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Why data analytics teams must speak retail
A recent university study finds many retailers are still using “very basic tools” rather than advanced analytics partly because the analytics function is “often run by people who do not really understand” the retail business.
The study was based on interviews with 24 retail executives in the Americas, Europe and Asia, and found employees seeking solutions from analytics teams often tune out after realizing they don’t understand their problems.
The researchers, led by the University of Chicago, told the Harvard Business Review, that executives “told us they face a critical lack of employees with the right skills to design and use analytics tools. What they need most are employees who can bridge functional gaps — translators, that is, between analytics and the business.”
Other inhibitors include a risk-averse culture with unclear goals and an increasing number of employees considering their work as “more art than science.”
Retailers that better utilize analytics embrace the mantra “Think big, start small, and scale fast” and celebrate experimentation. The researchers wrote, “Specifically, leaders can spearhead an internal campaign emphasizing that analytics are meant to empower decision-makers, not replace them.”
Also recommended was developing in-house training programs to either teach business-domain knowledge to those on the analytics team or analytics fundamentals to others.
New tool sets are required with insufficient resources, legacy systems and siloed data among non-personal issues holding retailers from more fully embracing analytics. Developing “the know-how to exploit the new tools” often slows adoption.
The researchers wrote, “There weren’t many electricians around at the start of Thomas Edison’s career, and the Wright Brothers were bicycle mechanics. In this respect, the data analytics revolution is no different. What is different is the speed with which these new tools are being designed. In the age of data abundance, those who learn to profit from its insights first are almost certain to gain a powerful operational advantage over their competitors.”
A recent McKinsey study found organizational maturity to be a barrier to retailers’ realizing analytics’ full potential. McKinsey stated, “Organizational maturity encompasses both processes to technically embed and continually improve use cases, as well as constant change management with the users of the analytical insights.”
Discussion Questions
DISCUSSION QUESTIONS: Do you agree that advanced analytics adoption is being held back by “a critical lack of employees with the right skills to design and use analytics tools”? What solutions do you see to encourage greater organizational adoption?
Poll
Loading …BrainTrust
John Lietsch
Chief Operating Officer, Bloo Kanoo
Kenneth Leung
Retail and Customer Experience Expert
I think they have this backward. In my experience, retail leaders don’t understand analytics. The real culprit preventing adoption is that the analysis and tools that are developed rarely interface with the tools that the frontline HQ and store team folks use to execute their job functions. Too often, to implement the recommendations from a given analysis, the teams have to do manual overrides or manage exceptions. That’s not sustainable and, I believe, is a reason solutions aren’t adopted.
The challenge is not just about use of tools but also interpretation of results and being able to explain things appropriately.
Unless a given approach and format of presentation is understood widely across an organization it is likely that the analysis may not be adopted appropriately in driving decision making. Structure in approach and methodology ought to be the first step — perhaps with specific methods explained in their presentation as an aid to change management within the organization.
Yes, I believe that there is a gap in the practical, operational application of insights and insight creation by data teams. The key is to start with the question. What decision are you making, and what data do you need to inform the decision? Data and analytics are the “how” and asking the right business question is the “what.” One of the biggest challenges I see is in making the insights easy for field managers to use and apply. These managers are not data analysts, but all managers should be able to interpret basic data and apply insights in their day-to-day decision making.
Are we really comparing data analytics to the invention of the light bulb? Data analytics has been around for years and I don’t think it is being held back as much by talent as it is by the necessary organizational support, culture and business case. Data analytics has immense value but it must be leveraged properly because to the untrained or unscrupulous, data can be made to say just about anything. Organizations should focus on running better, more profitable businesses which demands making better, more profitable strategic decisions. To do that, they need data along with a host of other things but it starts with acknowledging the need to improve (the what) and determining the tools necessary to accomplish the goals (the how). One of those “hows” is unquestionably “data” but it isn’t the only one (and data isn’t the light bulb or the airplane).
Are we shocked? Retailers felt pressured into jumping into analytics without understanding what it was, how to do it, or how to use it. They just hired bodies to analyze their massive data sets and are now dismayed that the data analytics folk can’t produce anything useful. Fire them all — then start over with a team focused on solving business problems who can use data to give evidence, not gut feel answers, in a language everyone gets.
Yes. We have found in our research that retailers have a hard time getting good data analytic skills period. Overall, I think a good data scientist can make more money in other industries. Retailers aren’t willing, generally, to pay enough, so they rely on their vendors instead.
Perhaps the question should be: are the vendors doing a good enough job training and embedding analytics in their software? I think they likely are, but adoption lags for cultural reasons.
Now, when it comes to knowing the industry I am old-fashioned. I think EVERYONE in the retail enterprise should know the business, spend a few days working in stores and warehouses. Sit with buyers. So that’s not the core issue.
It’s a poor work-person who blames their tools. That said, not enough retailers understand data analytics well enough and clearly not enough tech folks understand retail. The result is that the former don’t know what to do with the output and the latter don’t know what output to generate.
We don’t need to be data scientists to understand the application of data analytics as it applies to our jobs. We do need a broader understanding of data literacy and applied numeracy so that the layperson can understand data-driven concepts and what meaning they convey. The fastest car is only as good as the driver behind the wheel.
This gap in analytics maturity is not only on the retail side but on the supplier side as well. Too many suppliers bring basic analytics and insights to the retail partner. Yes, retailers need help in this area, and those suppliers that truly bring insights that are un-biased and speak to how they can grow the category or categories in which they compete, for both the retailer and themselves, will be elevated to a more trusted advisor status. Retailers admit they need help in this area and often do rely on the supplier community to fill this gap, and there is nothing wrong with that as a retailer cannot be expected to be an expert in every category. Tools are a part of the equation, but the collaboration that does exist among some can raise the capabilities of all over time and when done on a more consistent basis.
I am with Gary. The problem isn’t the talent. We cannot expect everyone to understand all the insights of the business and how to apply them.
Today’s discussion uses the word “teams.” That certainly is the answer. However a team of analytical experts will not solve the issue. Seed that team with multiple functions. The results will be leagues more valuable.
Is it the employees who are lacking or the top management? There are people who understand analytics and ones who understand retail. Top management need to embrace the power of analytics, empower their staff, and invest in and train them correctly to be able to get the best out of the new technologies. If top management do not know the right questions to ask then blaming the employees is not the answer.
Data analytic technology has moved so fast recently that most senior leaders are not familiar with the tools, the methodologies, and the insights, and therefore aren’t able to leverage the full potential of these practices. To be a data-driven organization, it takes senior leaders to sponsor and lead this cultural shift.
My counsel is to start small with a dedicated team of diverse skill sets that include expertise in data science, architecture, development and business. This team works on use case pain points and solves them with a combination of process, technology and people solutions. As the team tackles bigger issues, build out a second team, third team, and continue to rinse and repeat. All of the sudden the organization will hit a tipping point where incredible value is being realized across many functional areas of the business, reinforcing the senior sponsor’s cultural shift.
Right on, David! I’ve always advised retailers to think big but work small. Deliver (and promote) value on a regular schedule.
Data must be organized to support key retail metrics. “Bad history is no history.” The world of data analytics can be blinding with bright shiny objects that are distracting and add little value. On the flip side, well-built reports supporting clearly defined and constructed hierarchies, attributes, and critical KPIs utilizing skilled translators can add tremendous value and efficiencies to any retail business. Be sure the data foundation is solid and go from there.
The researcher in me feels obligated to note this sounds less like research and more like a set of people making their own recommendations and using the idea it’s backed by research to justify them. Fortunately, they offer some good insights.
We remain in an era where people still think decisions should be data driven — which implies ceding management responsibility to the data. Thus they are quite correct that “analytics are meant to empower decision-makers, not replace them.”
Training, also, is needed. However there’s little to be gained by teaching analytics fundamentals to others. The analytics teams MUST learn how retail runs and what matters to retail. Thus, there is a need to “teach business-domain knowledge to those on the analytics team.”
Deming observed that applied statistics is 5 percent statistics and 95 percent application. So, too, any effective use of data. The application matters most.
The struggle is real and the gap exists between the business teams in retail and the analytics team. There are plenty of skilled data scientists out there to use the tools, the problem lies in the translation between the retail business owners and the analysts.
Fortunately, there is a standard that retailers can follow called CRISP DM that can be built into retail processes such as request forms and ongoing project work. The first step in this analytic process is called, “Understand the Business” and it is the responsibility of the analyst to understand the business problem and context, and to be able to freely ask questions to clarify.
If retailers don’t want to follow this process completely, they have another option which is to add a role of a data translator or project manager that can help bridge the gap between the business owners and the analytic/IT teams.
This sounds like classic early adopter/late adopter dynamics. It’s important to have the right process champions in place at the right time and place. Introducing and managing evolutionary change has always been difficult. It’s important to create and build on breakthrough “aha” moments.
Excel worksheets are still the most used tool for planning and analysis. Sigh. While analytic skills, partnerships between IT and LOB (line of business), and a “think big work small” approach are important, top down support and a data-driven culture is the most critical success factor.
Unless the C-suite insists on bringing information into decision making retailers will not be able to turn insights into action.
I totally agree. It is incumbent upon the data team to understand what keeps the merchandising and operational people up at night. Mining data for the opportunity of finding a nugget of information that answers a question that no one has asked, is all too often the norm. The retail practitioners can help in the process also. If they provide a list of priorities in the form of “If I knew this, I could better do that” … then the data team is focused on actionable outcomes, not just more information that likely will go unheeded.
“You were supposed to be the chosen one!” shouted Obi-wan Kenobi to Anakin Skywalker on the banks of a river of lava at the end of one of the Star Wars movies. Retail business leaders could say the same to their Analytics colleagues. Expectations are high when they allocated budget for headcount and technology to become data driven. Effort, then frustration and disappointment follows.
But unlike Anakin, all is not lost. Counter to the assumption expressed in the HBR article, if “organ withdrawal” exists, it isn’t only the fault of the analyst. Yes, analytics practitioners can do more to learn the language and culture of retail, but retail leaders also would do well to lean into the opportunity to better understand the analytics function. This is best accomplished through mutual transparency and a joint effort to create a culture of passion, persistence, integrity and respect.
Don’t give up. There is still good in this partnership.
Advanced analytics can generate the data, but someone has to interpret to determine the course of action. Moreover, the course of action needs to be executable within the framework of the company’s logistics organization. Having the data steer you in a direction that your rudder can’t turn to doesn’t help anyone.