Linda Whitaker
VP of Science Delivery
It’s no secret that advanced science technologies are becoming critical for business success (yep, even for retail). With the power of data science, retailers have the opportunity to enhance marketing strategies, operations, financial performance, and of course, stay ahead of the competition. However, despite all of these benefits, many retailers still struggle to operate as a data-driven organization. Linda Whitaker, VP of Science Delivery at Cognira, shares 3 reasons why retailers struggle to fully embrace data science into their business.
3 reasons why retailers struggle to fully embrace data science
1. Data science and analytics is not part of their culture
To successfully adopt big data and advanced science technologies, businesses need to have a data-driven culture. This means having sustained support from executives, laid out objectives with strategic goals, and a dedicated, multi-faceted team (Check out our blog on creating a data science team in retail here).
Today, many retailers struggle to make analytics part of their culture because they are just looking for quick data fixes, gatekeep data from employees out of fear of sharing sensitive information, and switch from system to system each time leadership is shuffled.
If you really want to excel in data science, it has to become part of your company DNA!
2. Retailers struggle with bad past experiences
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Solve the wrong problem
Data, science, technologies and business processes need to align. Solutions that do not adhere to operational constraints or account for downstream systems usage can be suboptimal at best, downright harmful at worst.
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Solve a problem in a way that users can’t support or accept
If the system is a black box, gives confusing results and/or throws many exceptions that users have to override, they will quickly lose confidence. The system must make life easier for users, not harder.
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Create a solution that does not achieve promised benefits
It can be hard to measure benefit, and this cannot be an afterthought. Good data science can get thrown out and bad can stay in without consistent and continual performance measurement.
3. Finding data science experts you can trust and rely on
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Communication can be difficult
Typically, there is no consistent language or definitions around analytics/data science/big data.
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Vendors are part of the problem
Vendors are anxious to boot out competition, not always forthright. Additionally, they typically do not speak retail and may not solve a problem as the business wants it solved.
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Data science experts are a scarce resource
Technology is a moving target, making technology and data science expertise hard to find, and even harder to retain. (To learn more about technology advancing, check out one of our blogs on the analytics evolution and its impact on retailers).
