Photo: Getty Images/Dobrila Vignjevic
Are employees or execs holding back data-driven cultures?
According to a survey from Exasol, nearly two-thirds of data teams (65 percent) have experienced employee resistance to the adoption of data-driven methods at their organizations, despite an overwhelming 73 percent believing that most employees are open to a data-driven approach.
Of those respondents who have experienced some resistance, 42 percent of decision-makers attribute a lack of understanding of the organization’s data strategy, followed by a widespread (40 percent) lack of education about the positive impact data brings.
The survey of over 2,000 data strategy decision-makers from the U.K., Germany, the U.S. and China was taken just before the emergence of the coronavirus. Past studies have found companies struggling to embrace data-driven and data-first approaches. The shortfalls have since been amplified as COVID-19 has forced companies to unlock the value of data faster.
Randy Bean, CEO of NewVantage Partners, a consulting firm, however, believes a lack of executive commitment is often the major impediment to a data culture. He wrote in a column for MIT Sloan Management Review, “Most pay lip service to the criticality of data in their annual reports and company mission statements, but far fewer companies embody it in their DNA or in their day-to-day business practices.”
Alation’s just-released “State of Data Culture Report” similarly found c-level executives often resistant to embracing data despite 78 percent having company-wide initiatives to become more data-driven.
Alation’s survey of 300 data and analytics leaders in the U.S. and Europe found 67 percent asserting their company’s C-level executives ignore data when making business decisions, relying instead on gut instinct. The top reason was that the execs believe their gut instinct is the differentiator, cited by 42 percent. Another 35 percent said it’s because there’s not enough collaboration and 35 percent said it’s because they are used to doing things their own way.
Trust in data quality was found to be a primary hurdle in the effort to be data-driven. Ninety percent of those polled said that their executives at least sometimes question the data that they use, with 56 percent saying this happens often or all of the time, according to Alation’s report.
- 65 percent of Data Teams Have Experienced Employee Resistance to Adopting Data-Driven Methods – Exasol/Business Wire
- Why Culture Is the Greatest Barrier to Data Success – MIT Sloan Management Review
- Alation Releases Inaugural State of Data Culture Report – Alation
- Data and the Human: Data-Driven vs. Gut Instinct – CIO
- How to build a data-driven company – Silicon Angle
Discussion Questions
DISCUSSION QUESTIONS: Are employees or c-level executives putting up greater resistance to creating data-driven cultures and organizations? Is data quality or some other factor the bigger issue, and how should companies address these issues?
Poll
BrainTrust
Ben Ball
Senior Vice President, Dechert-Hampe (retired)
Phil Rubin
Founder, Grey Space Matters
If so, they are doing it at their own peril and that of the organization they are supposed to be leading. Every modern organization knows that data is the most valuable asset they have in order to compete effectively. Having the people that have the skill sets to understand how to turn that data into actionable insights and recommendations is critical. CEOs who are not taking a laser focus on making sure their employees are trained in this area are not effective CEOs and should be replaced.
The retail companies that are doing so strikingly well these days — think Target, Ulta, Tractor Supply — share both strong executive leadership and a data-driven culture. If we’ve learned anything during COVID-19 it’s that strategy, new technologies, and executive leadership are table stakes now.
Many executives focus on the outliers. Those outliers frequently call into question the total benefit of the data driven approach so squeaky clean data is a must to change the culture. Education is also important as everyone who interacts with the data needs to understand the tools. It is critical that the person scanning the product at POS understands their role as well as the CEO. Gut feel backed by science is a winning combination.
I find this really interesting as I had previously thought it was lack of skills that was holding things back. However when I think about it I can remember many years ago with decision support systems, individuals being suspicious of the results – as if they knew better. This was part of the overall change management that was required but it was explained to me that this was due to the deep rooted fear of losing their value. How true this was I really don’t know.
Given today’s arsenal of data, ranging from advanced traffic cameras and metrics to easy and accurate a/b tests, it’s amazing to see the lack of data adoption at all level of an organization. Though some employees drag their feet, these employees’ behavior often stems from a lack of accountability and motivation from C-level execs. But when the execs begin expecting data-driven decision making, an organization typically falls in line–and, not surprisingly, begins to excel.
Gregory, good points on the arsenal of data being generated and thrown at retailers. Do you think it is a lack of adoption or a lack of ability to manage that tsunami of data you mention? My experience tells me that it is the lack of ability to manage more data from all sides with platforms that are already mishandling the current data.
The lack of accountability you mention has also been from making buying decisions that “seem” smart but actually hinder their groups from being more productive with data.
Decided to start off the week with an easy one for us, eh? Thanks. Of course they are! And for reasons both good and not so good. My first thoughts run to the not so good category. Lack of understanding, reluctance to give up the freedom to exercise judgement over data driven direction, loss of control and relevance as “the decision maker.” But the good reasons are also valid. Data driven outputs are only as good as the data inputs and the algorithms — both of which are the product of human intervention and can be manipulated either by error or intent — before the executives ever see the data. Data only tells part of the story, we don’t know what we don’t ask. And every good researcher knows exactly how to ask questions of either the respondents or the data base that will produce the answers they want. And perhaps the best reason is this, Wayne Gretzky (and a few really great executives I have known) used to say “I skate to where the puck is going to be, not where it is.” The data tells you where the puck was, if it’s accurate, but it takes human intervention — whether you use numbers and call it a forecast or your gut and call it a prediction to know where the puck is going. Great executives know which to listen to and when.
When I was in college back in the 1980s, I spent each summer at Wakefern in their management training program. It was a great experience. One of the highlights was spending time with the buyers, who all had stacks of t-log data printouts in their offices. Whenever I’d ask what they were used for, the response inevitably was that the buyer was supposed to be referring to the trends in the printouts when they were making assortment, pricing and promotion decisions but never even looked at them because they knew more than the data.
Fast forward 35+ years and there is still an attitude that the training merchandisers and others receive sets them up to know more than the data. Even though these are typically middle age white men making decisions for marketing to women of all ages and backgrounds. This disconnect is costing chains a lot of market share and profit, and needs to stop.
Organizations need to be data-driven, especially with the more advanced tools available today, but not at the expense of some judgment calls that might be based only on experience and instinct. The debate rages in all sorts of industries, including Major League Baseball, where a new generation of metrics-driven executives has displaced a lot of old-school understanding of players’ character and mentality. The smart teams (like the smart organizations in any industry) have found room for both.
Perhaps the resistance is wise – that means employees and C-suite executives all see what data teams may not: Data most often discovers things of small importance at great cost to the organization. The juice is often not worth the squeeze.
Trust in data is the key. In organizations where business users are owners of the data – they are responsible for defining the attributes, valid values, drive the data migration strategies – those organizations do well.
When all the data related strategy, tactics and execution is left to IT, then it is easy to see why the business does not trust the data, and therefore the analytics culture never gets rooted in. Unfortunately such organizations are the norm rather than the exception.
My experience has shown that a large portion of leaders running retail organizations have been in retail all of their careers. I think in the grocery business it is even more so the case. So having come from the food business, I can say that may seasoned executives still very much rely upon gut instinct, although I can also say that there is a migration toward leveraging data. The migration is not as swift as perhaps it should be, however I do believe it is happening.
This sounds like a classic struggle between left brain and right brain thinking. The creatives don’t want to be muzzled by the number-crunchers and the number-crunchers would love to find a way to give the creatives some boundaries. In the apparel business it’s about risk management, not risk elimination. It’s about the right amount of risk to take at the right time and at the right place. Dosage. Risk is a differentiator, so absolutely embrace it! If the creatives and the number-crunchers can actually listen to one another and learn from each other, some pretty amazing things can happen. Data-informed design is a thing. Partnership. Teamwork.
There isn’t much to add. Randy Bean nails it. It is what I have been seeing forever.
I am glad he includes mission statements in his comments. One of the most fun exercises when I teach is taking mission statements by know companies and comparing them to what the companies actually do. We then rate them on the BS Quotient.
“BS Quotient…” That’s a great one … I’m “borrowing” that from time to time if you don’t mind, Gene!
Be my guest!
Data vs. gut and experience – That’s been a discussion in my merchandising career for decades. I’ve heard data scientists at tier one retail tell me that their tools are close to eliminating the need for trend offices and line reviews. They claim that soon they’ll be able to tell buyers exactly what to buy, where to put it, and in what quantities. Reminds me of the dog in the airline cockpit. The pilot is there to pilot the plane, the dog is there to bite the pilot if they try to touch anything in the cockpit.
I’ve also heard the counterargument from buyers and merchandising executives; all the business intelligence in the world is useless when a superstar influencer shows up at an event in a new silhouette and demand suddenly spikes for an item we passed over at line review.
The reality is there is a happy medium. Successful merchandising requires a keen understanding of markets and customers and local, precise data to support and validate product decisions. Leveraging both is the winning combination.
All levels of an organization should have some knowledge of data literacy. C-level executives need to lead the way by supporting data-driven decisions and initiatives – without this support, a data-driven culture isn’t going to happen.
Everyone knows the importance of data but using it effectively has been a challenge for a while now. Retailers need to think of the right questions to assess the data efficiently, it is very easy to go down a rabbit hole in the absence of the key goal of using the data.
From my experience, the reality is that both employees and execs can frequently be responsible for holding back data-driven cultures. The main difference is that the right execs with the right focus, determination and leadership are able to move their organizations in the right direction whereas it is a lot harder to drive change from the bottom. One thing is clear, there will always be resistance to new change and best practices – the simple fact is that a lot of execs got where they are by doing things a certain way for a long period of time and new data best practices might not have been involved in their advancement. Nevertheless, the best leaders (at all levels of the organization) should be persistant in driving a culture of data-informed decision making.
The painfully obvious answer: CEOs and corporate boards.
One of my favorite quotes related to this, which we use to support the idea of leveraging customer data and specifically comp customer (vs. comp store) metrics comes from a lawyer, Louis Lowenstein, who was also a professor at Columbia: “You manage what you measure.” He wrote this related to “Financial Transparency and Corporate Governance” in Columbia Law Review.
To wit (I’m not a lawyer but have been accused of looking like one), only companies that are clear loyalty leader like Amazon and Charles Schwab report comp customer metrics.
No, employees are not holding back data-driven cultures. The article mentions data resistance as being one of the issues, yet does not define this. Change and growth are always about better preparedness, including an understanding of the changes and why.
There is a saying “In God we trust, all others must bring data” that we’ve all heard at some point. But it is human nature to make a decision based on instinct or experience, and then to find data that justifies the decision. The first step to change this reality is to ensure a reliable system of record — whether that be a sales force automation or enterprise financial system — that is used consistently and rigorously maintained. Only with a reliable system in place can data-backed decision making be demonstrated and reinforced at all levels.
My bet is that the “nearly two-thirds of data teams that have experienced employee resistance to the adoption of data-driven methods at their organizations” aren’t just resistant to data, they’re resistant to change of any kind. Humans aren’t built for change (we’re built for survival) and unless you create a culture that embraces and creates change, including how data is used, you’ve already lost the game.
Employees are hesitant because historically they have been handed enterprise objectives that do not match the enterprise capabilities with the expectation that it all will “work out.”
One area that is common is around the lack of data management that actually INCREASES the effort to get to Analytics (the outcome of a data-driven strategy) and REDUCE the quality of the insights or the velocity in which they can make decisions on the data.
Enterprises have scattered data to all parts of their company and it is now in many different formats and types.
Try managing that process effectively to become “data-driven.”
Our experience shows us that 75% of data scientists’ time is spent just wrangling data from all parts of the company to even run advanced analytics or data model it due to the distribution of data across the enterprise.
Data Management is STILL the critical component and a flexible, but harmonized data foundation is the key to moving forward.
It’s no wonder they are hesitant around the term “data-driven”….
The key is trust in the data. I think the newer generation is more accepting to being data driven after seeing the success of analytics and also raised on metrics starting with personal social media (likes, followers, etc) to online shopping/advertising which are obviously data driven. Some execs have the “managing the message” approach to running the business and probably have less trust in data, especially when it goes against previous experiences.
My experience is, too many executives want data and research only as long as it fits their narrative. They’ll pay for it and then ensure it gets buried.
For entrepreneurs, it’s arrogance and impotence. For corporate executives, it usually revolves around job security.