When it comes to commentary with valuable real-world insights, I can always count on the participants at my weekly #BIWisdom tweetchats on Fridays. I kicked off a recent discussion with this question to the group: “What are the top five worst practices in business intelligence?”
It took only a few minutes for them to toss out a lot more than five. As I commented then, there are a bunch of successful overachievers who participate in #BIWisdom tweetchats!
I certainly don’t want to minimize the great successes organizations are having with business intelligence. But it’s a fact that some BI initiatives sputter. So let’s look at why a BI initiative sometimes doesn’t fully deliver on its promise. Failures, after all, are very instructive.
So here’s the list we compiled —
Some of the worst mistakes organizations make in BI initiatives
Some of the worst mistakes organizations make in BI initiatives
Technology/tools:
" Thinking the BI toolset will make up for not understanding the business
" Thinking BI tools will solve the business problems instead of using BI to solve the problems
" Generalizing solutions or tools for all types of users – BI is not a one-size-fits-all type of solution and many “tend to implement bright shiny objects with no real understanding of whether or not it’s a good fit with their organization”
" Thinking the BI toolset will make up for not understanding the business
" Thinking BI tools will solve the business problems instead of using BI to solve the problems
" Generalizing solutions or tools for all types of users – BI is not a one-size-fits-all type of solution and many “tend to implement bright shiny objects with no real understanding of whether or not it’s a good fit with their organization”
Data:
" Thinking that data quality is a technical problem
" Thinking that data quality is not everyone’s concern
" Assuming some nice-looking charts from bad underlying data is actually good BI
" Believing the same visualization will work across different datasets
" Assuming that all the data is not relevant and some should be excluded
" Thinking that data quality is a technical problem
" Thinking that data quality is not everyone’s concern
" Assuming some nice-looking charts from bad underlying data is actually good BI
" Believing the same visualization will work across different datasets
" Assuming that all the data is not relevant and some should be excluded
Insights:
" Having a mindset to shoot the messenger who delivers unanticipated insights
" Being afraid to share BI insights with customers and suppliers; this comment was followed by a tweet that “sharing the insights is a good way to cement ties in the value chain and it’s good business”
" Internal or external billing for every small change to a report, analysis, etc. – “it kills what analytics is about”
" Undertaking projects that depend on looking at the existing reports and recreating in BI with no change
Training:
" Knowing how important training is but still running out of funding for it
" Believing a sales rep who says you don’t need much training – “remember, they make more from license sales”
" Having a mindset to shoot the messenger who delivers unanticipated insights
" Being afraid to share BI insights with customers and suppliers; this comment was followed by a tweet that “sharing the insights is a good way to cement ties in the value chain and it’s good business”
" Internal or external billing for every small change to a report, analysis, etc. – “it kills what analytics is about”
" Undertaking projects that depend on looking at the existing reports and recreating in BI with no change
Training:
" Knowing how important training is but still running out of funding for it
" Believing a sales rep who says you don’t need much training – “remember, they make more from license sales”
Implementation/outset:
" Implementing BI technology without use cases
" Being unwilling to disrupt existing processes to gain the BI success
" Not resolving misalignment between IT and business users – “this results in fighting over scheduling priorities and diminished resources”
" Asking questions primarily in retrospect – “it’s much easier if questions come first”
" Not owning the biz problem – “an example: it’s in the data warehouse, so it’s not my job”
" Focusing solutions exclusively upon executives; but a tribe member tweeted that we can attribute this to a sales tactic in earlier days when it was the only way vendors could sell outside of IT since the executive team had the money to buy
" Implementing BI technology without use cases
" Being unwilling to disrupt existing processes to gain the BI success
" Not resolving misalignment between IT and business users – “this results in fighting over scheduling priorities and diminished resources”
" Asking questions primarily in retrospect – “it’s much easier if questions come first”
" Not owning the biz problem – “an example: it’s in the data warehouse, so it’s not my job”
" Focusing solutions exclusively upon executives; but a tribe member tweeted that we can attribute this to a sales tactic in earlier days when it was the only way vendors could sell outside of IT since the executive team had the money to buy
Those are the frontrunners among the culprits that erode the achievable value in BI initiatives.
One of the #BIWisdom participants pointed out that many of these issues have the same root cause: lack of trust – either trusting the business users, IT or the BI “experts.” A lack of understanding about technology can breed distrust. And good communications between all involved can reduce misunderstanding up front.
The area of training I agree – recurrent training is essential for success. One of the participants tweeted that schools have finally caught on and are teaching for data enthusiasts. As she observed, these days, “everybody is a data generator and consumer. Computing and analysis are no longer synonymous with IT; they are a common way of life with everyone.” Millennials are changing the way we consume and report data, so a generational change is starting to make a difference regarding the importance of training.
Bottom line: It’s that time of year when the Internet is flooded with articles and blog posts of predictions for the upcoming year. As I often tell journalists and inquirers, I don’t have a crystal ball and don’t make predictions. But I’ll make an exception now – I predict that we’ll see even more success in BI initiatives in 2015 if organizations eliminate these “gotchas” from their practice.
Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweet chat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.
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