Boost Your Team’s Data Literacy
Data skills are now essential for almost every role in every organization. Companies need more people with the ability to interpret data, to draw insights, and to ask the right questions in the first place. These are skills that anyone can develop, and there are now many ways for individuals to upskill themselves and for companies to support them, lift capabilities, and drive change. Indeed, the data itself is clear on this: data-driven decision-making markedly improves business performance.
In every industry, companies are amassing more data than ever. Retailers micro-inspect our purchasing history, airlines measure why flights are on time, and just about every service call, email, and interaction is tracked. The benefit of all this data? Companies can figure out how to better solve customer needs, improve their services and operations, and make better decisions about the talent they hire.
We’ve entered a golden era of data. You don’t have to be Walmart or IBM to build a data lake in your company — that opportunity is now available to every company through cloud-based systems at modest cost.
But while we have all this data, and it’s becoming more influential than ever, there’s still a big problem at hand: Most of us are not very good at interpreting and making sense of it. A few years ago, The National Center for Education Statistics (NCES) assessed the data-interpretation and problem-solving skills of adults in 23 countries, and found that the United States ranked 21st out of 23 countries participating. A vast percentage of the workforce needs to enhance their data literacy.
Schools and colleges are often ill-equipped to solve the problem of data literacy. Math curricula prioritize critical core concepts (calculus and algebra) above more applied subjects like statistics and probability. Campaigns for greater pragmatism — like this 20-year-old paper — have gone largely unheeded.
Today, the responsibility has shifted from academic institutions to employers, where skills development programs are flourishing. Companies like Bloomberg, Guardian Insurance, and Adobe now have data science and digital academies that are focused on helping employees in all disciplines learn how to analyze data.
This is a responsibility more employers should embrace. They have the resources, and they’d certainly reap the rewards.
Over the last five to 10 years, the data skills we need have evolved. In the early days of data science, companies wanted skills in SQL, data extraction, information normalization, and technologies like parallel processing, big data analysis, and R (a programming language).
Today, as research from IBM indicates, many of these technologies are embedded into data platforms, so companies are looking for different skills. Not only do business people need to understand the nature of these data systems and how they work, they need to understand how to create sound data governance, privacy, security, and trust. As AI becomes a more critical component of business, modern professionals have a growing need to understand how to challenge the outputs of algorithms, and not just assume system decisions are always right.
For example, we recently conducted a focus group of 20 sophisticated companies telling us which data skills were missing in their own organizations. These analytics teams told us they were not suffering from a lack of technical skills, but from skills in data-driven problem solving. They specifically mentioned people lacking the skills to:
Of course, we need underlying advanced data skills, but what our research found is that it is these associated, attendant soft skills that make an even bigger difference.
The cost of not understanding the context of data is huge. A global retailer’s analytics team discovered an enormous variation in sales volume and employee turnover across their retail locations. They studied the correlation of revenue to various metrics and reached the conclusion that stores with more specialized job roles and solutions-oriented sales teams were significantly outperforming their peers. They also had happier employees and lower turnover. When the team (which spent an entire summer analyzing the data) presented the results, the executives who heard the story were impressed. They believed there was something big in the findings. Until one of the executives asked: What season of the year did the analysis apply to? It turned out that over the summer, the retail workforce is highly contingent and turnover always triples for this organization during that single season. That context had not been considered, and rendered that initial iteration of the analysis essentially meaningless.
Ultimately, the findings were significant, and the team went back and learned a lot about their business. But the basic consulting and business skills of understanding context and creating clear definitions of data were missing.
Data skills are now essential for almost every role in every organization. But with such skills being so deficient in so many businesses, it’s difficult to know where to start improving capability. We encourage people to focus on the basics.
First, make sure people know how to use the tools. Excel is still very important: 35 years on, one in five adults on the planet use Excel. While lots of software will provide reporting and analytics functions, for most users (unable to utilize full business intelligence) to feel like they’re in control of their data and, in particular, to compare data from distinct and disparate sources, Excel (including the still-ubiquitous CSV file) remains the common currency. Here’s an infographic of 100 Excel features, ordered by usefulness, to help you prioritize learning.
Second, set up a capability academy for data skills. A capability academy is a place employees go to advance specific job-related skills. They are sponsored by business leaders — ideally all the way up to the CEO — who care about data skills at the firm. Too frequently, general-purpose corporate learning systems start off claiming to do everything for everyone and, lacking a tangible purpose, end up doing little for anyone. A data-oriented academy, aligned to a change program the company manifestly cares about, is more likely to work. Carefully select the data skills needed by your workforce to arrive at a skills framework. Use that framework to curate the experiences, people, courses, podcasts, videos, and articles that will spark learning joy. Cull the rest. To enhance data skills even for a large population, you probably need hundreds, rather than thousands, of learning opportunities. Quality beats quantity.
Third, use examples and stories in awareness campaigns to teach and inspire others about how to use data well. When the company uses data to make a major change in strategy, present the results to the entire organization so people see examples of how to do this well. For example, Starbucks applies AI to mobile app data to optimize suggestions for new beverages to customers. Netflix uses its vast data sets not only to personalize content on the platform, but also to help create compelling content. PepsiCo uses data visualizations to make high-stakes sales decisions.
These stories will likely cross functional areas — that big marketing campaign which evolved with some A/B testing; what hypotheses A and B were; what happens next on your latest experimental project; the inventory analysis which identified inefficiencies, better stock management and cost savings; the sales report which led to increased focus on more profitable accounts and a sizable revenue uplift; the employee engagement survey which led to some well-loved perks for the whole company. These are all potentially meaningful, emotive data stories for multiple populations.
As part of this, emphasize the benefits of improved data skills ahead of the skills themselves. The skills (statistics, lookups, error checking, etc.) can quickly become off-putting. But the main benefits can be appreciated by everyone: better judgment and decision-making, and increased confidence when making those decisions. Utilize real success stories to bring some definition to these soft-sounding benefits.
Fourth, bake data into all important decision making. “What data do we have or can we get which supports or contradicts this business case?” is the kind of question that should be asked as a matter of course. When presented with data used as evidence for a particular decision, encourage people to (respectfully) interrogate it. Are the sources reliable? Is the analysis correct? What checks have been made? Are other sources of evidence consistent with this story? How important is the decision? What further evidence will we need in order to act?
One way of doing this is to put relevant, real-time data into the hands of first-line managers, forcing them to pay attention. For example, Warby Parker has grown a large data science team but, more importantly and unusually, puts data responsibilities in the hands of everyone, including store level staff. A large defense contractor gives all managers a set of high-level dashboards to measure “talent health,” which aggregates employee engagement, ratings, and other employee feedback. A whole new set of “action platforms” for managers are now available from vendors in all areas of business.
Data literacy has become important, for almost everyone. Companies need more people with the ability to interpret data, to draw insights, and to ask the right questions in the first place. These are skills that anyone can develop, and there are now many ways for individuals to upskill themselves and for companies to support them, lift capabilities, and drive change. Indeed, the data itself is clear on this: Data-driven decision-making markedly improves business performance.
Josh Bersin is founder of Bersin by Deloitte, and now the Josh Bersin Academy, the research and professional development academy for HR and business leaders. He is a global research analyst, public speaker, and writer on the topics of corporate human resources, talent management, recruiting, leadership, technology, and the intersection between work and life.
Marc Zao-Sanders is CEO and co-founder of filtered.com, a company that blends consultancy with technology to lift capabilities and drive business change. Find Marc on LinkedIn here.
Boost Your Team’s Data Literacy
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