Some of these insights include KPI’s (Key Performance Indicators), data visualization, big data, mobile BI and much much more. The goal of the training for the trainee is to master these insights and techniques, allowing them to confidently apply this new knowledge to their field of expertise. The Business Intelligence training program will give you an overview of the two training days.

Day 1 will focus on the important business aspects in relation to Business Intelligence (BI) and Business analytics. International BI trends such as BI in the cloud (what are the benefits?), mobile BI (will everyone be connected?), big data (hype or big business?) and Business Analytics (vendors trying to create the next hype?) will be discussed followed by a group discussion. Indicators such as KPI’s (key performance indicators), KRI’s (Key Results Indicators) and PI’s (performance indicators) will be looked at including the relation between them.

Next the creation of expert dashboards and reports will be taught which will increase productivity and the return on investment. This will be followed by business intelligence project management and governance, which looks into the 10 steps BI project methodology which can ensure success. In addition it looks at the most important pitfalls, scoping and also making the business case for BI. Lastly a case study will be will investigated on how to increase organizational performance by looking at Ahold, winner of the Dutch Business Intelligence Award. Many discussions and assignments will be undertaken on this day.

Day 2 will focus on the more important technical aspects in relation to BI and Business Analytics. Data visualization and data quality will be discussed, focusing on how data can be transformed into useful information, whether 3D works on 2D devices, when to use certain types of graphs (lines, bars, pies, etc), methods for improving data quality and data profiling tools will be discussed.

This will be followed by a discussion on real-time BI, focusing on integrating BI into the Web, difficulties when searching the Web for relevant information and some good examples of real-time invisible BI will be looked at. How does one create a sustainable Business Intelligence Framework? This question will be investigated by focusing on re-usability, usability, role based access, superior performance and creating BI apps in quick time. Lastly the choice between different BI architectures and tools for BI and Business Analytics is investigated. Important selection criteria, major steps in the selection process and research findings on BI tools will be focused on.

These two days will bring anyone up to speed with the basic and advanced BI knowledge. Not only IT experts should be aware of the above technicalities, therefore it is paramount to any organization that Directors of Finance, managers in performance management, IT, BI, and data warehousing should all comprehensively be able to use the above concepts and techniques. Grasping these will at no doubt lead to a better functioning organization.

CAMBRIDGE, Mass. – 08 Nov 2011: New research released today by MIT Sloan Management Review and the IBM Institute for Business Value (NYSE: IBM) reports that organizational challenges, more so than technology hurdles, are holding companies back from fully integrating analytics across their enterprises.

According to a global survey of more than 4,500 executives, managers and analysts from more than 120 countries and 30 industries, 44 percent of organizations say cultural barriers to enterprise-wide analytics adoption, such as the requirement for new leadership competencies and organizational resistance to new ideas, are the primary barriers.  In contrast, only 24 percent point to technology concerns.

Org Challenges Graphic:  New research from IBM and MIT Sloan Management Review reports that cultural barriers, more so than technology hurdles, are holding organizations back from fully integrating analytics across their enterprises. This chart shows the percentage of respondents who rate these challenges as extremely difficult to resolve.

The new report, entitled “Analytics: The Widening Divide,” builds on the findings from the original study by MIT SMR and IBM in 2010 to understand how companies are embedding analytics in more of the enterprise’s processes and operations. The 2010 study found organizations fall into one of three levels of sophistication: basic users referred to as Aspirationals, followed by the more Experienced users, and the most advanced users referred to as Transformed.  Year-to-year comparisons reveal that the more sophisticated users are expanding their deployment of analytics and widening the performance gap over their peers.

For instance, from 2010 to 2011 the percentage of respondents who cited a competitive advantage using analytics grew 23 percent for Transformed and 66 percent for Experienced organizations.  These same organizations are more than twice as likely to substantially outperform their competitive peers. In contrast, Aspirational organizations lost ground in competitiveness, falling 5 percent since last year.

“Our new research shows that the early and aggressive adopters of analytics make significant gains in both performance and overall competitiveness,” said Fred Balboni, IBM’s global leader, Business Analytics and Optimization.  ”These indicators point to an urgent need for organizations to foster a data-oriented culture and drive an analytics strategy that embeds fact-based insights into decisions and processes at every level of the business.”

Competitive Advantage Graphic:  The ability of organizations to create a competitive advantage with analytics has surged in the past 12 months, according to new research from IBM and MIT Sloan Management Review.  This chart shows the percentage of respondents who cited a competitive advantage using analytics, year over year, grouped by analytic sophistication levels

“We’ve found that there are three legs to the competitive analytics stool: a data-oriented culture, information management competency, and analytics expertise,” said David Kiron, executive editor for MIT Sloan Management Review.  ”Companies that have all three use analytics to deliver advantage in the marketplace.”

The study found that the majority of organizations are using analytics to manage their financial and operational activities, but are less likely to rely on analytics-based insights for decisions in other key areas.  On average, less than 25 percent of Aspirational organizations, and one-half of Transformed organizations, say they rely on data and analytics to make decisions involving customers, business strategy and human resources.  Even Transformed organizations are not using analytics to their fullest potential, indicating ample opportunities for advanced users to do more and for less sophisticated organizations to create a competitive advantage by targeting analytics at key strategic activities.

While Transformed organizations use analytics more broadly across the organization than their peers, they differentiate themselves by intensely focusing on applying analytics to three areas:

• Increasing the speed of decisions – Transformed organizations are more than three times more likely than Aspirationals to focus intensely on making better decisions, faster.
• Managing enterprise risks – Eighty-six percent of Transformed organizations are addressing the full range of organizational risks that can impact their business, while none of the Aspirational organizations have the same level of focus.  Transformed organizations are using analytics to not only mitigate, but also anticipate risks.
• Engaging customers – Transformed organizations are outpacing their peers in leveraging the enormous amounts of data available today to understand and engage with their customers in new ways. Two-thirds of them are putting analytical insights into the hands of customer-facing employees to drive sales and productivity — compared to one-fourth of Aspirationals.

The study examines how Transformed organizations are creating an advantage in the marketplace. The analysis shows that of all the characteristics exhibited by this group, their proficiency in six areas (represented by the percentage of Transformed companies that say they possess these characteristics) distinguished them the most:

• Ability to analyze data – 78%
• Ability to capture and aggregate data – 77%
• Culture open to new ideas – 77%
• Analytics as a core part of business strategy and operations – 72%
• Embed predictive analytics into process – 66%
• Insights available to those who need them – 65%

To access the full report, visit MIT SMR or IBM.  Go here for more information on the MIT SMR/IBM joint New Intelligent Enterprise project.

About MIT Sloan Management Review

A media company based at the MIT Sloan School of Management, MIT Sloan Management Review’s mission is to lead the conversation among research scholars, business executives and other thought leaders about advances in management practice that are transforming how people lead and innovate. MIT Sloan Management Review captures for thoughtful managers the creativity, excitement and opportunity generated by rapid organizational, technological and societal change. Source: IBM.

By John Stokdyk

Millions of words – mostly impenetrable jargon – have been written about business intelligence (BI) and performance management. And Microsoft BI global strategy honcho Bruno Aziza has added 50,000 or so more with a new book, ‘Drive Business Performance’.

But on the way to meet him in London recently, the thought occurred that if you set aside the underlying technology for a minute, what we’re really talking about is organisational culture.

For example, CPM and business intelligence tools feature not once, but twice in our Software Satisfaction Awards – once in the sales and marketing category and once in the accounting and finance section. The territorial divide reflects life on the corporate front line, where sales and marketing people want fast, responsive business intelligence – and will go out and get it for themselves if finance can’t come up with the goods.

Finance managers, meanwhile, are more at home with Excel, Crystal Reports and all the other devices of data analysis. More often than not, they’ll look down their noses at DIY reporting by other departments and cast doubt on the accuracy of the numbers. And if we’re being pedantic, poor old HR didn’t even get a look in, even though good people information can be invaluable for reducing costs and planning resource deployments more efficiently.

Conceptually, BI is simple: data produced by an organisation’s transactional processing and operational IT systems can be collected and summarised into totals and reports that give managers get an immediate view of how they’re doing. IT initially made its presence felt within finance to speed production of management and statutory accounts, so the evolution towards BI has traditionally started within that department.

Microsoft’s BI point man, Bruno Aziza, agrees that performance management is about people and culture and argues says it should involve every knowledge worker within an organisation. But he also admits that some behavioural hurdles still need to be overcome.

“Performance management is more than financial reports for finance managers. If it stays in finance, it doesn’t empower the rest of the organisation,” he says. “Often the driver will be when sales, marketing, operations and HR ask for reports that finance can’t answser. Operations wants real time answers when finance is 2-4 weeks behind.

“You can’t roll out BI and expect everyone to be the same. Roles are different and so are the needs and metrics of different departments. You’ve got to understand the interdependencies. You’re not trying to deliver all the information to everybody and make people be like the CFO. You want to get relevant information to each.”

Aziza describes performance management as an “interdependency” where the organisational models tie into sales models and forecasts. This nirvana was achieved at one of the cases studied in Aziza’s book, battery company Energizer, where BI originated in operations, which now matches its production plans to sales forecasts that are closely aligned with the company’s financial goals.

Performance management may have worked for Energizer, but real life isn’t always that smooth for less focused and well motivated organisations. In a precursor to this article last year, AccountingWEB investigated the territorial dispute between finance and IT over business intelligence.

Microsoft’s BI point man, Bruno Aziza, agrees that performance management is about people and culture and argues says it should involve every knowledge worker within an organisation. But he also admits that some behavioural hurdles still need to be overcome.

“Performance management is more than financial reports for finance managers. If it stays in finance, it doesn’t empower the rest of the organisation,” he says. “Often the driver will be when sales, marketing, operations and HR ask for reports that finance can’t answser. Operations wants real time answers when finance is 2-4 weeks behind.

“You can’t roll out BI and expect everyone to be the same. Roles are different and so are the needs and metrics of different departments. You’ve got to understand the interdependencies. You’re not trying to deliver all the information to everybody and make people be like the CFO. You want to get relevant information to each.”

Aziza describes performance management as an “interdependency” where the organisational models tie into sales models and forecasts. This nirvana was achieved at one of the cases studied in Aziza’s book, battery company Energizer, where BI originated in operations, which now matches its production plans to sales forecasts that are closely aligned with the company’s financial goals.

Performance management may have worked for Energizer, but real life isn’t always that smooth for less focused and well motivated organisations. In a precursor to this article last year, AccountingWEB investigated the territorial dispute between finance and IT over business intelligence.

In the view of consulting editor David Carter, BI solutions were failing to deliver because the technologists who invented and managed them had no appreciation for what the numbers meant. Accountants are trained to test the validity of their numbers and have the ability to explain what they mean, added accountant-turned-technologist Jim Johnson. But often many accountants shy away from the technological frontline of BI and performance management.

Data accessibility and accuracy
The realities of daily business life also throw up a lot of practical difficulties. As Carter explained in another article, a sales department seeking daily or weekly feedback on revenues and volumes will look for the information from the sales order module of its accounts program. But orders are usually handled by sales admin or other customer-facing staff, which means that finance is often be left out of the loop.

The front office team is concerned about serving the customer. Creating an accurate database for management reporting comes much lower down their list of priorities. So sales invoice data can often be riddled with errors. The sales prices are going to be accurate – because customers will query any that aren’t, but cost prices and analysis codes can go awry, which is where the arguments begin.

Getting sales and finance to agree on gross margin figures on a computer sales report is notoriously difficult. There are so many different data sources that perfectly legitimate numbers in the sales daybook, summary reports and P&L won’t tie up. But if these sales reports all show different figures, managers will lose confidence them.

Source: MyCustomer.com

Now that both established vendors and upstarts offer BI applications as on-demand services, more customers are saying yes to SaaS – gaining faster deployment, and speedier access to reporting data.

Ask Dennis Hernreich, COO and CFO of Casual Male Retail Group, what his life was like before he switched to an on-demand business intelligence reporting application, and he remembers the frustration all too easily.

Casual Male Retail Group, a specialty retailer of big and tall men’s apparel with Rs 1,856 crore in annual sales, was using a legacy on-premise reporting application for its catalog operations. (The company also has 520 retail outlets and e-commerce operations.) But the reporting features built into the system were “extremely poor,” as Hernreich describes them: “Visibility to the business? Terrible. Real-time information? Doesn’t exist. How are we doing with certain styles by size? Don’t know.”

“It was unacceptable,” Hernreich says. And viewing those ‘canned’ BI reports (which lacked features such as exception reporting) could happen only with trips to the printer for a stack of printouts. “It was hundreds of pages,” he recalls. “That’s just not how you operate today.”

It’s not like Casual Male didn’t have all this information; it just didn’t have an intuitive and easy way to get at its catalog business’s sales and inventory trends in real-time. But that changed in 2004, when Casual Male began using a on-demand BI tool from vendor Oco, which takes all of Casual Male’s data, builds and maintains a data warehouse for it offsite, and creates “responsive, real-time reporting dashboards that give us and our business users information at their fingertips,” Hernreich says.

Today, Hernreich and Casual Male’s merchandise planners and buyers have access to easy-to-consume dashboards chockfull of catalog data: “What styles are selling today. How much inventory are we selling today. Where are we short. Where do we need to order. How are we selling by size. What are we out of stock of,” he says. “All of these basic questions, in terms of running the business — that’s what we’re learning every day from these reports.”

On-Demand Fears Linger

Casual Male Retail Group is part of the small (but growing) percentage of businesses using software-as-a-service (SaaS) BI tools, which can be deployed at a much faster pace and with much less initial cost than traditional on-premise software installations.

“To go from nothing to a fully automated system in a matter of weeks is an incredible sell for any company — large or small,” says Scott Cohenford, a senior analyst at RapidAdvance, a provider of cash advances to small and midsize businesses, who led his company’s efforts to purchase Business Object’s OnDemand platform.

Note that Cohenford is not an IT person by title or pedigree (he has an accounting background): ease of use (setup, integration, training) is a major selling point to on-demand BI customers. “I was tasked with reviewing the different options out there, seeing how quickly we could move forward and do so at a low cost and automate as much as possible,” Cohenford says. “And that’s what pushed me into the SaaS world’s BI tools.”

But a nagging majority of companies don’t share Cohenford’s sentiments, despite loads of hype and the success of SaaS pioneer Salesforce.com. A Forrester Research survey of 1,017 technology decision-makers found that adoption of SaaS and on-demand applications in large enterprises is now at just 16 percent. Aberdeen Group research showed that just 10 percent of all-sized companies used some form of BI analytics tools through third-party service provider, says David Hatch, research director of BI at Aberdeen.

The Forrester survey noted the oft-cited barriers to higher adoption rates, including concerns around integration, customization, security and total cost of ownership.

Of course, those concerns don’t go away for on-demand BI applications, but several recent macro trends have pushed companies to take another look.

One is the long-term effects of software industry consolidation and BI vendor upheaval in 2007, which has affected the plans of more than 100,000 customers of the established BI vendors, according to Hatch’s research. “This has opened the door to new, innovative BI technology developers and marketers,” Hatch writes in a recent BI report, “who see an opportunity to capture the attention of established BI customers with low-risk offerings that address questions resulting from all of the M&A activity.”

The Need for Speed

Even traditional on-premise vendors, like Business Objects and Cognos (now owned by IBM) are starting to offer on-demand solutions, responding to the rise of several pure-play on-demand BI vendors. “The increasing speed, power and availability of on-demand solutions are narrowing the performance gap between on- and off-premise application implementations,” Hatch notes.

Business Objects leads the on-demand BI space, with 70,000 subscribers. But smaller vendors, such as Oco, SeaTab, LucidEra, Dimensional Insight and OnDemandIQ, are nipping at its heels — offering customers both on-demand suites of reporting and analytic tools as well as highly targeted applications that solve specific customer needs and are delivered in weeks, not months or years.

Hernreich describes Oco’s integration work with Casual Male’s systems as “unobtrusive,” taking roughly six to eight weeks to complete. “And it worked on day one,” he says.

BI vendors are also hearing a lot of pain from current and potential customers who need a quick fix: inside companies of all sizes, the pressure to aggregate, synchronize and deliver clean and actionable data to business users has never been more intense. A recent Aberdeen survey of 4,300 companies found that the number-one technology that could have the greatest impact on the business during the next two to five years was BI and analytics. (SaaS initiatives grabbed the second spot.)

It’s Not Just for SMBs

Vendors offering on-demand and SaaS applications have made names for themselves by serving midsize and smaller companies, which typically don’t have the IT resources to dive into a full-fledge on-premise rollout. The partnership makes a lot of sense for both parties, say analysts.

“Mid-market companies lack the sunk costs that large enterprises have already invested in a BI infrastructure. They have similar requirements to integrate, report and analyze data from numerous systems, but they don’t have the staff or infrastructure to pull it off,” writes Gartner Research Director Kurt Schlegel in a February 2008 report on BI self-service options. “Plus, mid-market companies are often in volatile business cycles where revenues could grow quickly or come crashing down.”

But larger companies are also starting to test the on-demand waters with targeted applications. Welch’s, the Rs 2,616-crore consumer-packaged goods manufacturer known for its jams, jellies and juices, recently installed an on-demand BI application from Oco. In January, Welch’s rolled out a transportation logistics BI application that serves up analytic insights from Welch’s systems and its distribution partners’ in a way that its Oracle BI software (installed in 2007) could not.

“We’re essentially capturing every element — from the customer orders we receive, to bills of lading on every shipment we make, as well as every data element on every freight bill we pay,” says Bill Coyne, director of purchasing and logistics for Welch’s. “We dump them all into one data warehouse [maintained by Oco], and we can mix-and-match and slice-and-dice any way we want.”

Coyne says that Welch’s tries to ship its products five days a week out of its distribution center. “But we found ourselves just totally overwhelmed on Fridays,” he says. “We would complain, ‘How come there are so many orders on Friday?’”

It turns out that Coyne’s team was doing it to themselves. The data aggregated into Oco’s data warehouse and sliced-and-diced by Coyne’s team revealed their errors, which they have now fixed. “Just trying to steer away from Fridays provides us a huge benefit,” Coyne says. “We can look at the number of orders per day, the number of orders by day per customer and overall customer order patterns.”

Welch’s spends more than Rs 200 crore each year on transportation expenses, and the Oco BI application and reporting features have become critical in a very short period of time, he says. “We literally can’t go any amount of time without knowing this stuff,” Coyne says.

In addition, training users on the new application is “the equivalent of training someone to use Google,” he notes. As to the payback, it’s been “almost instantaneous.”

On-Demand Changes IT’s Role

The oft-cited concerns regarding on-demand and SaaS applications (integration, customization, security) typically don’t emanate from the business side of an organization. Typically, they come from IT groups already under intense pressure from project backlogs and a lean number of staffers, who most likely don’t have BI development skills. “The IT and BI skill sets that are required to meet this demand are in limited supply,” notes Aberdeen’s Hatch.

With easy-to-install on-demand applications, IT’s role as gatekeeper is minimized, say analysts. By 2012, Gartner’s Schlegel predicts that emerging technologies such as on-demand and SaaS BI tools will make users “less dependent on central IT departments to meet their BI requirements.”

However, even the most enterprising line-of-business executives have to realize they need IT’s buy-in and support for on-demand BI applications. Welch’s had just finished the four-year, Rs 124 crore project installing Oracle’s ERP applications in-house, which included a BI application, and IT was understandably “concerned” about Coyne’s new external application needs, he says. “They wanted to make sure we were doing the right thing,” Coyne says, “but even they said that this particular area of Oracle does not have a solution for it. It wasn’t much of a debate at all.”

One major sticking point for IT usually involves the security of corporate data as it moves outside of IT’s control. But executives and analysts say that the potential business benefits of quicker access to BI data, coupled with the robustness of third-party providers’ security may outweigh concerns.

Casual Male’s Hernreich says he was initially “nervous about the information being offsite,” but that the final analysis showed the business benefits of a SaaS business intelligence solutions were too big to ignore.

And for SMB companies like RapidAdvance, well-established vendors’ security controls are a benefit — not a concern. “For a small company to leverage and piggyback off the firewalls and data protections [of a vendor like Business Objects] was a big selling point,” Cohenford says.

To many companies, the future of on-demand BI applications is already here. Says Hernreich: “It’s essential to operating the business.”

Source: CIO India

EMC Corporation today unveiled the findings of the largest-ever global survey of the data science community. Spanning the United States, the United Kingdom, France, Germany, India and China, the EMC Data Science Study reveals and quantifies a rampant scarcity across the globe for the prerequisite skills necessary for a company to capitalize on the opportunities found at the intersection of Big Data and data analytics.

Only one-third of companies are able to effectively use new data to assist their business decision-making, gain competitive advantage, drive productivity growth, yield innovation and reveal customer insights.

Explosion of Digital Data

The survey revealed that the explosion of digital data created by mobile sensors, social media, surveillance, medical imaging, smart grids and the like — combined with new business intelligence tools for analyzing it all – has created a corresponding explosion in the opportunity to generate value and insights from the data. As such, the business demand for data scientists has quickly outpaced the supply of talent.

The Data Science Study

The EMC Data Science Study respondents included nearly 500 members of the data science community globally including: data scientists and professionals from related disciplines such as data analysts, data specialists, business intelligence analysts, information analysts and data engineers globally, all of whom have IT decision-making authority.

Key Findings

• Informed Decision-making—Only 1/3 of respondents are very confident in their company’s ability to make business decisions based on new data.
• Looming Talent Shortage—65% of data science professionals believe demand for data science talent will outpace the supply over the next 5 years – with most feeling that this supply will be most effectively sourced from new college graduates.
• Barriers to Data Science Adoption—Most commonly cited barriers to data science adoption include: Lack of skills or training (32%) budget/resources (32%), the wrong organizational structure (14%) and lack of tools/technology (10%).
• Customer Insights—Only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.
• New Technology Fueling Growth—83% of respondents believe that new tools and emerging technology will increase the need for data scientists.
• Lack of Data Accessibility—Only 12% of business intelligence professionals and 22% of data scientists strongly believe employees have the access to run experiments on data – undermining a company’s ability to rapidly test and validate ideas and thus its approach to innovation.
• Advanced Degrees—Data scientists are 3 times as likely as business intelligence professionals to have a Master’s or Doctoral degree.
• Augmenting Business Intelligence—Although respondents found an increasing need for data scientists in their firm, only 12% saw today’s business intelligence professionals as the most likely source to meet that demand.
• Higher-Level Skills—Data scientists require significantly greater business and technical skills than today’s business intelligence professional. According to the Data Science Study, they are twice as likely to apply advanced algorithms to data, but also 37% more likely to make business decisions based on that data.
• Love the Work—The study discovered highly favorable attitudes toward the companies where they work. In fact, data scientists believe their IT functions are better aligned and better able to attract talent, are ahead in key technology areas like cloud computing and not surprisingly rate their company’s data analysis and visualization abilities very favorably compared to the views of business intelligence professionals.
• Involved Across the Data Lifecycle—Data scientists are more likely than business intelligence professionals to be involved across the data lifecycle–from acquiring new data sets to making business decisions based on the data. This includes filtering and organizing data as well as representing data visually and telling a story with data.
• Tools of the Trade—Data scientists are more likely than business intelligence professionals to use scripting languages, including Python, Perl, BASH and AWK. Yet, Excel remains the tool of choice for both data scientists and business intelligence executives, followed closely by SQL.

Data Scientists Quotes

Andreas Weigend, Ph.D Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist Amazon.com

“We live in a data-driven world. Increasingly, the efficient operation of organizations across sectors relies on the effective use of vast amounts of data. Making sense of big data is a combination of organizations having the tools, skills and more importantly, the mindset to see data as the new “oil” fueling a company. Unfortunately, the technology has evolved faster than the workforce skills to make sense of it and organizations across sectors must adapt to this new reality or perish.”

Michael Driscoll, Ph.D Boston University, Co-Founder and CTO at MetaMarkets

“Neither tools nor people alone can solve the challenges of Big Data. They must work together and that is the promise of data science. Despite advances in business intelligence software tools, the number of people with experience using these tools, and with real-life exposure to large-scale data sets, is small. Data science is a young field, and its growth will be fueled as much by technology as through the mentorship of new acolytes by leading practitioners.”

Additional Resources

• Read the EMC Data Science Study in full

What is true business intelligence?

In our view it’s running your business better using key information about your processes, your clients and the market. To be able to do that you should gather all kinds of data from a variety of source systems inside and outside your company network, integrate the data and transform it into information to produce insights in such a way that we can speak of ‘intelligence’. Each company has it’s own intelligence which can bring a real competitive advantage, allowing companies to swim in the profit pool.

What is a cloud?

A cloud is an infrastructure of hard- and software that can be used for a period of time. You may want to use more disk storage, give more users access to the applications or use more memory. Or maybe at some time you want to drop a group of users, which does not pose a problem with the cloud concept. With your applications in a cloud, it should be really simple to scale up or down. Often you only have to pay for the usage, which can be a real advantage.

There are various types of clouds including; the private cloud and the public cloud. In the public cloud every user uses the same business intelligence software solution, the same version and has the same standard indicators and reports. In this type of cloud there is often very little space for customization as it comes down to the data that can be stored, the transformations and the key performance indicators. If you want high customization for example you should extract data from a source system that other users in the cloud don’t have. This means you should move to a private cloud. In a private cloud, which is almost the same as buying a virtual or dedicated private server from your hosting partner, which we are all familiar with, some of the advantages of being in the cloud will disappear. In a private, highly customized cloud, scaling up isn’t that easy (compared to the scalability of a public cloud), whether it is deployed within the company’s firewall or hosted by a third party.

Tap water and BI in the cloud

Cloud solutions like BI in the cloud are often compared to tap water, at least it is often visualized that way. The BI vendors are marketing their offerings of BI in the cloud as: just turn on the tap and ‘drink the intelligence’. Maybe it tastes good for a while, but what competitve advantage will this solution really bring to your company if your competitors can do exactly the same? In our view true business intelligence can’t really be in the public cloud. In addition, who likes the idea that someone else is managing their brain?

“The only way to make money in the Cloud is to have a lot of customers. The only way to get a lot of customers quickly is to give everyone the same configurable application and avoid custom development work. In the Cloud, economies of scale are everything. But BI is largely a custom development effort.” Wayne Eckerson, BeyeNETWORK

Tap water is, on average, 500 times cheaper than bottled water, some people would say, boycot the bottle. Could we say the same about BI in relation to the public cloud? Is it, on average, 500 times cheaper than having your own BI infrastructure? Even if that is true, we don’t think it’s wise to move your company specific intelligence to the public cloud, even if it is possible (there are some really nasty integration, security and compliancy issues to overcome). However, non-specific highly standardized indicators like the sickness rate could be in the public cloud as a data mart. However you may have to ask yourself if that is true business intelligence. So, don’t boycot the BI bottle!

>>>> Contact me for an appointment about BI in the cloud and vendor independent consulting.