Big Data Paralyzing Your Business? Avoid These 3 Common Traps

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With the abundance of data flowing into organizations these days, the next challenge isn’t usually how to acquire more data but how to translate it into something meaningful—informed actions that lead to higher revenues, lower costs, increased productivity, greater customer loyalty and so on.

As the volume of data expands and the dependence on data increases, there’s a higher likelihood of companies being potentially handcuffed by data if they’re not disciplined and attentive.

In order to leap forward with data, companies need to ensure their people can adequately trust, consume and analyze it.

1. Data Distrust

One of the most pervasive forms of data-related paralysis is data distrust, which occurs when individuals question the quality of their data and refuse to rely on it for decision-making. In a recent KPMG study, 84% of CEOs indicated they’re concerned about the quality of the data they’re basing their decisions on. Data quality is clearly top of mind for most executives as they want to avoid making wrong decisions based on erroneous or incomplete data.

Once data distrust takes hold at an organization, it can significantly undermine all analytics efforts and remain a barrier until it is adequately addressed.

However, in many cases, flawed data can still be helpful and safe for directional guidance. Data can be precise yet inaccurate and still be insightful.

Too often a higher standard is placed on the data (perfection) than scrutinizing one’s own beliefs, opinions and assumptions. In the quest for perfect data, most companies will experience diminishing returns as they inch closer to a 100% match between two systems.

Rather than automatically assuming you have a data quality issue, you may have a change management issue instead.

How to overcome Data Distrust:

  • Evaluate whether data issues are isolated, widespread or imagined
  • Review the severity and frequency of the data discrepancies
  • Work with IT team to keep the data within an acceptable margin of error
  • Hold teams accountable for maintaining data quality and introduce processes to reduce errors
  • Win back confidence in the data by demonstrating its precision and usefulness

2. Data Daze

While data daze may be less familiar than analysis paralysis or data distrust, you’re likely to have already experienced it personally or seen other people hampered by it. As data becomes more and more pervasive, data daze occurs when individuals become intimidated or overwhelmed by the sheer volume of data placed in front of them.

How to overcome Data Daze:

  • Re-visit your KPIs and supporting metrics to ensure they align with your corporate strategy
  • Consolidate and curate your KPIs and supporting metrics to increase their relevance and potency
  • Organize sets of supporting metrics under KPIs hierarchically or thematically
  • Chunk data into more digestible layers or groupings that can be drilled into by business users
  • Strive to simplify and improve data visualizations and report/dashboard layouts
  • Train employees on how to properly visualize data, craft data stories and interpret data

3. 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.

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 efforts
  • Go back to basics and try a simpler analysis approach

Becoming a more data-driven coming is a journey-not a destination.  Ultimately, companies need all of their leaders and employees to join them in that journey.

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.

 Source:  forbes.com by Brent Dykes

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