BI implementations fail because they are sold to the IT departments and not to the business users. The use case and ROI needs to be built with the business users. If that is not done, it results in:

• high probability of self-ware
• lack of ROI for the business user
• a pure IT project not driven by the needs of the business

Define ROI, and then what? Cancel the project if it is not there? And with it your own job? Never!

One of my favorite questions from the audience was not a surprising one: a woman asked how they had measured the ROI of business intelligence projects. Unlike many of the other questions, the panelists did not have an easy or straightforward answer. A couple of the panelists did not think ROI could be measured quantitatively. The consultant on the panel had a more solid answer, but still acknowledged the challenge compared to other investments.

It seemed a little ironic to me that BI, which at its core is about measuring and managing performance, can’t be measured and managed itself?

The Importance of Time To Value And ROI

With the economic volatility and investment decision and justification process that many organizations have in place today, BI practitioners are strongly focused on the time to value dimension of ROI. They need to not only realize a positive return, but also show that return quickly.

ROI = Financial Business Benefits – TCO

Let’s assume the literal definition of ROI is “the financial results (impact, sales, revenue) generated by an investment of time, money and resources into a specific set of actions.”

It is easier for us to measure “activities” such as number of Reports, cubes developed, number of BI users, number of hours saved in delivering reports, BI tool usage; etc.

And while, value can be shown easily using a product which can consume and spit out dashboards as easily as making scrambled eggs in the morning, one has to wonder how much value it provides over time when the data to support such dashboards often still requires much manual intervention, ie. acquisition from source systems, cleansing, transformation and loading into a consumable format. Where’s the ROI in that? Most systems boast on the time savings achieved with implementation when calculating a BI system’s ROI.

Measuring ROI starts back with your overall strategy: Why are you doing what you are doing? Who are you trying to influence and what do you want them to do?

Goals of the BI project / initiative have to be defined prior to start, based on which the ROI can than be measured and calculated.

Some ROI determining factors include:

• Cost savings – as mentioned above and may include hardware/software, licensing, maintenance fees, etc.
• Time savings – employee production and better decision making
• Process automation – includes the time saved on report/analytics creation

These are quantifiable ways of identifying the value of BI and the ROI attained through BI adoption. However, other aspects such as enhanced productivity, information visibility, and increased profitability are more difficult to ascertain.  For instance, marketers are constantly trying to identify the success of their marketing campaigns.  This may include defining the correlation between individual campaigns and lead generation or online marketing activities or advertisements and an increase in sales.  Unfortunately, it is not always easy to identify where opportunities are generated and the correlations that exist between marketing initiatives and sales. 

The same difficulty exists in identifying ROI for BI.  The quantitative aspects may be easy to define, but the qualitative factors that make BI beneficial to organizations is said to be elusive by many. Even ROI calculations developed by industry experts and solution providers tend to lack the evidence to prove BI’s value conclusively beyond time savings and general process automation.

ROI

• Time-to-Value
• TCO
• Tangible Benefits

Time to Value

• Average Implementation Days
• Reduction in time to access, generate and analyze information

TCO

• Decrease in existing BI Overhead
• Decrease in Reporting Overhead

Tangible Benefits

• Increase in Revenue
• Decrease in Operating Costs
• Increase in Cash Flow
• Increase in Employee Productivity
• Increase in Customer Satisfaction
• Improvement in Business Agility

Better ROI leads BI Initiative Success and Satisfaction.

To add another anecdote of evidence: “Businesses want the Intelligence, not the BI”

This means BI has almost become a pejorative term outside of IT departments. It is coming to mean big, slow, and unsatisfying to the business needs.

A big part of the problem is just that all this BI stuff just costs too much both in money and effort/skills, therefore companies centralize it, therefore almost by definition it is too remote from the business endpoints that really need it.

This results in a lot of frustration with BI technologies, as far too many BI efforts have struggled with the business-knowledge gap where the IT/BI people aren’t close enough to the business problems to add value there directly, and the business side people aren’t sophisticated enough about data and the BI tools to be self-service.

All this points to SaaS BI, but including creation of a real business problem solution, not just deploying a tool in a cheaper way.

“Without the Business in Business Intelligence, BI is dead!”

Conclusion

In today’s volatile economy, organizations have been increasingly focused on tactical, quick-win technology projects at the expense of large strategic projects. The impact of technology projects on the top and bottom line of the organization is increasingly under scrutiny. The purpose of ROI is to provide a financial metric with which to measure such projects and provide a like-for-like comparison between initiatives that may have very different justifications and business benefits.

Alternative Metric: # of Click on the Dashparts - Instead of time spent, try looking at the dashboard that an average user uses.  The more clicks they do on dashpart, the deeper their level of engagement.

Some Other points to be considered:

• Uncertainties about how to get started – Again, in part to the pseudo-mystique surrounding the world of “business intelligence,” many executives and managers do not feel that they “have what it takes” to get started benefiting from understanding their customers and marketplace better by leveraging the data they have been collecting in their ERP systems for years. There are simple ways to get started and one can always make the leap to more sophisticated business intelligence applications when conditions warrant.
• Don’t Make Statements. Ask Questions: Practice of asking questions FIRST and making statements later on ROI for BI projects is a best practice. For e.g. “What value did you get with that  information?”
• “We have a problem with ROI” Tell the truth, even if it hurts. Your customers will find out sooner or later, make it sooner rather than later. Be honest and avoid corporate spin. Most “good customers” can handle truth.
• BI project ROI is a ongoing and evolving process
• ROI calculations are difficult. Are they always necessary? We should start worrying about ‘ROI’ and just start BI projects. What’s the ROI of putting pants on in the morning?
• “BI project is like a crystal gazing”, you never know what you’re gonna get. Priotizing BI requests based on ROI is the key to ensure maximize your potential value and provide the best return on investment
• “What we’ve got here is ROI to communicate. Lack of clear communication is the greatest crime against customers a BI project leader can commit. Communication is about dialogue, not the old fashioned IT monologue that did not allow for customer feedback.
• It’s better to spread that investment over a broader number of users, raising the ROI for each user. Focusing only on analytical users is expensive and wasteful. BI initiatives fail because organizations make large investments to equip a small number of back office analysts with BI capabilities

Posted on July 19th, 2010 by Sanjay Mehta
Filed under: Business Intelligence, CIO Community, View Points & Perspective

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