4 Data Analytics Mistakes Successful Retailers Avoid

Big Data is having its moment in the spotlight, thanks in part to digitization, the proliferation of IoT devices, the continued rise of social engagement sites and technological advancements that allow for extensive data capture. The retail sector alone has collected such an unprecedented amount of data that it’s been hailed as the “new data industry” by CIO magazine. When processed and analyzed, you can use this information to promote personalized customer engagement, increase employee productivity, identify new business opportunities, and improve operational efficiency.

However, despite the vast wealth of customer data available to businesses and the clear benefits of data analytics, the U.S. retail industry has only captured 30 to 40 percent of its projected value due to barriers such as a lack of analytical talent and siloed data within companies, according to a report from the McKinsey Global Institute. Far too often, businesses will invest in a retail data analytics solution without any clear idea of what their data strategy is or how they intend to act on the insight that solution provides, which ultimately makes it harder to understand how to utilize that data and can result in a net loss.

Don’t miss out on the incredible potential of Big Data analytics in the retail industry—take a look at these common, costly mistakes retailers make with data analytics to avoid making them yourself.

Mistake #1: Thinking a “one-size-fits-all” solution is the answer.

In an ideal world, you’d be able to use a single data 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. Therefore, it’s in your best interest to look for a toolset with the flexibility to accommodate this diversity of thought and action.

So, what would such a toolset entail? First, you’ll need a visualization service that makes data easily digestible by end users, business analysts and IT personnel alike. Next, implement an SSRS operational reporting service that enables you to create detailed, highly formattable reports that provide a clear view into every level of your business’s operations and every stage of the sales process. Finally, look into a toolset that operates on a self-service model that’s intuitive and easy-to-use for members of all levels of your organization. So long as these tool draw information from the same underlying data and a sufficient data governance model exists, it shouldn’t matter which tools you use—simply choose one that’s fit for purpose for your users and organization.


Mistake #2: Insufficient investment in workforce and lack of defined metrics.

According to Gary King, Weatherhead University Professor at Harvard University, 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 your employees don’t know what to do with it.

You’ve probably heard from a number of outlets about the skills shortage in the data science and analytics field. As recently as 2015, 40 percent of companies reported that they struggle to find and retain data analytics talent, according to a survey from MIT; that percentage has likely increased with the growing demand for data management and interpretation skills. Even if you are able to find a qualified data analyst, there are still the matters of defining key metrics and training employees.

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. Once you’ve defined these metrics, the next step is to make sure your employees have the requisite knowledge and tools to execute them. Be sure to invest in high quality, comprehensive employee training, specifically training that covers the role of data analytics in the retail industry. Also, 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.


Mistake #3: Creating an analytics culture from the bottom-up rather than the top-down.

When it comes to developing their company’s data analytics culture, many retailers 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 your 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.

A top-down approach to retail data analytics culture is preferable because it prioritizes business initiatives, uses Big Data to optimize operations to better serve those initiatives and forces team to work collaboratively to achieve a broader goal, breaking down data siloes in the process. This approach also saves time, money, and effort because it doesn’t require the purchase of extraneous tools and systems and because it enables employees to focus their efforts on achieving the goals you set. With the right solution, top-down analytics can even provide a broad view of the market, enabling analysts to identify patterns and make data-driven decisions to propel business growth.


Mistake #4: 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 often hesitant to share their data and resources, often at the expense of identifying valuable business opportunities.

If you’re experiencing issues with data silos, you’re in good company: Although 73 percent of companies consider themselves analytically-driven, only 38 percent share insights outside of their department, according to a report from Dun & Bradstreet. And once they become entrenched in company culture, silos are almost impossible to eradicate—that said, there are still measures you can take as a retailer to mitigate the damage done by silos.

Start by creating a consistent interdepartmental methodology for handling data. Rather than separate members of your data analytics team into separate silos based on who collects data, who processes data, and who uses data to make data-driven decisions, turn it into a collaborative process. Most importantly, make sure to use an operational solution that offers seamless integration not only across departments, but also across systems to promote data and resource sharing, collaboration, and transparency.

The foundation of any foolproof retail data analytics strategy is an analytics-enabled business solution—and not just any solution, but one that offers ongoing support, vertical integration, and quality insight into how to keep customers engaged and how to streamline back-end operations. Contact the team at Hitachi Solutions today to find out how our comprehensive, integrated software suite can help you use data analytics to your advantage.

Free Guide
Data Centric Company

The solution is to create and nurture a data centric company. This 14-page eBook will explain how your organization can harness the power of data by defining and following through on sound data-centric practices.