4 Mistakes to Avoid When Implementing Business Intelligence

4 Mistakes to Avoid When Implementing Business Intelligence

Implementing a data strategy – including data analytics and business intelligence (BI) – is critical to gain a competitive edge in today’s business world, regardless of industry. But it is fraught with difficulty and expensive mistakes.

According to Gartner, 80% of BI projects do not deliver the desired return on a business’ investment. But there are ways to ensure success – read our guide on what to look for in a business intelligence solution.

If you are at the beginning of your BI initiative, or are considering taking the plunge, here are the top 4 business intelligence mistakes to avoid.

Mistake One. Assuming Analytics is just for IT Departments

Data analysis is achieved through the implementation of technology, but the algorithms all by themselves are not going to get you to your objectives. You have to embrace the concept of digital transformation and think of your data project as a business-wide initiative, not simply a task for your IT team.

Your IT team is naturally going to do the technological heavy lifting. But the project has a much greater chance of achieving your goals if you bring more people to the table and attain data literacy levels across business users.

View the data project as just one element of a much larger business plan, identify a use case that propels your overall strategy, and put together a strong interdisciplinary team.

Mistake Two. Not Accounting for Scalability

There is nothing wrong with starting small when it comes to your data project; in fact, it is almost always the best approach. But always consider scalability because not being able to scale a solution and get users on board will become a long-term and expensive problem.

To maximize the success of your data initiative, use a data tool with functionality appropriate for every user’s need and skill level. It should be just as easy for the sales team to access and use the data that they need as it is for the IT department to access the parts that they need to use.

Mistake Three. Relying on Poor Quality Data

No one sets out to purposely use poor data, obviously. But when you first start a BI project, the right data to use (and what to do with it) is not always immediately clear, leading to one of the most common business intelligence mistakes.

Take the big-picture view of your organisation’s current status and future goals, identify the data you’re going to use (or start collecting it), and enact a strong governance method. Read our guide to data governance and cleansing here.

This will get your project off to the right start and ensure reliable results as you continue to explore analytics.

Mistake Four. Working in Data Silos

Data analytics can and should be self-service. When your entire organisation can leverage your BI tool and make data-driven decisions, everybody wins.

But all too often, siloed teams prepare their own data and create their own reports, shared only with each other.

The issue is magnified if several data tools are in use. Everyone’s sourcing from their chosen data pools, preparing data their way, inadvertently letting errors slip in, and using different calculations.

The unfortunate result is that people make decisions based on poor or incomplete data, totally missing the point of the entire initiative. There is no way to ensure the data is being cleansed consistently, and no way to make truly valuable connections among insights.

Ensure that your data strategy is founded on one properly governed and well-organized platform, with data that reflects the single, real version of the truth.

Business Intelligence Mistakes: Conclusion

Yes, data transformation can be challenging for organisations, and the endless pace of technology can make things even more complicated. But BI has been around long enough to establish the best practices and strategic approaches that provide real returns and eliminate the most common business intelligence mistakes.

Having this big-picture overview of common pitfalls is a good place to start. From here, you can begin considering your goals, getting your team together, and deciding which internal and external data is needed.

Ultimately, think everything through carefully, knowing that BI tools achieve results only when implemented correctly. Steering clear of these common business intelligence mistakes means you are already ahead of the competition.

And if you would like a little help avoiding business intelligence mistakes and getting your data project off on the right foot, talk to our data experts today to see what we can do with your data.

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