We love data. But we also know too much data can be a bad thing.  Here are three ways we’ve seen retailers using data to their detriment.

And some ideas on how to avoid these pitfalls.

1. Analyzing anecdotes

One data challenge we've seen is people manually reviewing too much data anecdotally.

Examining sales volumes for the last time you ran that promotion is valuable. But a single person can only consider a few data points at a time. And that data is open to interpretation, which is often biased.

At one retailer, team members in three functional areas, were each reviewing historical anecdotes, estimating the impact of the same up-coming promotions, and arriving at different conclusions.

Analyzing anecdotes uses valuable time. And bias can lead you in the wrong direction.

Strong analytics will curb reliance on anecdotes.

Your teams will always want to see sales history. Making it efficiently available brings a lot of value. To deliver even more value, provide demand insights based on proven modeling techniques. These will steer teams to better informed, less biased decisions. This investment in data analysis, paired with an explanation of insights will also curb reliance on so much historical data.

2. Divining too many details

Want to know whether a discount with a recipe sent to Jasmine’s mobile account will inspire her to buy that lemon-infused Tunisian olive oil when she walks into your Kansas City store the second week in July - if July 4th falls on a Wednesday – and it’s raining, but just a little bit?

While many systems can mechanically model customer demand at very, very specific levels of detail, taking into account nearly unlimited factors, often the data cannot support reliable results.

Systems with erratic recommendations are burdensome to manage. And these systems don’t get used, leading to less effective decision making.

A balanced approach will help ensure rich and reliable results.

Well-equipped implementers, data analysts and solution providers can balance detailed insights and dependable results in a variety of ways.

However, when one retailer reached out for help with their forecast challenges, they painfully admitted - “Our last implementor was very good – they did everything exactly as we asked them.”

Pushing for very specific approaches to demand modeling may pressure your implementor or data teams to make you happy in the short term. Instead, being clear about where you are looking for value, how you will leverage the forecast, and your bandwidth to manage exceptions will help produce detail-rich forecasts in which you and your teams can have confidence.

3. Bigger is better syndrome

With so much data available to retailers today, some can become eager to leverage more of it than is necessary to address today’s most pressing opportunities. And that data can be difficult and time-consuming to access.

Data-intensive analysis can leave other important, more easily addressed challenges unattended. Valuable insights are left unmined and unseen. Out of tune systems continue to misbehave, getting you into who knows what kind of trouble.

The right data for the job will help give you higher returns on your effort.

Petabytes of data and shiny, new tools can be enchanting – and in many cases, are worthwhile. But the most powerful and valuable approaches to leveraging data in retail are not all new. Being open to a variety of data sources and solutions can help you get the most value for your investment.

Can you think of other ways too much data can stop progress or bring harm to your business?


A little bit about Cognira

We’ve helped retailers get value out of big data and little data. Using the most modern cognitive and machine learning tools. And time-proven, robust statistical techniques.

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