Top 5 Business Intelligence Mistakes (And How to Avoid Them)


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Wednesday, December 28, 2016

Business intelligence (BI) and analytics will be top of the agenda for chief information officers (CIOs).

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Top 5 Business Intelligence Mistakes (And How to A
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That’s the view of US-based IT analytics experts, Gartner, who say that the BI market is being propelled by remarkable innovations which are, in turn, contributing to a major shift to modern analytics.

In practice, corporations are embracing cloud BI services more than ever before. Similarly increasing in popularity is the use of conventional BI software, and any number of new apps and social BI.

However, if BI solutions are on the increase, so is the technology’s misuse and the likelihood that companies trying to harness its power will simply use the technology incorrectly.

Read on to find out about five of the most common BI mistakes, and how to avoid them.

Poor problem diagnosis

A key point for all organizations is to not rush into leveraging BI tools, unless a clear business case exists for them. Jumping in too soon is a common mistake, as Scott Schlesinger of consulting, tech and outsourcing group, Capgemini points out.

If firms start out by not knowing precisely what they want to achieve, the chances of finding an effective solution to the actual problem rapidly decreases. This is important because ROI depends on a clear grasp on the business case so that big data can be used discriminately.

No silver bullet exists for companies seeking to address general capability. Instead, organizations should start with the business problem that needs to be solved and understand the specific capabilities needed to move forward - these details should then inform the purchase of BI tools.

Inadequate data quality

Correctly diagnosing your problem will depend on how authentically robust your data sources are, as information quality is the buttress to every successful intelligence venture.

According to InformationWeek research, 55% of CIOs cited data quality as the most significant barrier to successful implementation of BI solutions.

Gartner considers data quality to be a common stumbling block, leading BI applications to be founded upon data that is irrelevant, incomplete or questionable. Consequently, it is important that firms form a process or set of automated protocols to isolate incoming issues and prevent low-quality data from finding its way into the data warehouse or BI platform.

Too much, too soon

The best BI implementations are put together with care and over time, as underlined by Daniel J Ronesi, director at law business service, Aderant.

Crucially, implementation should not be rushed. A more measured approach will allow time for training, while giving users a chance to habituate themselves with the working skills needed to operate BI software proficiently.

The rush to get dashboards in place quickly may be exacerbated by low budgets, with managers not wanting to put their necks on the line by funding expensive BI tools. As such, many dashboards are delivered quickly, giving poor value as they are silo-specific, instead of being based on key corporate objectives.

Gartner recommends that IT organizations make reports as pictorial as possible to forestall demands for dashboards, while including dashboarding and more complex visualization tools in the BI adoption strategy.

Not finding a scalable solution

Sometimes firms discover a BI solution that addresses an immediate problem to the neglect of future developments. While effective in the short term, the industry landscape is always shifting, so it is important to remain dynamic and scalable.

Francois Ajenstat, director at Tableau Software, underlines how self-service analytics are becoming the norm at fast-moving companies that are on the cutting edge of their industry.

When speaking to vendors, software specialists, Olympic, recommend to ask about architecture and how scalable a solution is. Furthermore, bear the strength of mobile in mind; most C-level executives would much rather have information at their fingertips. As a result it is essential that the BI solution is able to cater to the mobile market.

Ultimately, you need a business intelligence solution that will be effective within the ecosystem of the firm and one that can grow with your business needs.

Failure to get buy-in from users

Firms can face problems when IT departments rush to purchase BI tools without considering buy-in demands from end users.

Assuming that employees will adapt smoothly and efficiently to newly acquired technologies, simply because the organization is standardizing on them, is a sure way to slow the entire process.

Instead, employees must be educated as to why the services are needed so that they grasp the value of the whole proposition.

Building on this point, Ray Major of Halo Business Intelligence says that firms can easily fail to appreciate just how difficult it is to steer an organization’s culture in a direction that sees BI tools being accommodated smoothly.

End-user buy-in is critical and demands real internal effort, with focus falling on the advantages of a BI system. If this is done successfully, employee goals and performance can be tied in with results driven by analytics and metrics.


As technology continues to advance, BI tools are becoming increasingly relevant to the growth of the corporate world. They keep companies up to speed with the data rapids and ultimately allow business leaders to stay on top of how to improve operations.

Anticipation of and participation with these trends is crucial so that new BI practices can be co-opted into our processes. On the other hand, companies that become complacent, drawing solely on reports set up during implementation, risk falling out of sync with their competitive environment. 

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