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11 Reasons Why Most Business Intelligence Projects Fail

Mico Yuk discusses what companies can do

5May

Like any powerful tool, business intelligence projects must be managed properly to realize lucrative results. The fact that you're equipped with extra knowledge doesn't automatically guarantee that you'll be successful in its application, so it's critical to avoid common pitfalls. Here are some of the top factors that might cause your analytics endeavors to fall short.

1. Lack of Executive Support

No complex project succeeds without consistent guidance. Sadly, many companies seem to forget this golden rule when it comes to business analytics, and their projects either outright fail or go off in the wrong directions.

Impactful business intelligence revolves around relevant metrics. Booz & Company insiders suggest that CIOs ought to play integral roles in selecting, implementing and managing project metrics as well as the software and other tools used to track them.

2. Old Technology

Technologies like SAP and Oracle were once at the forefront of business intelligence. While these platforms still have applications in provisioning, ERP and CRM, their roots lie in decades-old business models and technological standards that clearly weren't designed with the cloud in mind.

Workflows are changing, and BI technology needs to keep up. Build your projects around tools that incorporate data visualization, mobile accessibility, and drag-and-drop dashboards right out of the box, as almost all of today's standalone BI products do.

3. Lack of Business Support

One Gartner global manager noted that a lack of business support and training was a common culprit in the failure of BI projects. No matter how much information you create, failing to communicate with team members and enact actionable response mechanisms will render your data ineffectual.

Business intelligence undertakings aren't just pet projects for managers. They ought to represent company-wide efforts to make changes, so make sure everyone is on board from the beginning, and implement training that helps workers play their part by tracking relevant data.

4. Too Many KPIs

Key performance indicators, or KPIs, are essential for tracking your successes and failures, but is there such a thing as too much performance measurement? Experts at Business Finance seem to think so, citing overuse of performance indicators as the number one mistake in electronic dashboard and scorecard implementations.

Complex KPIs require more maintenance and analysis, and they can make what should be routine business intelligence tasks into extremely unwieldy labors. If you absolutely must track an army of indicators, narrow them down over time to reduce your workload and minimize data confusion. Focus on KPIs that deliver the most unique information with the greatest accuracy and precision.

5. No Methodology for Gathering Requirements

Where does your data come from, and how do its origins impact its validity? Different acquisition methods can change the way business intelligence looks, and they also impact how it represents vital information.

Before choosing an acquisition methodology, learn what makes it tick. Whether you delve into the intricacies of data-mining algorithms or go some other route, broadening your knowledge increases your chances of project success.

6. Overly Long Project Timeframes

Unrealistic deadlines can cripple an otherwise promising project. With business analytics, information is available instantly, and it may lose relevance the longer you sit on it. While smart analytics commonly derive insights from old data, it's important to define a cutoff point.

Choose project timeframes that suit the cycles or processes they track. If the classic quarterly financial calendar has absolutely no bearing on your week-to-week business model, ditch it in favor of something that generates conclusions more rapidly.

7. Bad User Experience

The user experience, or UX, is critical to data visualization. According to Wired, confusing or poorly designed dashboards and infographics simply confuse things, making it harder for you to utilize information.

Simplify your UX to make it more accessible. Whether this means reducing the number of KPIs your dashboard tracks or switching software entirely, clarity is vital to informed decision making.

8. Lack of User Adoption

Does your Big Data collection rely on users to generate info? If so, you might be leaving yourself in the dark. Cindi Howson of BI Scorecard notes that BI adoption among employees is only around 22%.

BI adoption may be more enforceable if you simplify the process. Deploying mobile tools that are easier to use has been shown to increase adoption rates.

9. Bad Data

Low data hygiene is one of the biggest sources of business intelligence woe. Analytics that fail to expunge outliers and noise guide your decisions in the wrong direction, so always take the time to sanitize, especially if you're striving to conquer omnichannel retail.

Choose data visualization and gathering tools that include sanitization and filtering functions, and ensure these features are customizable. As you learn more about what constitutes good data, you'll be able to further modify and refine how you respond to change.

Discover more about crafting successful BI projects by checking out our other blog articles, or share any pitfalls we missed in the comments below. For more help with your specific projects, contact us for expert assistance.

10. Lack of Proper Human Resources

Gathering data isn't all there is to BI. Modern entities that lack the staff to draw conclusions and implement changes based on the information they gather find it impossible to learn from their analytic activities.

According to CIO, data scientists play vital roles in interpretive analytics. Helping these individuals shift to data steward roles can ensure that their analysis efforts actually push strategic decision making forward.

11. No Upfront Definition of True ROI

Big data projects commonly promise amazing bounties, but profitability depends on your accounting methods. It's vital to remember that you're not just paying for software and other tools; you're also investing time, training and potential relationships with your consumer base.

Get an accurate picture of your BI ROI by defining your profitability metrics before you're forced to rely on them. With the ability to choose from multiple methods of calculating and improving ROI using tools like Big Data visualization, it's easy to understand the importance of setting your standards in advance.

You can find out how to apply Mico's methods to your own organization with her online course, which is open for enrollment from the 23rd of May. Register here today.

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