Your Employees Don’t Know Analytics
Why a properly trained workforce is your first step toward a data-driven culture
Everyone is striving for a data-driven culture – such cultures harness big data and predict future results to make better decisions today. But here’s what data-driven cultures do that other organizations don’t: they invest in analytics training for their employees. Whether or not you suspect the analytics acumen in your organization is low, it’s probably even lower than you think.
Let’s admit it: analytics is broad and buzzwordy.
Analytics comes in all shapes and sizes, and the rate of change is overwhelming. Defining analytics as broadly as a KPI or as complexly as predictive modeling, professionals struggle to agree on the true meaning of analytics and its role in business. That being the case, how can average employees know what it means? Analytics, big data, AI, predictive analytics – these are still buzzwords with loose definitions. But underneath the buzzword exterior is deep value to be gained.
Analytics capability is accelerating faster than most organizations are keeping up.
In the mid-2000s, the value of advanced analytics in business was hotly debated. Contrarians thought topics like data science and machine learning were overblown. However, advocates like Tom Davenport of Harvard Business School believed big data was the next wave of value for organizations. History is leaning in favor of Davenport if corporate investment is any indication.
One example of analytics’ exponential growth is data collection. According to a report by IBM, 90% of the world’s data was created in the last two years. The recent Cambridge Analytica scandal and subsequent questioning of Facebook’s information protection processes are just the latest examples of how far-reaching data collection has become.
The challenge for organizations? While most are getting smart about data collection, few know how to use the data to its full potential – if at all.
In response to this growing capability, employees have well-placed intentions and a desire to dig.
Recently, I overheard an employee at a major corporation discussing a data mining project. Data mining derives value from raw data and is often a precursor to analytics. This individual was downloading data from a business intelligence tool, extracting the data to Excel, and using the raw data to build basic reports. The employee did not realize such analysis and more could be performed within the tool itself. Unfortunately for this employee’s department, “data mining” had come to mean “boring data manipulation” (also known as data munging).
This is a common occurrence. The employee was likely never trained on what data mining is, let alone how to use the department’s pricey business intelligence tool. This lack of understanding of fundamental analytics process wastes time and creates confusion on how analytics can add value.
The biggest barrier to analytics is the learning curve.
Clearly the employee in the above example wanted to mine data and has the ability to learn how. Let’s look at three studies that make the case for analytics education as a precursor to other types of analytics investment:
- In NewVantage Partners’ 5th annual survey of senior corporate executives, more than 85% of respondents reported starting programs to create data-driven cultures, but only 37% reported success. These failings highlight the steep learning curve for employees who must adjust to changing technologies and decision-making processes.
- In a Harvard Business Review survey of HR Executives, 47% believed one of the largest obstacles to achieving better use of data was a lack of analytic acumen.
- In TDWI’s 2016 Best Practices Report, two-thirds of respondents said they either have an analytics program now or plan to within a year, but the largest barrier to adopting big data was a lack of skilled professionals.
This means buying the latest software like SAS, JMP, or Tableau won’t alone raise analytical proficiency. Qualified employees who would jump at the chance to use these tools need training on how to work them. Non-technical employees with great potential but little confidence or exposure to analytics must be brought out of the woodwork.
Cultivating a data-driven culture starts with education.
Advances in data storage, computing power, and software have made analytics skills easier to acquire for anyone who wants to learn. Leaders should dispel the myth that one must be gifted in technical skills to contribute to a data-driven organization and provide their people with support to improve acumen. To start, here are questions to help you assess where your organization is today and identify opportunity areas:
- Does my organization have an analytics training program?
- Do we have software that makes it easy to produce visualizations and explore data?
- How easy is it to access data for the employees who need it?
- How do I know the data is correct?
- Is there a department to oversee the fidelity of the data stored in our databases (e.g. data governance)?
- Is it too difficult to acquire and share data?
- What bottlenecks exist in getting employees data to analyze?
Broad understanding of analytics is the only way to create a data-driven culture. It’s not enough to provide tools or create departments with analytics focus. In your organization, people exist who not only have the capability to learn analytics, but also the appetite.