Is Big Data Paralyzing Your Business? Avoid These Three Common Traps (Part 3) | Riskonnect

This blog post is the final blog in a series of three about how big data can paralyze organizations from taking action. It was written by Brent Dykes, Director, Data Strategy at DOMO, a Riskonnect partner. This series was originally published as one long article on Forbes.com.

The three common traps individuals encounter when dealing with big data include:

  1. Data Distrust
  2. Data Daze
  3. Analysis Paralysis

The first blog post in this series covered data distrust, while the second blog post in this series covered data data daze. Now, let’s take a look at analysis paralysis.

Analysis Paralysis

When people think of data impeding progress, they often think of paralysis by analysis. Typically, analysis paralysis occurs when someone over-analyzes or overthinks their data and fails to arrive at a key insight, decision, or outcome. It often plagues analysts, data scientists, and even analytical leaders who keep drilling deeper into the numbers but get lost in the process and fail to extract anything valuable. Sometimes, no matter how much we poke and probe the numbers, they may not reveal anything insightful or actionable.

For example, an analyst was analyzing an email marketing program and was struggling to find something substantial in the data. While he wanted to provide a slam-dunk insight, his main stakeholder—a data-savvy email marketer—had done a good job of optimizing the email program over the past several years. Rather than continuing his analysis, the analyst decided to review his intermediate findings with the stakeholder to see if it would trigger other perspectives or questions. In the end, the partial findings ended up forming 90% of the final deliverable and were sufficient to answer most of her business questions even though it didn’t result in any earth-shattering insights or recommendations.

Part of being skilled in data analysis is knowing when to keep digging and when to move on. Recently, I had the opportunity to interview executives across various departments at a few different organizations. On a couple of occasions, an executive mentioned how his or her team was struggling with “analysis paralysis.” While their teams may have been immobilized by data, the paralysis they were experiencing was clearly occurring BEFORE the teams even attempted to examine or explore their data in depth. It’s more likely that they were suffering from the effects of data daze than analysis paralysis.

While the phrase “analysis paralysis” is fairly well-known, the malady isn’t as common outside of analytical roles. Most companies haven’t yet reached a data maturity level where a significant number of business users are routinely performing analysis on their own. However, as companies embrace self-service analytics and data skills evolve, this form of data paralysis will occur more frequently as the opportunity to explore data becomes more decentralized and democratized.

How to overcome Analysis Paralysis:

  • Set a time limit and carefully monitor your progress
  • Step away from the problem and try again with a fresh perspective
  • Share your analysis approach with someone else to gain a different viewpoint
  • Revisit the original business question(s) to refocus your effort
  • Go back to basics and try a simpler analysis approach

Becoming a more data-driven company is a journey—not a destination. Ultimately, companies need all of their leaders and employees to join them in that journey, but unfortunately some people will get lost along the way. Some individuals will lose faith in the data. Others will be overwhelmed by the volume of information being shared. Yet others may dive into the data looking for insights and never re-surface. Mitigating and overcoming these paralyzing traps is important to driving action and value with your data. The more people who can participate in this data journey and remain on the path, the better it will be for your company’s long-term success with data.

**This article was originally published on Forbes.com on September 28, 2016  

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