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Mistakes to Avoid When Selecting and Deploying BI Tools

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Buyer’s remorse is never a pleasant experience but is particularly damaging when the purchase is something as large and far-reaching as enterprise business intelligence and data analytics tools. Companies always aim to get the best return possible on their investment — and, in this case, that investment tends to be significant.

This makes it very important to know the common challenges associated with choosing and rolling out BI technology so your organization can ideally avoid these pitfalls altogether.

Here are some common mistakes for enterprises to avoid when selecting and deploying BI tools.

Mistake #1: Not Identifying and Understanding Your User Base

Achieving success in your data endeavors depends on ensuring the technology your organization picks out and implements is compatible with its user base.

One of the main consequences of misalignment in this area is low adoption throughout the enterprise — meaning users do not end up actually using the tools made available to them. Some barriers that can stand in the way of user adoption rates include inaccessible, overly complicated and/or slow tools. Furthermore, some tools are designed for teams made up of experienced data experts; others are designed to facilitate self-service BI for non-expert users.

As Inside Big Data notes, companies can spend millions of dollars on new BI software yet end up deriving little to no business value because users are not able or willing to regularly use the chosen tools. This is why it pays to carefully consider all the characteristics and needs of the user base before buying.

Mistake #2: Failing to Tie BI and Analytics to Business Objectives

Modern BI tools tend to have many bells and whistles — that is, impressive and advanced features that extend far beyond the capabilities of yesteryear’s legacy systems. This is generally a positive development for enterprises deploying them.

However, this doesn’t mean companies should buy tools solely based on their general capability, as one expert writes for CIO. Rather, companies need to consider how BI and analytics technology will tie into their larger business objectives and what specific performance goals users will be trying to meet with their BI reporting.

Mistake #3: Underestimating Governance and Security Demands

Whether your enterprise is planning to deploy a variety of complementary BI tools piecemeal style or one unified platform across its ecosystem, prioritizing data governance and data security is key. Look for a platform with data preparation tools that address and mitigate risks in these areas. For instance, it’s important to factor in how granularly administrators are able to control access authorization by teams/users. 

Mistake #4: Expecting Users to Adopt Tools Without Support

As we covered earlier, there’s a big difference between tools being made available and people actually using them. It’s a fallacy to expect users to adopt tools on their own without support in the form of training and data-supportive company culture. The cultural component of data largely has to do with how leaders throughout the company communicate about and utilize data in their own decision-making processes.

As important as it is to choose tools that suit the user base wherever they currently are on their data journeys, additional literacy training may be necessary to help everyone — even people who have not worked extensively with data before — understand the interface, work with the visualization models and interpret the results well.

When it comes to BI tools, maximizing your company’s return on investment depends on choosing technology suited to the needs and expectations of its user base, making governance and security over data a priority, connecting BI technology to clear business objectives and increasing adoption of chosen tools through cultural support and literacy training.

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