By Dave Wells
Published on April 2, 2020
As organizations become more data driven and the scope of data consumers expands through self-service, data literacy and data fluency have become valuable organizational competencies. Current events throughout the world are escalating the value of these competencies from valuable to imperative. The COVID-19 pandemic affects all dimensions of business and society. As the pandemic moves from current events to recent history, data competencies will be essential for businesses to manage the economic, social, and regulatory consequences.
Data literacy is the ability to derive meaningful information from data. Literacy skills include the ability to find the right data for analysis and reporting, evaluate and understand the data, blend data from multiple sources, prepare data for specific use cases, develop conclusions or derive insights from the data, and publish those conclusions and insights in a variety of visual forms and formats.
Data fluency goes beyond literacy to include advanced skills such as expression of ideas about data. Data fluency is similar to language fluency. A language literate person can read and write sufficiently to understand and communicate. But it takes a language fluent person to produce captivating prose such as a novel or philosophical essay. A data fluent person goes beyond conclusions and insights to create data stories that drive ideation, spark the imagination, and fuel innovation. Data literacy produces reports and visualizations from data. Data fluency brings data to life. Ideally, every data scientist will aspire to stretch beyond literacy and become data fluent.
Data is a central component of modern business management. Everyone with a role in business decision making—strategic, tactical, and operational—must become data literate and those in some key roles must even be data fluent. In a previous blog, I described the Enterprise Data Catalog (EDC) as a single source for all of the information that is needed to work effectively with data. The EDC fills several important roles in developing data literacy and fluency:
The catalog is the primary means for data consumers to find, understand, and evaluate data.
It is a collaborative platform to share tips and techniques for data preparation and data blending.
It is a knowledge sharing resource that is used to capture individual and tribal data knowledge and effectively convert it to enterprise knowledge.
It is a learning resource where shared experiences among data stakeholders increase the collective knowledge of all who work with data.
It describes the standards, patterns, and guidelines that are essential to data storytelling to ensure effective communication and avoid miscommunication.
The impact a data catalog has on literacy and fluency is directly related to the maturity level of your data catalog implementation.
Maturity of EDC implementation is based on the scope of users and use cases for which the catalog is applied. The maturity model consists of five levels as illustrated in figure 1. Each progressively higher level includes all users and use cases of the lower levels and extends to include new users and use cases.
The characteristics of each level are described in the table below with columns expressing who uses the catalog, for what kinds of use cases, and the impact that is achieved for data literacy and data fluency. The table is largely self-explanatory, however, there is one important point that should be called to attention. Note that at level 4 the impact on literacy is expressed as “strategy of data literacy” and at level 5 it is expressed as “culture of data literacy.” I express it this way because I believe that culture is stronger than strategy. Strategy is an expression of what we aspire to be. Culture is simply the reality of what we are. Ultimately a culture of data literacy with steady growth of data fluency managed through a data competency program is needed to get from data inventory to maximum business value from data.
The enterprise data catalog offers tremendous potential to be a driving force to grow data competencies throughout the enterprise. Getting to level 5 is a journey, not an event. Begin with self-assessment to know where you are on the maturity model, then plot your roadmap to data catalog maturity. Be realistic and recognize that you can’t leapfrog from level 1 to level 4. Take a step at a time and continuously evaluate. Pay close attention to the human factors because adoption and engagement are critical success factors.