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April 22, 2011

Traditional BI – 57 Channels (and nothin’ on)

by Shirish Netke

Dissatisfaction is the basis of progress. When we become satisfied, we become obsolete.”   J. Willard Marriott


We talk to customers often about their dissatisfaction with things as they are and hear the same pattern of complaints.  Despite increasing adoption of BI and data analytics tools, the current sets of tools are inadequate to meet the needs of users.

The market of BI is enormous.  According the recent Census 2010, there are over 20,000 large and medium-sized enterprises (organizations with over 500 employees) and ~ 7 million small businesses (organizations with ten to 500 employees) in the United States alone.  Now include Europe and Asia and you can see the potential.

However, most organizations face the following limitations:

  • Spreadsheets Not Suited for Data Analysis and Lack Reliability
  • Analysis Tools Not Designed for Business Users
  • Highly Inflexible Solutions are Difficult to Implement and Maintain
  • Substantial Total-Cost-of-Ownership
  • Bridging the gap between BI (historic look back) and Predictive Analytics (look forward)

Spreadsheets Not Suited for Analysis and Lack Reliability.  Spreadsheets are the BI weapon of choice in almost every organization. Spreadsheets have been widely adopted by users for data analysis because they are readily available and easy to use. However, spreadsheets are general purpose productivity tools designed for data input and calculation. The performance of spreadsheets declines when analyzing large data sets or performing real-time, dynamic calculations. Spreadsheets are often shared and edited by many parties, resulting in multiple versions of similar material. This lack of version control causes inconsistencies in analysis, and significant data reliability challenges.

Analysis Tools Not Designed for Business Users.  Until recently most BI tools were developed specifically for data analysts and other power users.  These systems require training and sophisticated skills to construct or modify predefined, inflexible data sets (data cubes). These tools are used to produce static reports which the business user cannot easily modify or explore in an interactive manner. A typical business user does not possess the skills or authority needed to modify the underlying data cube and therefore must engage their IT departments to reconfigure the analysis to produce the requested information between each decision cycle. As a result, business users get frustrated as they do not have access to critical data in a timely manner and may miss time-critical insights and opportunities.

Highly Inflexible Solutions are Difficult to Implement and Maintain.  Traditional BI solutions require the integration of large volumes of data stored across an organization and its partners and the development of a pre-defined summarization of the data (or data warehouse) to support static query and reporting tools. These tasks can be time-consuming and complex and often require significant professional services support to complete. In addition, traditional BI solutions can be difficult to update and require substantial investments to refresh the underlying data.

Substantial Total Cost of Ownership (TCO).  Public and private sector organizations incur significant hardware, software and professional services costs to deploy and maintain traditional BI solutions. The average BI platform implementation takes 12-18 months from the time of initial purchase. It is well known in the Software Industry that the cost of development and deployment for BI and data warehouse applications is about 3-5 times the cost of the software. These initial and ongoing costs result in a substantial TCO for many traditional BI and analytics applications. Most providers of traditional BI tools rely upon professional services revenue for a large portion of their total revenue, and thus have little incentive to migrate to a more customer friendly license-based model or to solutions that are simple to install and easy-to-use.

Bridging the gap between BI (historic look back) and Predictive Analytics (look forward). Companies are always looking for ways to bridge the gap between BI and predictive Analytics.  BI is primarily structured data analysis that aggregates transaction data from software applications like CRM, SRM, SCM, ERP and others.   Predictive analytics are forecasting, mining investments aimed at improving competitive performance and decision making, perhaps through more accurate predictions of customer demand or better modeling about the risks a business might face in its supply chain.  Bridging the two worlds is where the opportunities are and action is today.

Solving these problems for customers in search of better decisions is obviously where the opportunity is.  Customers are very interested in leveraging all the data they are gathering and using it to come up with a competitive advantage and improve their business. There is a lot of investment going into easy to user visualization,  a tremendous of innovation in that space.


Now that most firms have the core systems – E-commerce, ERP, CRM, SCM, WCM, HRIS, SFA – in place, they are beginning to look ahead at BI and analytics.

But the journey to useful analytics will take time and effort. When asked what is the biggest hurdle to business analytics implementation in your organization?   The typical answers were 1) It’s too complex; 2)It’s too expensive; 3) We do not have management buy-in; and 4) We need to define our requirements first.

So we leave you with a question:

What’s the biggest hurdle to Business Analytics  in your organization?

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