Watch Out! 7 Data Analytics Pitfalls

Starting up a data analytics initiative is a great first step, but success is not a foregone conclusion. Some companies assume that just by embracing the idea of “data” – and collecting as much as possible – they’re going to revolutionize their business. These businesses soon discover that dealing with data is difficult, and turning it into value is even harder.

Analytics may not be easy, but it’s not impossible either. The good news for companies that are new to leveraging data is that many other companies have blazed a trail before you. And their experiences offers an opportunity to learn from their mistakes and avoid them on your own journey.

With that in mind, watch out for these highly-common but entirely preventable data analytics pitfalls:

● Operating Without a Strategy – Wanting to “leverage data” is not enough. Companies must create a data strategy that guides how they use data, and for what purposes. Having a guide to follow and benchmarks to strive for ensures that data is always living up to its full potential.

● Misunderstanding the Value of Data – A specific piece of data is valuable for reasons both obvious and unknown. If you assume you know where, when, and how to use certain data, you often overlook it’s potential in other areas. Saving as much data as possible and then diving into from different angles ensures that valuable insights aren’t being ignored.

● Emulating the Obvious Peers – Examining how other companies have adopted analytics is a smart move, but companies often have a limited focus. They look only at direct competitors in their own industry and ignore the innovations coming out of other industries. Be willing to follow good ideas and best practices no matter where they originate from.

● Focusing Only on Internal Data – The data you own is highly valuable and totally proprietary. But external data available through research reports, government studies, or online sources is valuable too. Combining internal and external data gives companies the broadest and most detailed perspective possible. Working this data into detailed data visualizations makes the insights immediately apparent and actionable.

● Failing to Audit Data – Years of bad data management have left most companies with data silos and wildly disconnected information sources. Integrating this information is a prerequisite of any analytics effort, but it’s easy for data to fall through the cracks. Conducting a comprehensive data audit reveals what is present and what is missing.

● Working Without a Leader – Analytics is an enterprise-wide effort, but it requires someone to lead and oversee the initiative. Without a dedicated data steward it’s easy for bad habits, disorganized inputs, and mixed messages to make data hopelessly confusing. Assign someone early in the process, and give them both authority and accountability over the project.

● Look to Tomorrow – Analytics is an ongoing effort that must constantly evolve. What works today’s may not work tomorrow. And by the time tomorrow arrives, there will be new priorities, data, and technologies to consider. For all those reasons, analytics must constantly be adapting and improving.

Even with a roadmap it’s hard to achieve all your goals with analytics. How you plan and prepare is important, but success or failure is largely determined by the technology you use. Implement solutions that prioritize intuitive features, accessible controls, and flexible options. Those qualities put the user first. And when users are eager and engaged to use analytics, companies tend to get the most out

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