Organizations are generating more and more data at higher rates. To harness the value of that data, employees need to be comfortable engaging with the data, achievable by developing a data literacy program around their needs and skills.
One of the key responsibilities for IT departments is to partner with business units to generate, collect and store data that can be transformed into actionable insights. Human beings seem to be doing a great job at the data generation part. Digital data created over the next five years will more than double the amount of data created now, according to Dave Reinsel, senior vice president at IDC's Global DataSphere, in the organization's 2021 DataSphere and StorageSphere forecast.
But how are organizations doing at helping end users access data and build the skills they need to analyze all this information? Not so good, apparently.
Data discovery, or not knowing data exists or who has it, was reported as the top challenge for using data to drive business value by 40% of respondents to a recent Alation research survey. The democratization of data, data not being shared between groups, and the lack of employees' analytical skills were among the other top challenges. In addition, 98% of the survey participants said their organizations faced negative consequences because of ignoring data when making key business decisions.
Obviously, providing organizations with an abundance of information and end users with the latest analysis tools isn't translating into widespread data-driven decision-making. So, what can be done to improve employee adoption?
Building a data-driven culture and a data-literate organization
One of the key requirements for a successful data-driven culture is a workforce that not only understands the value of data, but also feels comfortable working with it. There are numerous methods organizations can use to promote the benefits of data-driven decision-making, but the key to increasing employee adoption is to help them become self-sufficient.
Like any other skill, becoming self-sufficient in data analysis comes from a combination of training, mentoring, education and experience. The more self-sufficient a person becomes, the more comfortable they are performing tasks requiring those skills.
The term that the IT community uses to describe this level of proficiency is data literacy. Data literacy is a person's ability to read, write, analyze, understand and utilize the data they interact with on a regular basis. An employee that is data literate is also able to effectively communicate the meaning, usage and importance of the data they use to others in the organization.
Teaching employees to become data literate doesn't mean training every employee in the organization to become a citizen data scientist. The goal is to customize data literacy training so each employee has a level of knowledge that is commensurate for their role and position in the organization.
Information needed for the data literacy analysis process
Some of the information organizations need to gather includes:
- What is the employee's role in the organization (e.g., duties and responsibilities)?
- How comfortable are they working with data? What is their level of frustration, and what is its root cause?
- Do they have the skills they need? If not, what is the training and mentoring they recommend to help them become more self-sufficient?
- What data do they work with on a regular basis (e.g., volume, complexity and the amount of analysis needed to transform it into actionable insights)?
- Is there any additional data they need?
- What are the strengths and weaknesses of the tools they use to interact with data?
Data literacy program development
Depending on the line of business an organization is in, its size, employee head count and the amount and complexity of data it provides to its workforce, data literacy initiatives can range from months to multiyear journeys.
Here is a starter set of recommendations to help organizations jumpstart their data literacy improvement program:
Identify data literacy champions. Champions can help evangelize the benefits of data literacy and data-driven decision-making, which is critical to the success of the initiative. From C-suite to day-to-day operational personnel, champions should come from all levels on the org chart. It is important to have champions in positions that are relatable to the target audience. A shop floor manager that is a proponent of data-driven decision-making will have a much greater impact on plant employees than the organization's chief data officer.
Overcome cultural issues. Organizations can overcome cultural issues that include both individual data skeptics and general workforce data analysis insecurity by promoting the benefits of data literacy. These include faster decision-making, reducing manual labor and improving the quality of day-to-day operational activities. It's important to remember, however, the goal is to promote and inspire, not dictate and enforce.
Assess and prioritize data literacy needs. Like all analysis projects, organizations collect information at a high level and evaluate the findings to narrow the scope of their investigation. For data literacy, the goal is to work through the org chart to identify and prioritize the data literacy needs of the various units in the organization.
Gather workforce feedback. Use surveys to elicit data literacy feedback. Use the initial survey responses to identify global needs and trends, and more customized surveys tailored to specific organizational roles and units. Face-to-face group discussions and one-on-one meetings are perennial favorites for analysis teams that want to gather more detailed insights.
The goal of the analysis should be to identify the root cause of the workforce's inability to easily analyze and interact with the information that is available to them. Is it cultural, a tool problem, a data literacy problem or a combination?
Tailor data literacy training to employee needs. A sales team manager shouldn't need to be a data scientist to perform their daily responsibilities in a high-quality manner. They should have access to tools that provide sophisticated, but easily understood, analytical information helping them achieve their organizational goals. Their level of data literacy should allow them to further analyze the information to generate additional insights.
As stated previously, the key to data literacy training success is to tailor the curriculum to the employee's role and the level of skills they need to successfully interact with the data they use. The educational needs for a senior-level decision-maker are different than for a plant floor employee who is managing a product assembly line.
Another consideration for organizations is third-party data literacy training. There are numerous vendors that include BI software providers and traditional training companies that market data literacy courseware. The offerings range from courses that focus on a specific role in the organization to companywide programs.
Achieving data literacy isn't a one-time project with a beginning and an end. The organization's data literacy needs are fluid and dynamic by nature. New tools, technologies, data sources, people, processes and business objectives all combine to compel those tasked with maintaining the organization's data literacy objectives to continuously assess and refine their approach.