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9 ways you’re failing at business intelligence

Bruce Harpham | Nov. 22, 2017
Solid business intelligence is essential to making strategic business decisions, but for many organizations, BI efforts are derailed by poor data practices, tactical mistakes and more.

Executives know they need high quality data in order to make sound business decisions. But getting accurate data in a timely, user-friendly format remains a challenge. Sure, there is a vast industry of consultants and vendors with business intelligence (BI) expertise.

How do you know if you are being “led down the garden path”? Is it time for an upgrade to your BI or to launch a new training program? To answer these questions, knowing where others have made mistakes is helpful.

1. Being an ‘order taker’ when building BI systems

“The customer is always right.” It’s a noble sentiment that has done much to improve customer service, especially in retail. But with technology, business users may not always understand what they are asking for. Even worse, they may try to dictate the technical details of the solution.

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Implementing what users ask for instead of what they need is a recipe for BI failure. “Successful BI projects require elaborating and managing requirements, as well as the ability to properly validate BI results,” says Wolfgang Platz, founder of Tricentis, which offers a continuous testing platform to companies such as HBO, Toyota and BMW. The “five whys” technique — asking why five times about a single issue to attain greater depth — is one way to understand what users truly need.

2. Cutting testing time and resources

“Move fast and break things” is a key idea in the startup world. Established businesses, too, often have a need for speed. But in that quest to go faster, activities perceived as ancillary can suffer, like testing. Viewing testing as deadweight can lead to significant quality issues, especially if you rely on manual testing. Instead, look to testing and related “ancillary” processes as ways to deliver a higher-quality BI experience.

“Restricting testing, especially the only testing being done is manual, leads to a high number of defects in user acceptance testing that ultimately affect delivery times,” Platz says.

3. Short-sighting broader data integrity matters

Business intelligence tools are excellent at processing, displaying and analyzing data. But what if you are feeding corrupt data in the system? Or better yet: How would you demonstrate to an IT auditor that you have high-quality data guiding your management decisions? Focusing too narrowly on the BI tool and its configuration may mean you will miss these critical details.

“Today, BI isn’t being used only to support better decisions; BI is often embedded into operational processes. If you have errors in your financial or regulatory reporting — which are often supported by data warehouse technologies — BI can help bring those to light. But other processes can still fail. For instance, an insurance company with broker fees that are calculated even just slightly wrong can negatively impact your reputation and then increase customer churn,” says Platz. “Today’s businesses need to have a proactive, automated approach to BI testing to expose data integrity issues as quickly as possible.”

 

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