5 tips to avoid analysis paralysis in business data analytics

Learn how to pull the right insights from your data, and avoid feeling stuck with your analyses.

Tags: Analysis
Published: November 07, 2018

We undoubtedly live in an era of big data, and it comes with significant advantages and risks for organizations. Even with all the data available to us, the need for valuable human-driven analysis remains vital. This presents a unique problem in the big data era: the risk of collecting too much data, and getting lost in the sea of figures and statistics.

Analysis paralysis is real, and it affects everyone from individual consumers to large corporate organizations. The hunger for data can be all-consuming, and without a clear vision for analyzing that data, you could be stuck in your analyses.

Thankfully, you can utilize some valuable tips and tricks to avoid analysis paralysis. Here are some insights from prominent data analytics experts on combatting this organizational affliction.

 

Embrace the human element

You cannot take the human element out of data analytics, even as artificial intelligence and machine learning hastily adopt processing techniques. Julian Birkinshaw, professor of strategy and entrepreneurship at London Business School, argues that business should embrace the human part.

“Recognize some decisions can be entirely fact-based (such as A/B testing on website design), but others need human judgment. The best are made with a combination of both […] In today’s fast-changing world, the twin drivers of business success are decisive action and emotional conviction.”

 

Keep it simple and focused

You won’t get very far by inundating yourself with spreadsheets and dashboards. More importantly, you don’t necessarily need to report and analyze everything that’s collected. Tabbish Hasan, director of performance improvement for Farber, notes that valuable insights require a focused analysis strategy.

“The solution to analysis paralysis is logical and process oriented. Instead of reporting across the full blizzard of possibilities that big data throws up, businesses should establish a basic analysis structure that focuses on a set of Key Performance Indicators (KPIs).”

 

Be realistic in your goals

Even if you could benefit from advanced data collection techniques, would they really fit in with your organization’s stated goals? Laks Srinivasan, co-COO of Opera Solutions, argues for a more realistic approach to data algorithms and problem solving.

“With so many different technologies and flavors of analytics available on the market, we can easily see how companies can over-invest or invest in the wrong solutions. Why purchase deep learning and neural net technologies — as cool as they are — when simple linear regression models will do?”

 

Accept the uncertainty

When you’re dealing with reams of user data, there are bound to be deviations and misrepresentations. But rather than endlessly searching for the perfect answer, you need to embrace a level of uncertainty with your analysis. Charles Carpenter, assistant director of analytics for the Chicago Cubs, notes that this can also give you a stopping point, allowing you to avoid getting lost in the data.

“In most business situations there’s no one right answer, but there are a variety of answers that meet the business objectives. You need to be comfortable with embracing that uncertainty and embracing that there may not be a perfect answer. I’ve also learned that explaining the analysis to stakeholders means I’ve got enough information to stop. If left unchecked, I would happily analyze data until there was nothing left to analyze.”

 

Gain agreement on the parameters

If your marketing and sales teams aren’t speaking the same data analysis language, the end result could bear little benefit for the organization. If all teams help set defined parameters for data collection, it will help tell the story better for leadership. David Berglund, senior vice president and artificial intelligence lead for U.S. Bank Innovation, notes that this relationship is vital for getting real results from your data.

“Data scientists can analyze a customer’s past behaviors with marketing offers, and the predictive engine can assess a score regarding how likely the customer would be to accept a new offer. This lead scoring process, which aims to simplify the identification of qualified leads, can help your sales teams focus on customers that are more accessible.”

 

If you’re suffering from an organizational case of analysis paralysis, these tips can help you hone your data analysis processes. Above all, consider the business necessity of the data you’re collecting. If they aren’t proving helpful for your objectives, it might be time to refocus your efforts.