Digitalization has resulted in more data collection than in prior generations, and Covid has underlined the necessity to analyze data at a rapid pace. Businesses no longer want to wait a week for a report; instead, they want real-time or near-real-time actionable data in order to make better decisions.
Whether it is academic institutions trying to decide about opening schools or companies trying to sustain business due to global supply chain disruption, the pandemic has presented some unique, ongoing challenges for getting better at consuming and interpreting data. But not all data is created or understood equally.
There are several definitions of data literacy (DL) and it is constantly evolving. My version is having a certain proficiency and comfort with data usage in both personal and professional life.
What is the Struggle Behind Becoming Data Literate?
We live in a smart world with data all around us. Have you gone to shop for a new microwave or washing machine? Whether it is our microwave or washing machine or garage door opener, the most minimal thing all these modern devices can do is to connect to WIFI and generate data. There is no shortage of data we collect and store, but what we do have an shortage of people able to understand the data.
Most of working population did not have formal training in data literacy. Hence, there is anxiety around the volume of data collected and increased expectations about data usage in everyday lives and at work. It is no different than financial literacy, which has been around longer. Just earning a paycheck with no budgeting or investing will result in an uncertain future. Similarly, collecting data without planning how to use it results in losing a competitive edge in the current market.
Technology is constantly evolving and new ways of doing things are constantly introduced. Not everyone is keeping up with the pace. People’s unease around math and statistics is prevents them from making informed decisions and harnessing the power of data. AI-generated art just won first place in the Colorado State Fair, highlighting the integration of data in our daily lives and the importance of gaining some level of data proficiency.
Just creating dashboards and investing in digital solutions will not result in business value.
There is a data-understanding gap between people in technical and non-technical fields, causing different perspectives about data. The goal of data literacy programs is to reduce and eventually eliminate such gaps.
7 Tactics to Foster Data Literacy in Professional Lives
A common myth about data literacy is that it requires a lot of training, or the ability to code in R and Python, or everyone becoming data scientists. As a result, launching a data literacy program and hoping everyone will follow along is not a realistic expectation.
The following are some strategies for leading a successful enterprise data literacy program:
- Eliminate the fear of data. Acknowledge that people learn at different speeds and make mistakes along the way. Create a small safe learning atmosphere to eliminate the fear of failing, so that everyone will be comfortable joining.
- Get leadership to buy in. Data literacy should follow a top-down approach and be driven by leadership. To achieve success with business data literacy, leaders must be aligned and appreciate the value of data.
- Approach data literacy as a culture change. Cultural change is complex and requires perseverance to shift to a new direction. If data usage has not been a common practice in the organization, it will take longer to change the mindset.
- Define what success means to you. Success could mean different things for various organizations. It could imply anything from getting real time data in the hands of most team members, to less errors or production issues, or to avoiding data breaches. Define what success means to your organization in 3, 6, 12 months and years to come, to succeed in your data literacy journey.
- Start small. Every organization's journey to data literacy is unique, and in order to succeed, it is critical to start small with a pilot project. Create a small user group to demonstrate the benefit of data in their day-to-day activities and iterate the steps.
- Pick the right tools. It is not always necessary to select the most expensive or fancy tool. Try to assess the teams' existing skill level and select a functioning tool that will suffice, while keeping the learning curve to a minimum.
- Plan for changing needs. Do not treat data literacy as a standard set of trainings and material. Data literacy varies from team to team and changes over time for an individual. Plan for your data literacy journey to evolve.
Moving from Office to Home
As the world around us becomes increasingly digitized, succeeding in data literacy in their professional lives indirectly assists people in their personal lives.
Sangeeta Krishnan is an engaging business intelligence and analytics leader who possesses a winning blend of subject-matter expertise and practical experience from a variety of industries. Most recently, she joined Bayer as North American Analytics Lead for mass sales. She has worked with Fortune 500 organizations, not-for-profits, and everything in between, helping various organizations build their operations and monetizing data products from the ground up. Krishnan is a public speaker, content creator having articles published in industry journals, and was recognized as a Finalist of the Women in IT Awards 2018 (USA) in the Data Leader of the Year category. She is the author of Thriving in a Data World (Business Expert Press, December 2022).