Big Data retail analytics: 6 Mistakes To Avoid

1. Investing in a tech-only solution

Much has been made of how emerging technology is driving progress in the retail industry. Too many retailers wrongly assume that data analytics technology alone will solve all of their problems and lose sight of their people and processes as a result. While you should certainly consider investing in retail data analytics technology to better optimize business operations and maintain relevance, you should avoid going all-in on tech at the expense of the human element.

2. Thinking a “one-size-fits-all” solution is the answer

In an ideal world, you’d be able to use a single retail analytics solution across your entire organization. In reality, however, this “one-size-fits-all” approach is highly impractical. Simply put, retail businesses aren’t homogeneous; they consist of hundreds, even thousands, of business users, employees and other people, all of whom think and behave differently. It’s in your best interest to look for a toolset with the flexibility to accommodate this diversity of thought and action.

3. Insufficient investment in workforce and workforce development

It isn’t the sheer volume of data available to businesses that’s revolutionary, but rather that businesses are able to do something with that data. However, that means very little if you can’t interpret that data or you lack employees that know what to do with it

4. Lack of defined metrics

It’s important to define key metrics because it gives employees a set of achievable, quantifiable goals to work towards and prevents your workforce from being spread too thin. When shopping for a retail data analytics solution, consider purchasing one that keeps employee engagement and productivity in mind and empowers them with the tools they need to do their jobs well

5. Creating an analytics culture from the bottom-up

When it comes to developing their company’s retail data analytics culture, many prefer to take a bottom-up approach because it uses data as a jumping off point and creates a more accurate market forecast on which to base their business strategy. There’s just one glaring issue with this approach: It forces you to tailor your business strategy to fit the data, which could run contrary to your company’s needs.

6. Creating data and analytics silos

Silos are one of the biggest obstacles to taking full advantage of data analytics in the retail industry. Silos arise organically out of divisions between organizational departments; different departments have different objectives and are sometimes hesitant to share their data and resources, often at the expense of identifying valuable business opportunities.

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David BECK

David BECK

David is a former entrepreneur — Teacher — Researcher — Contributor to government publications.