 2008, and is now the leader in chained coffee shops in UK. Rivalry within the sector is high with branded players, such as Costa Coffee, The Wild Bean Café and Caffè Nero, continuing to increase the numbers of their locations over 2008 to 2013 period.Starbucks is likely to see many of its UK outlets continuing to close into 2009 and 2010.

France

Starbucks Coffee France SA has a massive 41% share and McDonald’s France SA with 16%. There is strong competition between Starbucks Coffee and McCafé. [For more on McCafe, see section on New Entrants below.] Chained specialist coffee outlets are likely to expand their network in all major French cities. As the concept of coffee shops is particularly well-known by tourists and young adults due to their travels, chained coffee shops are likely to open new outlets in cities such as Bordeaux and Strasbourg which attracts both tourists and students; and will increase their presence in semi-captive environments such as airports and rail stations. The outlook for specialist coffee shops here is that as the category is still at an early development stage, it should still enjoy healthy growth, at a projected 16% CAGR in constant value terms.

Brazil

The top four leading companies are Rei do Mate, Água Doce, Café Pelé and Fran’s Café, together accounted for more than 55% of value sales in 2008. Other companies include Starbucks , Suplicy Café, Octavio Café, In Bocca al Lupo, Café Ritazza and McCafe. Newcomers such as Havanna Café, Vanilla Caffé and Pelé Arena & Futebol have plans to continue to expand despite the economic slowdown. The outlook is that specialist coffee shops, especially those with a franchise system and able to reach states in which they are not yet present, will show the fastest growth in terms of outlets, transactions and value sales over 2008 to 2009. McCafé, Grão Espresso, Rei do Mate, Vanilla Caffé and new Pelé Café Arena & Futebol are likely to outperform in 2008 and 2009.

Argentina

There are many major players, nearly all local, in the specialist coffee shops sector here: including, Havana Café, Bonafide, Café Martinez, The Coffee Store, McCafe, Aroma Café and Starbucks. There is low penetration of international chains in this market – they represent a 0.5% share of the total sales in cafés/bars, and 7% of chains. This is mainly due to the fact that their global concept is not adapted to the tastes of demanding Argentinean consumers. Although the international chains have a small presence, there is still margin for growth in this sector. Particularly, there is still room for growth of outlets in high income areas and shopping centres.

Potential new markets for Coffee Bean & Tea Leaf

Of the four territories set out above, Coffee Bean & Tea Leaf has most potential of penetrating into France, Argentina and Brazil. The reasons are:

1. For France, the outlook for specialist coffee shops here is that as the category is still at an early development stage, it should still enjoy healthy growth.
2. For Brazil, specialist coffee shops, especially those with a franchise system and able to reach states in which they are not yet present, will show the fastest growth in terms of outlets.
3. For Argentina, there is still room for growth of outlets in high income areas and shopping centres.
4. There is already in place in the 3 territories a coffee drinking culture.

Industry rivalry in the form of fast food operators

Since 2008, Euromonitor has included McCafe as a chained specialist coffee shop. McCafe, owned by McDonald’s, has been making inroads into the coffee drinking culture using current resources provided by its McDonald’s outlets. In Europe, McCafe has yet to penetrate UK. However, McCafé outlets have been growing in number in Italy where it has 65. In Argentina and Brazil, McCafe is positioned ahead of Starbucks in brand shares rankings. In the US, towards the latter half of 2008, Dunkin Donuts and McCafe pirated the premium coffee culture trend started by Starbucks, and both players started making inroads into Starbuck’s customer base with increased offering in the premium coffee category.

According to the 2009 Brand Keys Customer Loyalty Engagement Index, Dunkin Donuts and McDonald’s were placed first and second, respectively, and Starbucks, third in the coffee category. This indicates that customers preferred the two fast-food brands rather than the “pure” specialist coffee shops. Coffee Bean & Tea Leaf should, therefore, consider fast food brands like Dunkin Donuts and McCafe as competitors within the industry, and strategise with these players in mind. The impact of the industry rivalry force created by the competition between specialty coffee retailers is high.

The growth of the industry has slowed while the number of competitors within the industry has increased. Both of these factors, in addition to Dunkin’ Donuts and McDonald’s high strategic stakes in the specialty coffee industry and continued focus on selling high-quality specialty coffee beverages at a low cost have caused strong industry rivalry.

POTENTIAL NEW ENTRANTS

The economies of scale within the specialty coffee industry have increased as the size of the top players has increased.

1. Increased economies of scale within the specialty coffee sector.
2. Companies such as Dunkin’ Donuts and McCafe have national distribution channels through which they can transport their specialty coffee at a relatively low cost compared to potential new entrants who have no such developed distribution systems.
3. These larger companies are able to reap economies of scale through their purchasing by negotiating long term contracts with coffee farmers and purchasing coffee beans in bulk quantities at discount prices.
4. Most of the favorable store locations within the larger metropolitan areas have already been occupied by current competitors within the specialty coffee industry.
5. Many companies now have proprietary product technology involved in the production of their specialty coffee as well as lower per unit costs due to an experience curve.
6. The industry has progressed toward more subjective traits such as the ambience of the store, the social responsibility of the company and brand identification.
7. Many companies have gained very loyal customer bases stemming from their past advertisements, customer service, objective product differentiations and early entry into the industry.

From the observations made above, we concluded that the barriers to entry in the specialty coffee industry are very high.

SUBSTITUTES

The global financial crisis in 2008 has made cheaper substitutes to specialist coffee products more desirable, as they are cheaper and, hence, more accessible. As specialist coffee is highly dependent on the high level of disposable income available, the financial crisis has reduced this disposable income causing a fraction of customers to choose cheaper alternatives for their daily caffeine fix. Cheaper local brews are dominating the market in several territories.

In Singapore, e.g. traditional coffee houses such as Ya Kun Kaya Toast and Ah Mei Café are expected to blossom over the short term. These modernised coffee houses provide a sense of familiarity in a comfortable setting. In Malaysia, local coffee shops known as kopitiams, are found spread widely across the rural landscape of Malaysia. These outlets serve breakfast as well as other local fare. In South America, the local specialist coffee chains have been offering brews for lesser than those offered by international chains.

Apart from the cheap local brews, other substitutes for caffeine fix are:

1. The caffeinated soft drinks offered by Pepsi and Coca-Cola. However, these substitute products pose little threat to the premium coffee industry.
2. Coffee has gradually gained preference over carbonated soft drinks. This is mostly attributed to the health concerns associated with carbonated soft drinks and the new evidence showing coffee as a relatively healthy alternative. (Harding, 2000)
3. Tea. Although tea products have become part and parcel of the menu for chained specialist coffee houses, there are companies which concentrate on just serving tea. However, tea houses pose little threat to the specialist coffee houses.

The threat from substitutes is low to medium, with the players serving the local cheaper brews ranking higher than the others. To minimize this threat, Coffee Bean & Tea Leaf should remain on its present course in providing quality products (beverages and food items) in a pleasant ambience (the third place).

For territories where there are strong local tastes in certain foods or beverages, we should allow the franchise operators flexibility in adapting to those tastes.

CUSTOMERS

1. The primary customers are individual consumers who are middle to high income earners, with a fair amount of disposable income.
2. Customers of specialist coffee houses face no switching costs and have an enormous selection of retailers from whom they can buy.
3. The threat of backward integration (in terms of the losing the customers as they could provide the service themselves) is minimized by specialist coffee houses themselves offering for sale premium coffee beans and coffee brewing equipment.

The bargaining power of customers has increased: With the global financial crisis hurting the main group of customers the most, this group has modified their consumption patterns. This is evidenced by the drastic closure of stores by the largest chained specialist coffee houses, Starbucks. This group of customers, with less disposable income looks for cheaper substitutes, or reduces their number of visits – cutting back and trading down.

For trading down, customers can either opt for cheaper substitutes or brew their own cuppa. For the latter, Coffee Bean & Tea Leaf have for sale, premium coffee beans and coffee brewing equipment. In line with this, Starbuck’s strategy of offering instant coffee for sale in the form of VIA needs to be watched closely.

We can also watch carefully how we price our products – seeking possible ways to provide value for money to our customers via meal offerings (beverage plus food item at discounted prices) and customer loyalty programs. Apart from the above, to maintain customer levels we must aggressively promoted themselves as necessary parts of the day — not merely as places to have morning coffee or coffee after work — with emphasis on food and ambience, and promoting our outlets as places to enjoy good-quality food and coffee in a pleasant environment.

SUPPLIERS

Coffee is a tropical plant which grows between the latitudes of 25°N and 25°S (see Appendix C). It requires very specific environmental conditions for commercial cultivation. The coffee plant is easily damaged by frost, a danger either in southern Brazil or, closer to the Equator, at altitudes around 2000 metres. Prices of coffee also fluctuate as the weather dictates. Nevertheless, any price increase in the raw material is not directly benefited by the growers, to a large extent – the increases benefit only the middlemen. We, at Coffee Bean are cushioned from price increases as we source our beans direct from the growers. Inadvertently, this process allows our suppliers much more bargaining power. However, we also view our relationship with the growers as long-lasting, and nurture it as such. Nevertheless, our selection guidelines for coffee beans soften the impact of higher supplier bargaining power.

Note: A development to monitor is the trend for coffee producing countries to take over the direct selling of coffee beans they produce.

A substantial cost for our business is property leasing costs. With the financial crisis still not showing significant improvements and the overall subdued sentiment of the property markets worldwide, we are confident that there will be no significant upward trend in property rents.

SUMMARY OF ANALYSIS

After applying Michael Porter’s five forces model to Coffee Bean & Tea Leaf environment, it can be inferred that the force created by industry rivalry has resulted an extremely competitive environment where differentiation is increasingly difficult and price wars are looming. The strength of the force imposed by the potential for new entrants is lower as a result of more formidable barriers to entry. The bargaining power of both suppliers and buyers are high as a result of increased unity among the suppliers and the accessibility of information among buyers. The threat of substitutes is significant given the cheaper substitutes available in the market.

OUR RECOMMENDATIONS

1. Maintain our current prices: The global financial crisis has not recovered. Any shifts upward in our pricing could result in backlash from our current customers.

2. Increase our value-for-money offerings: We can do so in packaging meals so that customers pay lower for paired items; and through our store loyalty programs. We should increase publicity in our store loyalty programmes.

3. Expand our selection of foods: This is to ensure we attract customers all day long – not only in the early mornings and before dinner. With regard to this, it is also wise for us to consider the local tastes of the customers and allow our stores to design products that are suitable to these tastes and preferences.

4. Enhance our product development efforts: As our customers cut back and trade down, we should consider these as opportunities to develop products that will fulfill their needs. An possibility is to develop our own brand of instant coffee. Additionally, as we are also offering for sale equipment for coffee brewing, we may enhance our customers experience by conducting workshops to help them brew a better cup of coffee.

5. Expand into territories which offer potential, like France, Brazil and Argentina. These territories offer opportunities for growth, and they are suitable in that there is present a coffee drinking culture. Moreover, in Brazil and Argentina, we have the added benefit of reduction in cost of transportation for our coffee beans.

6. Increase productivity and reduce waste: We need to discover ways in which our in-store productivity increases through better management of our supply chain management, and work processes.

Appendix A

Annual growth in number of stores

Year  Company owned Frachise

2002: 177 / 46

2003: 191 / 63

2004: 217 / 87

2005:  241 / 137

2006:  270 / 230

2007:  288 / 377

2008 : 278 / 444

Source: Coffee Bean & Tea Leaf. 2009. Company Growth. Retrieved from http://www.coffebean.com.

Appendix B

Singapore: Specialty Coffee Shops – brand shares

Brand Shares of Chained Cafés/Bars 2005-2008 % value Brand Global Brand Owner 2005 2006 2007 2008

Starbucks 17.4 19.1 18.7 18.0

Coffee Bean & Tea Leaf 21.9 20.5 18.7 17.2

Coffee Connoisseur, Sarika Coffee Co (S) 11.6 13.8 15.3 15.9

Bakerzin Baker’s Inn Holdings 5.6 4.4 4.1 5.8

Coffee Club Coffee Club Pte Ltd 4.3 4.6 4.7 4.6

Ya Kun Kaya Toast Ya Kun International 4.3 4.2 4.7 4.3

Harry’s Bar Harry’s International 2.1 2.1 4.6 4.2

Spinelli Spinelli Coffee Co Pte Ltd 3.5 3.4 3.2 3.3

Dôme Dôme Coffees Australia 3.2 2.6 3.1 3.1

Pacific Coffee Pacific Coffee Co 2.4 2.8 3.0 3.0

Killiney Kopitiam Killiney Kopitiam Pte Ltd 1.8 1.8 3.0 2.6

Gloria Jean’s Jireh International Pty – - 0.9 1.8 Ltd

McCafé McDonald’s Corp 0.5 0.7 1.2 1.2

Trung Nguyen Trung Nguyen Cafe Chain – - – 1.0

San Francisco Coffee Co – - – 1.0

Caffè Ritazza SSP Group Ltd – 0.5 0.5 0.4

Europa Pubs & Bars ABR Holdings Ltd 2.6 – - -

Caffè Ritazza Compass Group Plc 0.5 – - -

Others 18.4 19.6 14.3 12.5

Total 100.0 100.0 100.0 100.0

Source: Trade associations, trade press, company research, trade interviews, Euromonitor International estimates

Appendix C

Map showing areas of coffee cultivation

r:Coffea canephora m:Coffea canephora and Coffea arabica a:Coffea arabica

Source: http://upload.wikimedia.org/wikipedia/commons/9/96/Carte_Coffea_robusta_arabic.png This map is from Wikimedia Commons. Commons is a freely licensed media file repository.

Since the nature of our business is a bit alien to most business people, we have created this basic guidance document as an attempt to help you wrap your brain around the selection of successful covert operations candidates.

A covert operative such as a Dark Lord or Mind Slayer requires some special personality traits and mental abilities. Although the methods exposed below will probably not sound very scientific, they are the result of recruiting successful candidates for over 25 years. Let’s get started.

Personal Identity
We have discovered over the years that there are two types of identity: professional and recreational. A quick look on a Twitter profile (or other social media profile) will usually tell you who you are dealing with.

If the profile starts, “Mother of 5, devoted wife and internet marketing guru”, then you have found a recreational identity. This person is all about who they are away from work or career.

If the profile starts, “Manager of operations for ___, avid people watcher, cynic …” then you have found a “professional” profile.

The easiest way to tell is this: recreational people talk about who they are to other people. Professional people talk about what they do. It really is that easy. We begin by assessing how the prospective recruit has created their identity. We seek professional identities. Recreational identities have proven to be a poor investment when it comes to covert operations. They tend to quit and pursue their other interests.

Looks and physical traits
Let me be clear here, 450 pound guys do not tend to make good operatives. Covert operations is all about thinking on your feet, paying minute attention to detail while not losing the big picture and having the ability to get people to feel at ease around you.

Although you do not have to be a super model, you should seek out people with a pleasant appearance that are in fair shape. They can be a bit overweight, but should not have thrown health away in their pursuit of junk food. Those people make better clerks and intelligence analysts – behind the scenes people.

The truth is, people are more comfortable around nice looking, pleasant people. Use this to your advantage and you will find huge success with your program.

Another rule when it comes to looks that we have discovered is this: the more athletic people tend to be more competitive, active in their careers and more aggressive. These are good traits for a covert operative.

Mental Skills
You don’t have to be a genius to be a good covert operative, but it helps. These people will need to memorize details quickly (we train that skill) and will need to multi-task their thinking. For instance, when they are persuading others, they need to be able to think about their persuasion activity and be able to listen/interact with the audience they are affecting.

The most tell tale trait here is the ability to carry on a clear and reasonably fast paced conversation. They should be able to keep up and contribute value to the conversation. They should seem “mentally sharp” or “bright”.

Personal Interests
This is a big one. The potential recruit should be interested in psychology, hypnosis, mind-control and/or spies. They should have a passion for being the best at what they do and crave abilities that are not for the “common man”. To put it bluntly, covert operatives tend to be a bit elitist. It is this specific trait that will prevent them from divulging confidential information even under torture.

Dark Lords and Mind Slayers are part of a very elite brotherhood and should take considerable pride in this. It is also a great way for you to control them because of your relationship as a sponsor, you have a direct line of communications with their brotherhood.

Lifestyle
This one is tricky. The candidate should want a rich lifestyle, but should not be out of control with it. It is easy for a Mind Slayer to pass their covert income off as an “Internet business” and we will show them how to set that up. We call it a “cover income” and it is a great excuse for them to live above their “pay level”. Basically, they work and support you, and they are paid as a regular employee. They get a second “compensating check” that is direct deposited for their covered operations. The compensating check is direct deposited into another account of theirs. Pay your spies well and you won’t regret it.

Commitment and Loyalty
Needless to say, you don’t want to train one of these people every six months. We suggest at least a three year contractual agreement if not longer. The right person will agree to a long term contract and will sincerely desire to deliver value to your organization.

Other matters
The recruit that you send for either Dark Lord or Mind Slayer training will be educated in corporate intelligence, personal combat and psychological manipulation skills as a minim course of study. They will be immersed in 45 days of training (six times for a Dark Lord) and will walk out of our programs ready to do their job. Dark Lords are trained incrementally so that they can implement the changes required in a natural progression. They have access to coaches throughout their program period and afterwards if they choose. Mind Slayers have access to coaches for 12 consecutive months or longer if they choose.

Additional coaching occurs through membership in one of our membership community programs.

Your Commitment
As a President or CEO, you must be committed to the program structure as outlined in “Dark Lords – The Power Within” if you desire the revenue and growth that a covert operation offers. As such, you will probably end up with several Mind Slayers critically positioned inside of your ranks and at least one Dark Lord serving you and your staff. This takes a commitment from you and enforcement of that commitment on your staff. Your Dark Lord will be able to help you strategize a process to get your staff under your power. After all, you will be the ruling crown royal of your organization soon.

You will be working closely with your Dark Lord and should be comfortable with them. They will be your dirty little secret and your shadow warrior when you need things to happen behind the scenes. They will interact with your Mind Slayers and keep all covers in place so that you and your people are protected.

For more information, please feel free to contact us.

Padma Gonpo, Inc.
2533 N. Carson Street, Suite 4045
Carson City, NV 89706
800-206-3302 ext 3
“Dark Lords – The Power Within” www.padmagonpoinc.com/darklord.html

SoftSelect erweitert seine Matching-Plattform um Business Intelligence und bereitet Studie vor

Eine größtmögliche Effizienz und Transparenz der Unternehmensabläufe zu erreichen steht nicht nur in wirtschaftlich schwierigen Zeiten im Mittelpunkt strategischer IT-Entscheidungen. Eine unübersichtliche Situation im Anbietermarkt und der erhöhte Bedarf an analytischen Softwarelösungen stellt viele IT-Leiter und Entscheidungsträger vor große Herausforderungen. Das unabhängige Hamburger IT-Beratungshaus und Marktforschungsinstitut SoftSelect GmbH nimmt sich dieser Anforderung an und erweitert seine Matching-Plattform um den Bereich Business Intelligence (BI). Darüber hinaus bereitet das Hamburger Expertenteam für 2010 eine BI-Marktstudie vor, um Unternehmen einen umfassenden Überblick zum derzeitigen Angebot und zu aktuellen Entwicklungen zu geben.

Der Bedarf an effektiven, integrierten Werkzeugen, um unternehmerische Prozesse sowohl ganzheitlich abzubilden als auch systematisch auszuwerten, wächst zusehends. Valide strategische Entscheidungen erfordern ein hohes Maß an Transparenz in Planung und Controlling sowie fundierte Auswertungen über sämtliche Geschäftsbereiche hinweg. Dem gegenüber steht ein ebenfalls stetig erweitertes Lösungsangebot der Anbieter, das Auswahl- und Einführungsprojekte zu einer großen Herausforderung werden lässt.

Um Unternehmen mehr Transparenz im Business-Softwaremarkt bieten zu können sowie IT-und Projektverantwortliche bei der Auswahl geeigneter Softwarelösungen zu unterstützen, stellt SoftSelect eine umfassende und kostenlose Online-Matching-Plattform zur Verfügung. Neben den Bereichen ERP, Human Resources, CRM, Finanzbuchhaltung und Kostenrechnung, Anlagenbuchhaltung, Outsourcing, Materialwirtschaft, PPS und Service-Management wird das Leistungsspektrum zum Jahr 2010 um den Bereich Business Intelligence erweitert.

Unternehmen können mit Hilfe der kostenlosen Online-Plattform aus mehr als 800 Softwarelösungen recherchieren und anhand eines persönlichen Kriterienprofils mit den eigenen Anforderungen abgleichen.

In Ergänzung bereitet SoftSelect für 2010 eine umfassende Marktübersicht mit aktuellen Trends und Entwicklungen im Bereich der Business Intelligence Software vor. Interessierte ERP- und BI- Lösungsanbieter können sich unter marketing@softselect.de für eine Teilnahme vormerken lassen. Anhand eines umfangreichen Kriterienkatalogs u.a. zu den Themenbereichen Data Warehouse, Data Mining, ETL, OLAP-Analyse, Data Mining, Reporting, Berechtigungskonzept, Administration und Architektur werden die Lösungen im Hinblick auf Funktionen, Schwerpunkte und Zielgruppe analysiert. Weitere regelmäßig aktualisierte SoftTrend Studien auf Basis von Anbieter- und Anwenderbefragungen sind unter anderem zu den Themen ERP, CRM und Human Resources Software erhältlich.

SoftSelect bietet Unterstützung bei der Softwareauswahl von A bis Z
Individuelle Beratung bei der Softwareauswahl beinhaltet große Kompetenz mit Hilfe von Checklisten und Muster-Anforderungskatalogen, die an die spezifischen Anforderungen der Kunden angepasst werden. Kunden werden in Projekten von der Anforderungsanalyse, der Erstellung des Lastenheftes, der Präsentationsbegleitung und Moderation bis hin zur Vertragsprüfung oder auch in bestimmten Teilbereichen begleitet und unterstützt – hierbei ist Herstellerneutralität und strukturiertes, zielorientiertes Vorgehen bei geringem Aufwand langjährige Praxis des Beratungshauses.

Weitere Informationen: www.softselect.de

Wie Fabian in seinem Blog in seinem neuesten Artikel “SharePoint Magazin goes 2010” schon angekündigt hat wird das SharePoint Magazin nach dem Erfolg der Erstausgabe (in der ich zwei Artikel beigesteuert hatte) fortgesetzt.

Auch ich werde mich wieder mit zwei Artikeln zum Thema Self Service BI und Taxonomies / Findability beteiligen.

Gerade im Hinblick auf den offiziellen Release von SharePoint 2010 im Frühjahr denke ich das ein Print Magazin eine gute Möglichkeit ist, einer breiten Masse die neuen Features vorzustellen.

Nuestro moderno paradigma de gestión de información es completamente diferente al anterior. La información se ha vuelto masiva, su velocidad de creción y circulación es tremendamente rápida, y el catálogo de sus formatos es muy extenso. La información puede ser estructurada o no estructurada (en logs GPS, blogs, vídeos, podcasts o tweets). Proviniendo del interior o del exterior de la empresa, la información llega cada día, cada hora o en tiempo real. Las fuentes incluyen internet, procesos batch u objetos equipados con sensores. Esta combinación de volumen, velocidad y diversidad hace que utilizar bien la información (o simplemente usarla) se haya convertido en una tarea intimidatoria.

Una granularidad creciente hace que la información sea incuso más difícil de aprehender. Por ejemplo, las personas que pueden indentificarse ahora por geoposicionamiento y genotipo. En el mundo de los objetos inteligentes, no sólo se identifican por tarjetas de trazabilidad los contenedores y los pallets, sino que productos como fármacos, carne de pollo, fruta o vino añaden niveles mayores de detalle al ecosistema de la información mediante los modernos sistemas de lectura de etiquetado por radiofrecuencia.

La tecnología y los medios para establecer conexiones inteligentes están disponibles por doquier, y a un coste relativamente bajo. Pero, para transformar esta información en acciones inteligentes, las empresas necesitan una potencia tremenda de análisis. Este nivel de análisis ya está al alcance de las empresas, sin embargo, es necesario un fuerte cambio para que los líderes de negocios tomen decisiones estratégicas y gestionen sus organizaciones basándose en este análisis. Las compañías potenciarán a sus empleados, especialmente a aquellos que se encuentran en los puntos de contacto con los clientes y los proveedores, para que tomen mejores decisiones.

Crecimiento de la información

La velocidad con la que crece el volumen, variedad y granularidad de la información está produciendo una nueva complejidad sin precedentes:

- En 2010 la cantidad de información digital crecerá a 988 exabytes (equivalentes a una pila de libros desde el Sol a Plutón ida y vuelta)

- El 80% del crecimiento de los nuevos datos es contenido no estructurado.

- Cada día se generan 15 petabytes de información nueva. Su volumen es más de ocho veces toda la información de todas las bibliotecas de Estados Unidos.

- Hoy en día, la capacidad de proceso de la web es aproximadamente equivalente a la de un cerebro humano. En 2040, superará la capacidad de proceso total de toda la humanidad.

Fuente: CEDE

Because Microsoft SQL Server Analysis Services query and processing performance tuning is a fairly broad subject, this white paper organizes performance tuning techniques into the following three segments.

Enhancing Query Performance – Query performance directly impacts the quality of the end user experience. As such, it is the primary benchmark used to evaluate the success of an online analytical processing (OLAP) implementation. Analysis Services provides a variety of mechanisms to accelerate query performance, including aggregations, caching, and indexed data retrieval. In addition, you can improve query performance by optimizing the design of your dimension attributes, cubes, and Multidimensional Expressions (MDX) queries.

Enhancing Processing Performance – Processing is the operation that refreshes data in an Analysis Services database. The faster the processing performance, the sooner users can access refreshed data. Analysis Services provides a variety of mechanisms that you can use to influence processing performance, including efficient dimension design, effective aggregations, partitions, and an economical processing strategy (for example, incremental vs. full refresh vs. proactive caching).

Tuning Server Resources – There are several engine settings that can be tuned that affect both querying and processing performance.

Download SQL Server 2008 Analysis Services Performance Guide

Reference: Pinal Dave (http://blog.sqlauthority.com)

La Business Intelligence o inteligencia empresarial, inteligencia de negocios o simplemente BI es el conjunto de estrategias y herramientas enfocadas a la administración y creación de conocimiento mediante el análisis de datos existentes en una empresa.

Las empresas líderes van más allá de la registración y almacenamiento de datos en grandes cantidades. Lo que están haciendo es crear estrategias competitivas basadas en la investigación de sus propios datos de manera metodológica e innovadora, obteniendo resultados impactantes, y ésto se realiza por medio de la Business Intelligence.

En términos más globales, la inteligencia organizacional es el proceso de análisis de datos de la organización para poder extraer conocimiento de ellos. Con BI se puede: crear una base de datos de clientes, prever ventas y devoluciones, compartir información entre diferentes departamentos, mejorar el servicio al cliente, entre otras muchas posibles situaciones.

Los sistemas de la inteligencia de negocio se basan en crear modelos informáticos de negocio de modo que pueda funcionar más eficientemente.

El almacenamiento de los datos está en la base de los procesos de la inteligencia de negocio. Mediante las herramientas y técnicas ELT (extraer, cargar y transformar), o actualmente ETL (extraer, transformar y cargar) se extraen los datos de distintas fuentes, se depuran y preparan (homogeneización de los datos) para luego cargarlos en un almacén de datos.

En el mundo de ETL, la inteligencia de negocio se refiere generalmente al espacio entero de los sistemas de la base de datos, del software, del análisis, y de la evaluación del usuario que pretende entender y evaluar un negocio.

La BI alienta la multidimensionalidad, es decir la organización y visualización
de la información que se encuentra en distintas bases de datos. Con los aplicativos que integran la BI, ya sean utilizados de manera conjunta o individual, se puede reunir información dispersa en toda la empresa y en diferentes fuentes e integrarla para proveer a los departamentos de la accesibilidad, poder, flexibilidad y automatización que necesiten para analizar información.

La Business Intelligence permite llevar a cabo un verdadero Data Mining para recabar información sobre producción, mercados y clientes, pero también permite desarrollar la visión para intuir cambios o nuevas tendencias que constituyen la base del éxito del negocio. Las aplicaciones de data mining identifican tendencias y comportamientos para extraer información y descubrir las relaciones en bases de datos que revelen comportamientos poco evidentes.
La Business Intelligence acerca, gracias a su mecanismo de automatización total, la posibilidad de realizar tareas de todo tipo sin que intervenga el ser humano.

Las herramientas de inteligencia analítica posibilitan el modelado de las representaciones en base a consultas para crear un cuadro de mando integral que sirve de base para la presentación de informes

Una aplicación Business Intelligence suele incluir:

ETL: para integrar datos extraídos de diferentes fuentes y transformarlos de acuerdo al criterio analítico que permita su análisis.

Analytics: para hacer exploración de la información, relacionarla, establecer condiciones, filtros y relaciones que otorguen claridad a los mensajes de los números.

Dashboard: para visualizar datos y análisis, integrando a una amplia comunidad de usuarios, con reportes en tiempo real.

Balanced Scorecard: para diseñar, ejecutar y controlar estrategias efectivas alineadas a lo largo de toda la organización.

Utilizados en conjunto o en forma individual estos aplicativos permiten basar su competitividad en tres aspectos básicos:

1. Eficiencia y productividad en la ejecución operativa.
2. Decisiones más efectivas basadas en información analítica.
3. Extracción hasta la última gota de valor de los procesos.

La Inteligencia de Negocios permite la construcción de modelos de negocios analíticos y predictivos con énfasis en la exploración de datos, su transformación en series relacionales, la estadística y la visualización en formatos didácticos de inmediata interpretación.

An excellent study was issued by Intralinks and mergermarket a few weeks ago.  (You can find it here.) In August and September, they conducted a survey of experts in M&A – the people within each company responsible for deals.  The key finding:  almost two-thirds of those surveyed thought that the overall economic environment in their own regions would improve in 2010 (which is in distinct contrast to the economist, politician and journalist Cassandras who feel that we’re in for a double dip or even worse in 2010 and beyond).  Interestingly, 16% felt that this recovery was already underway and 8% that it would happen before year-end.  And in Europe, over 40% of respondents felt that corporate M&A would increase in the next year (and only 12% were pessimistic).

This is a significant finding, because the M&A market is driven at the corporate board and senior exec level by optimism (I would almost prefer to say ‘hubris’) and general business confidence.  The perception of an improving market is critical to the market’s turnaround prospects.

But caution amongst those surveyed is also noted in the study:  when asked about their own company’s activity, only about a third were optimistic or very optimistic.  (Yet only 20% were pessimistic and most (44%) were neutral.)  This is a valid cause for concern.  This would indicate that firms are reticent to commit their own money toward committed rapid expansion – and an M&A deal IS the way for a major existing firm to expand rapidly, even if most deals fail in the execution – despite those firms seeing the general business environment improving.

It was nice to see that 75% agree with me that the M&A market has already reached its inflexion point (see ‘M&A inflexion point: The turn to ‘up’ in activity’ which we published back in late September).

We’ve noted before on this site back in the summer (see ‘Distressed and bankrupt acquisitions: Should you do one of these deals?‘) that distressed acquisitions would be a major driver for some time to come.  This Intralinks / mergermarket survey confirmed that, as almost half of all respondents in Europe felt that distressed deals were the principal driver to the market for the next year (followed by 31% looking at market consolidation as the principal driver).  The overhang from the recession will still take a while to work through the M&A system, which is a wonderful mechanism to assist industry in self-correcting.

What are firms most expecting to do?  When respondents looked at what they would do in their own companies, they still feel that their M&A activity will be driven by finding undervalued targets (over 50% thought this).  If this is true, the growth in M&A activity might be faster, as a continuing stock market rise would make fewer such undervalued targets around.  Nicely, two-thirds of the companies DID expect to make an acquisition in the next 12 months.  Given the lead time to many of these strategic deals, the planning and even negotiations may be underway already.  This is support for the strong backlog of deals that many advisors note.

Overall corporate organisational restructurings were expected by 73% of respondents, and by a whopping 81% of those in Europe.  Europe really does appear to be the region that is pulling out of the recession with the greatest difficulties remaining.

I found most interesting the thoughts about which industries would see more consolidation, at least according to this survey.  There’s so much conjecture about where the next big deals will come, and each observer seems to have different feelings.  It’s nice to see a survey taking in the opinion of many experts, as this Intralinks / mergermarket survey is.  Contrary to my own personal view, financial services was seen in Asia/Pacific and North America as being the most likely industry to see deal activity.  In Europe, the consumer area was suggested to be the most active.  Notably, pharmaceuticals, industrials & chemicals, the extractive industries and the technology industries that saw huge deals in 2009 were not in the top list.

One of the best ways of getting a better insight about SAP solutions is to follow an official SAP training. If you search in the web there are many SAP Training providers available today. You may note that some of them provide SAP Training Online courses for your convenience.

As you already know, SAP is providing many different solutions for medium and large enterprises. Before you engage in SAP Training, it is recommended to spend some time to get familiar with different solutions provided by SAP. Some solutions are greater in demand while some others are not. So finding the area you want to get trained is not that straightforward. Start small and eventually you will get there. Best is to choose one of the component in SAP Business suite such as Customer Relationship Management, Human Capital Management, etc. Then gradually move into the other components as you desire.

Once you have the base, you can move into the advance topics such as SAP NetWeaver Business Intelligence. SAP AG describes SAP BI as “unites a powerful business intelligence platform, a comprehensive set of tools, planning and simulation capabilities, and data-warehousing functionality – delivered through sophisticated and user-centric enterprise portal technology to provide a coherent, comprehensive solution”.

SAP Business Intelligence is one of the high demand areas these days. SAP BI Training courses are now offered by many different certified partners. Knowledge of basic data warehousing, business administration and cross-functional processes are important prerequisites of the SAP BI Training. If you come to this level by gradually following the functional processes of SAP ERP you will grasp SAP BI quickly.

Getting trained in SAP is fun as long as you have the desire and motivation. If you are grasping the SAP ecosystem gradually you will find how easy it is to master in SAP. If you are from a software technical background, you may find easy in grasping certain advance concepts like SAP BI. Even you are not, you still can be comfortable with a properly structured course. Make sure to cover the prerequisites before attending to any of the SAP training course to harness their best value.

As you might already know, SAP is one of the world’s largest ERP solutions provider specially caters for medium to large enterprises. SAP ERP caters many different functional areas which are essential parts of any medium to large organization. These include Financials, Logistics, Human Resource Management, Asset Management, etc. An individual who has thorough understanding on theses SAP ERP components possesses many skills. To name a few, one of the key skills is the domain knowledge of these different areas which are paramount in any organization. Since SAP ERP consolidates the industry best practices to their product implementation, an individual can gain the knowledge and experience about the industry best practices of handling these domain processes.

Best way to get this knowledge and experience though a proper, well structured SAP Training. You may choose SAP Training Online over attending classes due to its convenient. Since you need to make an investment in pursuing SAP Training, one of the key points to consider is the training provider. If you search over the web you can find many providers offering SAP training, but make sure to verify whether the provider is actually a SAP certified partner in providing official SAP courses.  You might have to pay some extra bucks, but the training and the recognition is guaranteed. Also it will put a solid base for you to pursue the SAP certification. You may also want to finalize in which area you want to get the SAP Training. You may consider one of the SAP EPR functional areas as mentioned above or some other specific advance areas like SAP BI Training (Business Intelligence).

Once you successfully complete SAP training, next step would be to focus on SAP certification. Don’t wait too long to complete the certification after the SAP training is over. Have a plan and go for it as soon as you feel that you are ready. SAP Certification will help you to prove your SAP knowledge and also help others to recognize you as a true SAP genius. Your CV will be well recognized by potential employers when they see the SAP certification in your CV.

•KMS: nuevas tecnologicas para la gestión del conocimiento y su integración en una única plataforma. Son herramienta no relacionadas directamente con el Business Intelligence, pero nos permiten gestionar toda la información y el conocimiento que hay en la empresa para poder sacar un mayor partido de ella. Por ejemplo, toda la documentación interna, manuales, procedimientos que existe en la empresa esta guardada en un gestor documental que una vez indexado, se puede consultar por los integrantes de la organización de una forma ágil. Al fin y al cabo, la información es poder y cuanto mas rapida se pueda obtener, mas beneficios genera.

Introducción

Definiciones obtenidas del Consejo Superior de Informatica, del documento “Adquisición de un sistema de Datawarehouse” ( http://www.csi.map.es/csi/silice/Datwar.html )

El Data Mining es un proceso que, a través del descubrimiento y cuantificacion de relaciones predictivas en los datos, permite transformar la información disponible en conocimiento útil de negocio.Esto es debido a que no es suficiente “navegar” por los datos pararesolver los problemas de negocio, sino que se hace necesario seguir una metodología ordenada que permita obtener rendimientos tangibles de este conjunto de herramientas y técnicas de las que dispone el usuario.

Constituye por tanto una de las vías clave de explotación del Data Warehouse, dado que es este su entorno natural de trabajo.

Se trata de un concepto de explotación de naturaleza radicalmente distinta a la de los sistemas de información de gestión, dado que no se basa en coeficientes de gestión o en información altamente agregada, sino en la información de detalle contenida en el almacén.

Adicionalmente, el usuario no se conforma con la mera visualización de datos, sino que trata de obtener una relación entre los mismos que tenga repercusiones en su negocio.

Técnicas de Data Mining

Para soportar el proceso de Data Mining, el usuario dispone de una extensa gama de técnicas que le pueden ayudar en cada una de las fases de dicho proceso, las cuales pasamos a describir:

Análisis estadístico:

Utilizando las siguientes herramientas:
1.ANOVA: o Análisis de la Varianza, contrasta si existen diferencias significativas entre las medidas de una o más variables continuas en grupo de población distintos.

2.Regresión: define la relación entre una o más variables y un conjunto de variables predictoras de las primeras.

3.Ji cuadrado: contrasta la hipótesis de independencia entre variables. Componentes principales: permite reducir el número de variables observadas a un menor número de variables artificiales, conservando la mayor parte de la información sobre la varianza de las variables.

4.Análisis cluster: permite clasificar una población en un número determinado de grupos, en base a semejanzas y desemejanzas de perfiles existentes entre los diferentes componentes de dicha población.

5.Análisis discriminante: método de clasificación de individuos en grupos que previamente se han establecido, y que permite encontrar la regla de clasificación de los elementos de estos grupos, y por tanto identificar cuáles son las variables que mejor definan la pertenencia al grupo.

Métodos basados en árboles de decisión:

El método Chaid (Chi Squared Automatic Interaction Detector) es un análisis que genera un árbol de decisión para predecir el comportamiento de una variable, a partir de una o más variables predictoras, de forma que los conjuntos de una misma rama y un mismo nivel son disjuntos. Es útil en aquellas situaciones en las que el objetivo es dividir una población en distintos segmentos basándose en algún criterio de decisión.

El árbol de decisión se construye partiendo el conjunto de datos en dos o más subconjuntos de observaciones a partir de los valores que toman las variables predictoras. Cada uno de estos subconjuntos vuelve después a ser particionado utilizando el mismo algoritmo. Este proceso continúa hasta que no se encuentran diferencias significativas en la influencia de las variables de predicción de uno de estos grupos hacia el valor de la variable de respuesta.

La raíz del árbol es el conjunto de datos íntegro, los subconjuntos y los subsubconjuntos conforman las ramas del árbol. Un conjunto en el que se hace una partición se llama nodo.

El número de subconjuntos en una partición puede ir de dos hasta el número de valores distintos que puede tomar la variable usada para hacer la separación. La variable de predicción usada para crear una partición es aquella más significativamente relacionada con la variable de respuesta de acuerdo con test de independencia de la Chi cuadrado sobre una tabla de contingencia.

Algoritmos genéticos:

Son métodos numéricos de optimización, en los que aquella variable o variables que se pretenden optimizar junto con las variables de estudio constituyen un segmento de información. Aquellas configuraciones de las variables de análisis que obtengan mejores valores para la variable de respuesta, corresponderán a segmentos con mayor capacidad reproductiva. A través de la reproducción, los mejores segmentos perduran y su proporción crece de generación en generación. Se puede además introducir elementos aleatorios para la modificación de las variables (mutaciones). Al cabo de cierto número de iteraciones, la población estará constituida por buenas soluciones al problema de optimización.

Redes neuronales:

Genéricamente son métodos de proceso numérico en paralelo, en el que las variables interactúan mediante transformaciones lineales o no lineales, hasta obtener unas salidas. Estas salidas se contrastan con los que tenían que haber salido, basándose en unos datos de prueba, dando lugar a un proceso de retroalimentación mediante el cual la red se reconfigura, hasta obtener un modelo adecuado.

Lógica difusa:

Es una generalización del concepto de estadística. La estadística clásica se basa en la teoría de probabilidades, a su vez ésta en la técnica conjuntista, en la que la relación de pertenencia a un conjunto es dicotómica (el 2 es par o no lo es). Si establecemos la noción de conjunto borroso como aquel en el que la pertenencia tiene una cierta graduación (¿un día a 20ºC es caluroso?), dispondremos de una estadística más amplia y con resultados  más cercanos al modo de razonamiento humano.

Series temporales:

Es el conocimiento de una variable a través del tiempo para, a partir de ese conocimiento, y bajo el supuesto de que no van a producirse cambios estructurales, poder realizar predicciones. Suelen basarse en un estudio de la serie en ciclos, tendencias y estacionalidades, que se diferencian por el ámbito de tiempo abarcado, para por composición obtener la serie original. Se pueden aplicar enfoques híbridos con los métodos anteriores, en los que la serie se puede explicar no sólo en función del tiempo sino como combinación de otras variables de entorno más estables y, por lo tanto, más fácilmente predecibles.

Metodología de aplicación:

Para utilizar estas técnicas de forma eficiente y ordenada es preciso aplicar una metodología estructurada, al proceso de Data Mining. A este respecto proponemos la siguiente metodología, siempre adaptable a la situación de negocio particular a la que se aplique:

Muestreo

Extracción de la población muestral sobre la que se va a aplicar el análisis. En ocasiones se trata de una muestra aleatoria, pero puede ser también un subconjunto de datos del Data Warehouse que cumplan unas condiciones determinadas. El objeto de trabajar con una muestra de la población en lugar de toda ella, es la simplificación del estudio y la disminución de la carga de proceso. La muestra más óptima será aquella que teniendo un error asumible contenga el número mínimo de observaciones.

En el caso de que se recurra a un muestreo aleatorio, se debería tener la opción de elegir El nivel de confianza de la muestra (usualmente • el 95% o el 99%).

El tamaño máximo de la muestra (número máximo de registros), en cuyo caso el sistema deberá informar del el error cometido y la representatividad de la muestra sobre la población original.

•El error muestral que está dispuesto a cometer, en cuyo caso el sistema informará del número de observaciones que debe contener la muestra y su representatividad sobre la población original.

•Para facilitar este paso s debe disponer de herramientas de extracción dinámica de información con o sin muestreo (simple o estratificado). En el caso del muestreo, dichas herramientas deben tener la opción de, dado un nivel de confianza, fijar el tamaño de la muestra y obtener el error o bien fijar el error y obtener el tamaño mínimo de la muestra que nos proporcione este grado de error.

Exploración

Una vez determinada la población que sirve para la obtención del modelo se deberá determinar cuales son las variables explicativas que van a servir como “inputs” al modelo. Para ello es importante hacer una exploración por la información disponible de la población que nos permita eliminar variables que no influyen y agrupar aquellas que repercuten en la misma dirección.

El objetivo es simplificar en lo posible el problema con el fin de optimizar la eficiencia del modelo. En este paso se pueden emplear herramientas que nos permitan visualizar de forma gráfica la información utilizando las variables explicativas como dimensiones.

También se pueden emplear técnicas estadísticas que nos ayuden a poner de manifiesto relaciones entre variables. A este respecto resultará ideal una herramienta que permita la visualización y el análisis estadístico integrados.

Manipulación

Tratamiento realizado sobre los datos de forma previa a la modelización, en base a la exploración realizada, de forma que se definan claramente los inputs del modelo a realizar (selección de variables explicativas, agrupación de variables similares, etc.).

Modelización

Permite establecer una relación entre las variables explicativas y las variables objeto del estudio, que posibilitan inferir el valor de las mismas con un nivel de confianza determinado.

Valoración

Análisis de la bondad del modelo contrastando con otros métodos estadísticos o con nuevas poblaciones muestrales.

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En el artículo de Luis Carlos Molina, coordinador del programa de Data Mining de la UOC, también se dan definiciones interesantes desde otro punto de vista (..”torturando a los datos hasta que confiesen” ).

http://www.uoc.edu/web/esp/art/uoc/molina1102/molina1102.html

En este artículo se da una vision global de todo lo relacionado con DataMining, así como ejemplos prácticos de su utilización.

___________________________________________________________________________________________________

Mas información, igualmente, en la monografía elaborada por Cynthia Presser Carne

http://www.monografias.com/trabajos/datamining/datamining.shtml

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Algunas herramientas de DataMining serían las siguientes (obtenidas de las páginas http://www.datati.es/herramientas-de-datamining/ y http://www.dataprix.com/herramientas-de-data-mining):

· Aqua BAS 2008 / Aqua eSolutions

· IBM / SPSS: Herramienta de data mining que permite desarrollar modelos predictivos y desplegarlos para mejorar la toma de decisiones. Está diseñada teniendo en cuenta a los usuarios empresariales, de manera que no es preciso ser un experto en data mining.

· dVelox 2.5/ Apara: Plataforma analítica para la toma de decisiones en tiempo real que predice los escenarios futuros más probables para optimizar los procesos críticos de cualquier empresa, Está orientada a los sectores de banca, finanzas y márketing.

· IBM / Cognos.

· Microsoft SQL Server 2008 Datamining: Solución que ofrece un entorno integrado para crear modelos de minería de datos (Data Mining) y trabajar con ellos. La solución SQL Server Data Mining permite el acceso a la información necesaria para tomar decisiones inteligentes sobre problemas empresariales complejos. Data Mining es la tecnología de BI que ayuda a construir modelos analíticos complejos e integrar esos modelos con sus operaciones comerciales.

· MicroStrategy Data Mining Services /Microstrategy: Componente de la plataforma de BI de MicroStrategy que proporciona a los usuarios, modelos predictivos de data mining. Permite realizar tareas de data mining mediante el uso de métricas construidas con funciones predictivas o importadas de modelos de datos de herramientas de data mining de terceros.

· SAS Enterprise Miner / SAS: Solución de minería de datos que proporciona gran cantidad de modelos y de alternativas. Permite determinar pautas y tendencias, explica resultados conocidos e identifica factores que permiten asegurar efectos deseados. Además, compara los resultados de las distintas técnicas de modelización, tanto en términos estadísticos como de negocio, dentro de un marco sencillo y fácil de interpretar.

· Oracle DataMining: Función de Oracle 11g Enterprise Edition que permite diseñar aplicaciones de BI que más tarde realizan funciones de «minería» en las bases de datos corporativas para descubrir nueva información e integrarla con las aplicaciones de negocio.

· MIS Delta Miner / MIS: Herramienta concebida para efectuar análisis de alto nivel, detección de desviaciones y análisis interactivo sobre múltiples fuentes, tanto transaccionales (SQL Server, Oracle, DB2, etc) como multidimensionales (Hyperion Essbase, SAP BW, MS Analysis Services y Oracle Express).

· Teradata Warehouse Miner / Teradata: Solución que facilita la construcción de modelos analíticos directamente sobre la base de datos, eliminando así la necesidad de extraer muestras a sistemas del exterior. Además permite analizar datos sin cambiarlos de sitio y es capaz de visualizar la información para representar regresiones lineales o logísticas.

Dentro de las herramientas OpenSource:

· Weka: Solución de minería de datos OpenSource desarrollada en la Universidad de Waikato, que consiste en una collecion de algoritmos implementados en Java para realizar minería de datos. En la actualidad esta asociado al proyecto pentaho.

Una lista mas completa de herramientas datamining la encontramos en la comunidad KDNUGGETS.COM (Data Mining Community’s ) en el siguiente link:

http://www.kdnuggets.com/software/suites.html

•EIS (Executive information system): Soluciones que permiten visualizar, de una forma rápida y fácil, el estado de una determinada situación empresarial, presente o pasada, y que permite detectar anomalías o oportunidades. Aplicaciones de alto nivel que pretenden, mediante el acceso a las diferentes bases de datos de una empresa, ofrecer a sus directivos los elementos clave para que puedan tomar decisiones sobre la marcha de sus negocios. Generalmente el directivo accede a pantallas gráficas o cuadros de mando en las que se resumen los elementos más importantes que debe tener en cuenta para la toma de decisiones. Podrían contestar a la pregunta: ¿que necesito conocer ahora?.

Cubos OLAP (On-Line Analytic Processing): Son las herramientas que se basan en la capacidad de analizar y explorar por los datos. Nos permiten cambiar el enfoque del “¿que esta pasando?” que podemos obtener a través de las herramientas de reporting al “¿por que esta pasando?”.

Para descubrir el “por que”, los usuarios pueden navegar y profundizar en los datos para analizar los detalles o patrones.Las herramientas OLAP nos proporcionan analisis interactivo por las diferentes dimensiones de los datos (por ejemplo, tiempo, producto, cliente, criterios geográficos, etc) y por los diferentes niveles de detalle (para la dimensión tiempo, habrá nivel de detalle año, trimestre, mes, dia).

Esto significaría pasar de la visión estática de los datos a una visión dinámica, donde podemos ir “navegando” por los datos, bajando en el nivel de detalle, cambiando la dimensión por la cual analizamos la información. El tipico ejemplo sería una tabla con los datos de ventas y margenes por delegación de una empresa, y cuando observamos un indicador de rentabilidad negativa, buceamos en los datos de esta delegación hasta dar con el producto que se esta vendiendo a precios de coste con margen negativo. Este sería el tipico ejemplo de los Cubos Olap y los visores multidimensionales que nos permiten “profundizar en los datos”. Nos permitirían contestar a la pregunta: ¿que sucedio y por que?.

Para entender que se analizamos mediante los cubos Olap, hemos de saber que la información de gestión se compone de conceptos de información (dimensiones) y coeficientes de gestión (indicadores), que los cuadros directivos de la empresa pueden consultar según las dimensiones de negocio que se definan.Dichas dimensiones de negocio se estructuran a su vez en distintos niveles de detalle (por ejemplo, la dimensión geográfica puede constar de los niveles nacional, provincial, ayuntamientos y sección censal).

Este tipo de sistemas ha existido desde hace tiempo, en el mundo de la informática bajo distintas denominaciones: cuadros de mando, MIS, EIS, etc.

En general, los sistemas OLAP deben:

• Soportar requerimientos complejos de análisis
• Analizar datos desde diferentes perspectivas
• Soportar análisis complejos contra un volumen ingente de datos

La funcionalidad de los sistemas OLAP se caracteriza por ser un análisis multidimensional de datos corporativos, que soportan los análisis del usuario y unas posibilidades de navegación, seleccionando la información a obtener.Normalmente este tipo de selecciones se ve reflejada en la visualización de la estructura multidimensional, en unos campos de selección que nos permitan elegir el nivel de agregación (jerarquía) de la dimensión, y/o la elección de un dato en concreto, la visualización de los atributos del sujeto, frente a una(s) dimensiones en modo tabla, pudiendo con ello realizar, entre otras las siguientes acciones:

Rotar (Swap): alterar las filas por columnas (permutar dos dimensiones de análisis)

Bajar (Down): bajar el nivel de visualización en las filas a una jerarquía inferior

Detallar (Drilldown): informar para una fila en concreto, de datos a un nivel inferior

Expandir (Expand): id. anterior sin perder la información a nivel superior para éste y el resto de los valores

Colapsar (Collapse):operación inversa de la anterior.

Para ampliar el glosario sobre exploraciones en análisis OLAP, recomendamos la visita a la página Internet:

http://www.dssresources.com/glossary/olaptrms.html

Tal y como la tecnología y los usuarios han evolucionado y madurado, las distinciones entre OLAP y las herramientas de reporting se han vuelto considerablemente confusas. Podemos tener informes bien formateados o sumarizados basados en datos multidimensionales y el usuario querrá enseguida navegar y bucear en los datos para ver el problema con una metrica en particular. En este caso, el usuario no querra ser forzado a tener que pasar a una herramienta separada para analizar y explorar. Las siguientes características continuan distinguiendo las herramientas OLAP de las herramientas de query y reporting tools:

■ Multidimensional Users analyze numerical values from different dimensions such as product, time, and geography. A report, on the other hand, may be one-dimensional, such as list of product prices at one point in time.
■ Consistently fast As users navigate different dimensions and levels within a dimension, OLAP means fast—the speed of thought. If a user double-clicks to drill-down from Year to Quarter, waiting 24 hours,
24 minutes, or even 24 seconds for an answer is not acceptable. Report users, of course, do not want slow reports either, but some reports take this long to run and must be scheduled.
■ Highly interactive Drilling is one way users interact with OLAP data. Pivoting gives users the ability to view information from different perspectives such as by geography or by product. Slicing allows users to filter the data within these dimensions such as sales for New York only and then for New Jersey only, or crime statistics for Leeds only and then Manchester only. This kind of interactivity within a non-OLAP report ranges from nonexistent to only recently possible.
■ Varying levels of aggregation To ensure predictable query times, OLAP products pre-aggregate data in different ways. Reporting, to the contrary, can be at the lowest level of detail: rather than sales by product, you might have individual line items for a particular order number.
■ Cross-dimensional calculations With multiple dimensions come more complex calculations. In OLAP, you might want to analyze percentage contribution or market share. These analyses require subtotaling
sales for a particular state and then calculating percentage contribution for the total region, country, or world. Users may analyze this percentage market share by a number of other dimensions, such as actual versus budget, this year versus last year, or for a particular group of products. These calculations often must be performed in a particular order and involve input numbers that users might never see. Detailed reports, however, often rely on simple subtotals or calculations of values that are displayed on the report itself.

Para distinguir los requerimientos OLAP, es importante distinguir entre las plataformas OLAP y los interfases de usuario OLAP.
OLAP Platforms
La plataforma OLAP The OLAP platform is about how the data is stored to allow for multidimensional analysis. The cube shown in Figure 2-3, in Chapter 2, represents the OLAP database. On the one hand, business users should not have to care at all about how the data is stored, replicated, and cached, and yet the OLAP architecture greatly affects what you can analyze and how. The OLAP architecture also influences what OLAP front-end you can use.
There are four primary OLAP architectures as described in Table 3-3. Relational OLAP (ROLAP) platforms store data in a relational database so data is not necessarily replicated into a separate storage for analysis.
Multidimensional OLAP (MOLAP) platforms replicate data into a purpose-built storage that ensures fast analysis. Hybrid OLAP (HOLAP) uses a combination of storage techniques. Dynamic OLAP (DOLAP) will automatically generate a small multidimensional cache when users run a query.
With each OLAP architecture, there are trade-offs in performance, types of multidimensional calculations, amount of data that can be analyzed, timeliness of data updates, and interfaces through which the data can be accessed.
Architecture
Primary Difference
Vendor
ROLAP
Calculations done in a relational database, large data volumes, less predictable drill times.
Oracle’s BI EE, SAP Netweaver BI, MicroStrategy, Cognos 8,
BusinessObjects Web Intelligence
MOLAP
Calculations performed in a server-based multidimensional database. Cubes provide write access for inputting budget data or performing what-if analysis.
Oracle’s Hyperion Essbase, Microsoft Analysis Services, TM1, SAS OLAP, Cognos PowerCubes
HOLAP
Aggregations in a cache but with seamless drill-through to relational.
Microsoft Analysis Services, SAS OLAP, Oracle’s Hyperion Essbase
DOLAP
Mini cache is built at query
run time.
BusinessObjects Web Intelligence, Oracle’s Hyperion Interactive Reporting
(formerly Brio)

Historically, many OLAP products used a MOLAP storage, which led to inflexible cube databases, management of more replicated data, and limitations on the data volumes and level of detail that can be analyzed.
All of this has sometimes scared IT away from OLAP.
With OLAP, a report is just a starting view, say, Sales for 2007 by Country—a summarized starting point. As users click, drill, and pivot, the end result might be Sales, Unit Price, Volume for one quarter, for two products, in a particular city—a detailed, focused end-point. In a strictly relational reporting world, the starting view and end result would be two entirely separate reports, with dozens of iterations in between.

OLAP Viewers
Microsoft Excel is one of the most popular interfaces to OLAP data. In fact, for three of the leading OLAP products (Oracle’s Hyperion Essbase, Microsoft Analysis Services, SAP Business Explorer), the spreadsheet was initially the only interface. Users would open a spreadsheet and could immediately begin drilling within cells and Excel Pivot Tables to retrieve and explorer their data.
Today, Excel continues to be an important OLAP interface, but in addition, users can explore data via OLAP viewers. These OLAP viewers may be web-based (whereas Excel is desktop-based) and will have advanced charting and navigation capabilities. In addition, business query tools and production reporting tools may also be able to
What Are Multidimensional Expressions (MDX)?
MDX is a query language similar to SQL but used to manipulate data within an OLAP database. Microsoft created MDX as a language
to work with its original OLAP server, now referred to as SQL Server Analysis Services. As MDX gained industry acceptance, a number of other OLAP databases added support for MDX such that today OLAP viewers will generate MDX to access and analyze data in a number of different OLAP databases.
I would argue that every BI deployment needs an OLAP component; not only is it necessary to facilitate analysis, but also it can significantly reduce the number of reports either IT developers or business users have to create.
access OLAP data sources and allow users to drill around with a report.
Figure 3-2 shows an example of a decomposition tree via Microsoft ProClarity, a relatively unique way of visually navigating through hierarchical
information.
Just as business query and reporting tools allow users to retrieve data from relational databases without knowing SQL, OLAP viewers allow users to access data in an OLAP database without knowing multidimensional expressions (MDX). Many of the leading BI suite vendors offer OLAP viewers to third-party OLAP data sources, sometimes via the business query and reporting tool, or via a production reporting tool, or via a special
OLAP viewer. Examples of specialty OLAP viewers include Microsoft ProClarity (acquired by Microsoft in 2006) and Panorama NovaView.

Serian todas aquellas herramientas que nos van a permitir realizar consultar o informes para obtener información sobre los datos.

Este tipo de herramientas las podemos utilizar en dos ambitos:

-Entorno transaccional: desde las herramientas de informes o querys, atacamos directamente a la base de datos donde se registran las transacciones cotidianas de la empresa. En este entorno, las consultas son mucho mas complejas, los tiempos de respuesta son mayores y seguramente estaremos interfiriendo con otros procesos informaticos que comparten la plataforma (imaginemos el ejemplo de una lectura historica de datos años atras, en el mismo servidor donde se esta realizando la facturación de clientes).

-Entorno del Datawarehouse: atacamos directamente a la base de datos que esta optimizada para el análisis. Utilizamos una estructura y tecnología muchos mas apropiada y obtenemos mejores resultados, velocidad, etc.

Por ejemplo, un informe típico podrían ser las ventas por familia, por zona geográfica, el número de empleados y coste salarial por delegación, etc. Estaría destinado a usuarios con una aptitud técnica limitada y orientado a analizar de una forma sencilla tipo foto la información histórica.

Las herramientas de informes y querys son cada vez mas avanzadas, permitiendonos definir multiples formatos de presentación, definición de filtros, exportación de los datos obtenidos a multiples formatos (excel, pdf, etc), plantillas, campos calculados, totalización, agrupación, etc. Nos permitirian contestar a la pregunta “¿que sucedio?”.

De cara a la utilización de este tipo de herramientas, podemos definir varios niveles de utilización según la experiencia y formación del usuario, que podrían ser:

•Los usuarios poco expertos podrán solicitar la ejecución de informes
o consultas predefinidas según unos parámetros predeterminados (preparamos un repositorio de informes ya diseñados, y el usuario simplemente indicara los criterios de selección al ejecutarlos).

•Los usuarios con cierta experiencia podrán generar consultas flexibles mediante una aplicación que proporcione una interfaz gráfica de ayuda.

•Los usuarios altamente experimentados podrán escribir, total o parcialmente, la consulta en un lenguaje de interrogación de datos.

Algunas herramientas para reporting Propietarias son (aunque existen una multitud de herramientas y cada una de ellas podrá ser apropiada según nuestras necesidades):

1. Oracle BI Publisher
2. Microsoft Reporting Services
3. BusinessObjects Crystal Reports (SAP)
4. Microstrategy
5. Information Builders
6. IBM Cognos

Algunas herramientas para reporting de Opensource  ( gracias a Todobi.com, en http://todobi.blogspot.com/2009/11/las-mejores-herramientas-reporting-bi.html ):

1. BIRT Project (Actuate)
2. Pentaho
3. OpenRPT
4. OpenReports
5. FreeReportBuilder
6. Magallanes
7. ART – A lightweight reporting solution
8. DataVision
9. The Wabit – Open Source Reporting Tool
10. Rilb
11. JavaEye Reporting Tool – JERT
12. iReport

When I was originally contacted by Binary Fountain (who is now a sponsor of the Procurement Insights Blog) asking me to review and write about their solution, my initial thought was to view their offering through the lens of quantifying results.  Specifically, by providing timely access to near real-time intelligence, their clients would be able to measure the success of a program after the fact.

Seems like a reasonable approach, as Business Intelligence “BI” is after all leaving the cumbersome and confining realms of traditional ERP-centric platforms in which the extraction of needed information was an onerous task that warranted the moniker of “data mining.” However, and what is compelling about emerging BI solutions such as the one offered by Binary Fountain, is its ability to proactively influence decision-making through an intuitive “dashboard” technology that creates what I call a perpetual cycle of data verification.

Let’s look at one actual real-life example with which we (within the procurement profession) can readily relate . . . the utilization of Small, Women and Minority-owned suppliers.

Historically, the greatest challenges associated with these important and necessary programs were at the points of initial engagement, tracking transactional activity and quantifying the distribution of business through an analytical lens that would not only provide verification of targets being met, but also the effectiveness and impact of the program on the enterprise as a whole. The ability to quantify impact in particular is important, as understanding the gains as well as identifying any potential losses will elevate these programs from ones of muted political correctness to that of a practical strategy.

Given the aforementioned cumbersome processes associated with extracting data, many organizations found that initiatives centered on the engagement of Small, Minority and Women-owned businesses created more work than the results or returns warranted. This meant that the focus of analysis was limited to achieving what many felt were arbitrary “numbers” that in reality had little to do with the objectives of the buying enterprise as a whole. In other words, the program was the right thing to do politically versus being the smart thing to do financially. As a result, the term “set-aside” became a negative connotation of unearned entitlement. Nothing of course could be further from the truth, especially when you extend the quantifying metrics to include economic impact on a particular region or state.

This of course is where the Binary Fountain “dashboard” and complementing range of intelligence tools come into play. By streamlining the data gathering process to one of a seamless back-end integration that becomes part of the original transaction process itself, means that the time-consuming practice of extracting, analyzing and synthesizing information has been virtually eliminated. The result is that the focus can be placed where it needs to be, which is on the establishment and subsequent measurable impact on real-world objectives thereby broadening if you will, the intelligence landscape. From here, program assessment makes the all-important transition from one of intuitive expectation (re it should be beneficial) with sporadic or incomplete data support, to one of conscious clarity (here is where it is beneficial) through the timely access to accurate intelligence.

While I will leave it up to you to check out the Binary Fountain offering in greater depth through their profile page here on the Procurement Insights Blog, increased and reliable access to meaningful intelligence does create the confidence to pursue a strategy with a high degree of certainty relative to outcome. In an increasingly complex supply chain practice in which multiple and at times seemingly disparate objectives have to be incorporated into the decision-making process, Binary Fountain’s intuitive dashboard would seem to be a natural progression for your business.

By Bob Stein

Professional, reliable and easy to use Business Intelligence (BI) is essential to maintaining an edge and meeting financial performance objectives in the increasingly competitive convenience store retailing industry. The amount of change in the economy today, which can affect consumer confidence and behavior on an immediate basis, requires agile responses based on insights provided from valuable business data. Volatility in fuel prices, the stock market, retailer sales results and rising unemployment all make it necessary to be knowledgeable and dynamic in making decisions and changes in the business. To do so requires good data that is well organized and provides actionable insights: this is what Business Intelligence tools can do for retailers.

Characteristics of robust BI tools include the ability to efficiently compile data and report key performance indicators in an easy-to-understand visual/graphic “dashboard” that provides visual context of a company’s performance. For small and large retailers alike, BI offers an array of benefits that can improve a business owner’s decision making and increase profitability.

The “dashboard” interface of today’s BI solutions start with a daily snapshot of a convenience store operator’s business, placing data at its fingertips and allowing it to drill down from the dashboard to retrieve more details. Users can instantly compare today’s information against either budget or performance from previous months, quarters or years. Thus, the system gives store operators the tools needed to efficiently react to changing market conditions and make quick, competitive business decisions.

Users no longer have to spend countless hours sifting through multiple reports and data, because the BI software pulls all the available data as needed and consolidates information from various sources in different places, to immediately provide details about the business. BI quickly delivers information that in the past may have taken hours or even days to track down and compile.

The best business intelligence solutions identify both problems and opportunities for a convenience store business. BI can help retailers decide where to devote their resources and what operational trends they can exploit. The information helps users quickly respond to problem areas and improve store performance. Most importantly, the BI solution empowers convenience store operators to make decisions that can help affect their business in the most positive way, and ultimately gives them the data needed to gain a competitive advantage. The advent of Software-as-a-Service (SaaS) solutions means that even the smallest companies can obtain complete BI functionality and benefits.

Fortunately, even for small chains and single-store operators, access to quality BI is not out of reach. Traditionally, organizations had to either build or buy expensive systems to obtain BI power and knowledge. Today, Business Intelligence is no longer about massive, complex databases that require large staffs and larger IT budgets to maintain. Many tools can operate on small servers and interface with existing data warehouses to create decision support tools. This is inexpensive and fast.

With the SaaS model, the BI software and corresponding data are hosted by a technology vendor. Users, from independent retailers to large chains, pay a monthly subscription fee to access the power of BI — essentially renting the software and only paying for what they use.

Because users share the technology, vendor’s software and staff, this reduces upfront investments on servers and infrastructure, and eliminates the need for an IT staff to maintain and update the database. This does not mean, however, that a company’s information can be easily accessed and compromised. Advanced security features ensure complete confidentiality with this hosted model. The result is a BI solution — available today — that is faster, simpler, visually appealing, intuitive and best of all, more affordable than traditional solutions.

Hosted SaaS models not only benefit small retailers who lack the IT staff and resources to build, buy and support intricate BI software systems, but large organizations can also benefit by morphing their very complicated and involved systems into a much more simplified solution. The SaaS solutions available today make integrating BI into an organization very easy, regardless of size or budget.

With business more volatile and competitive than ever, BI is no longer a “nice to have,” it is now a necessary tool that enables retailers to stay at the forefront of their business by giving them access to the business data they need to make timely decisions and remain competitive. BI is a trend that is rapidly growing, and businesses today that don’t make use of it will be at an extreme disadvantage in a very short time.

The innovative, affordable BI tools available today are becoming more ubiquitous throughout the convenience store industry. These solutions are helping retailers of all sizes better understand their business and make optimal decisions that will ensure business success in the future and keep the convenience store industry healthy and prosperous.

At IBM’s 8^th annual Connect meeting with analysts, Steve Mills, Senior VP and Group Executive, had much to crow about. Software is the engine driving IBM’s profitability, anchoring its customer relationships, and enabling the vaulting ambition to drive the company’s Smarter Planet theme into the boardroom. Mills’ assets are formidable: 36 labs worldwide have more than 100 SW developers each, plus 49 more with over 20 – 25,000 developers in all. Mills showcased all this in a matter-of-fact, businesslike fashion with minimal hype and little competitor bashing. A research project aimed at extending Hadoop usage to a broader audience was among the highlights. 

Mills gave us a look at his organizing principle:

We have been working on extending the notion of what middleware is. It’s about connecting an organization’s applications, the codification of business process and function.”

Companies from midmarket to large enterprises run thousands of applications; understanding customers’ business scenarios, addressing identified gaps and promoting recommended patterns for success - adoption routes, solution stacks - is the driver. “It’s very easy to make a mess if you’re not guided,” Mills points out. He’s an effective, dedicated proponent of IBM’s Smarter Planet theme, and returned to it at this event, pointing out how IBM-supported projects that instrument and enhance the world’s often aging physical systems pay for themselves in efficiency savings even before the larger goals they enable are considered. He also held forth on other favorite topics: Industry Models, Cloud Computing (“You have to talk about Cloud”), and more, but told us he’d promised not to use all his team’s best slides before they could. “Not that I can’t talk about all of it,” he joked – and we’ve seen him do it. But no 3 hour keynotes here, mercifully, unlike some other vendors’ recent events.

Bringing Hadoop to Business Users

In his presentation, Rod Smith, VP, Emerging Internet Technologies, made it clear that the company is not ignoring the MapReduce/Hadoop phenomenon. He referred graciously to Cloudera’s work and picked up their phrase: “big data.” With the world creating nearly 15 PB of new data per day, a new class of content-centric WebApps is on the horizon, typically “longer running apps” – customers Smith talks with don’t like the word “batch,” he noted. But his focus was different from other vendors I’ve been hearing, where there is an assumption that the “big data” opportunity is limited to the sophisticated programmers who have so far led the way. Instead, “Put the business person in the center of the data,” Smith suggested. ”They want their own Google” – here meaning not a search engine, but a data interaction tool capable of visualization and other forms of manipulation.

It’s clear that the need for such solutions will be there, and someone will fill it. When a firm like Extrabux can process 40Gb/day, loading and indexing 70 million constantly changing input records for MapReduce by processing on Amazon’s EC2 cloud for less than $5000 per year – with no DBA – others will follow. (See the September issue of Charles Brett’s Insight-Spectra  for details of this case study.) Like other explorers in this new mode, Smith offered his own great examples, including  a Visa risk modeling app using Hadoop with the R statistical libraries that reduced an analysis literally from 1 month to 13 minutes. “This is not incrementally better; it changes everything,” he said.

Smith’s Big Sheets project showed off analysis performed on over 2 million patent documents – a “one person project, like all my things.” He referred to the iTunes interface and showed a similarly clean, intuitive model. And he pointed out that “the data operated on does not always get reduced; here it exploded, because one analysis was of how patents made references to other patents.” Similar things happen when analyzing social graphs; it’s why focusing on MapReduce alone to describe these cases doesn’t always paint the full picture. It’s just one step in more complex processes that can be distributed around large systems which scale on demand as needs dictate. Similar thinking about user empowerment, without the elastic scaling (yet), is behind Microsoft’s PowerPivot, which treats Excel as the UI, and adds operators to the Excel language which mimic the kinds of things MDX programmers can do with OLAP cubes, among other things.

IBM is looking past today’s MR cases, which are often reminiscent of early computing days, when specialists spent days to set up machines for a single program run. The problem then was scale too, and learning how to use machine resources efficiently was job one. Today, the economics have flipped – we understand that the people resources are more valuable and we have to empower them. IBM is looking beyond complex setup, java coding and single run models for “big data” processing and towards interactive big data analysis – at Web scale. In Smith’s view, that’s the key to going into an “evidence-based business world.” IBM is focused on hiding the complex details of system parallelization, fault tolerance, load balancing, etc. from the user by hiding everything behind the UI. Tech details weren’t at the top of Smith’s agenda for this presentation, but REST interfaces, the use of Jackal, extensibility via UDFs, integration of Pig, and exporting results into feeds and XML were briefly highlighted. As IBM continues to push at this area, we can expect to see some breakthrough innovations emerge, in larger, end-to-end scenarios.

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