Understanding Data Literacy

Understanding Data Literacy

David P. Marco, FIIM, Fellow, FIIM, CDMP (Master), CDP, CBIP, Data Management

Data Literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.

Stated simply, data literacy means that a person is able to identify a question or situation clearly, know what data is needed for that issue and where to get the right data, how to read / interpret that data objectively, and how to use the results to solve that problem or address that situation. Many organizations have so much data they do not know how to use it and when not to use selected data. Selected data literacy skills include, but are not limited to:

• Knowing what data is appropriate to use for a particular purpose
• Knowing where to search for the right data and its meaning (metadata)   
• Interpreting data visualizations, such as graphs and charts
• Thinking critically about information produced by data analysis
• Understanding data analytics tools and methods and when and where to use them
• Recognizing when data is being misrepresented or used misleadingly and holding themselves accountable for ethical data usage and management
• Communicating accurate results about data to people

5 Ways to Install Data Literacy

#1 Clearly communicate benefits and actions for adopting an approach to improve data literacy

The executive leadership, with the data management director needs to define what is data literacy for the organization and why data literacy is important. The leadership must be able to explain the benefits of adopting data literacy (or any new skill or tool) to anyone who may be affected by such an endeavor. Employees must believe that the change will benefit the company and themselves in their day to day work to be successful.

#2 Develop an enterprise approach to data management based on best practices and industry standards

Most organizations do not develop and enterprise approach for data management. This is a large task and requires the formation of a Data Management Program team, data management charter, adoption and implementation of an enterprise data management framework, organizational change management, several new technology implementations, and many other vital activities.

#3 Adopt and maintain a culture that encourages data literacy

Data literacy needs to be included in the Data Governance Communications Plan (how data literacy will be communicated to interested parties) and the Data Governance Socialization Plan (organizational change management). Promoting data literacy in organization starts with culture. Organizations need to establish data-is-essential cultures that encourage the use of data, with strong support for the use of facts in decision making and a culture that celebrates curiosity and critical thinking. Creating this type of culture requires a combination of the right technology, the right people, and a well-defined plan for organizational change management.

Recruitment will be one of the first areas where this shift will occur, as companies hire data-driven employees who champion the use of data throughout their organization. In addition, organizations need to prioritize the training of their current employees to give them the skills needed to be data literate.

#4 Develop roles and responsibilities for all aspects of enterprise data management

Data management has 12 key knowledge areas. They are:

1. Data Governance
2. Metadata Management
3. Data Warehousing and Business Intelligence Management
4. Data Science
5. Big Data
6. Data Quality Management
7. Reference and Master Data Management
8. Data Security Management
9. Database Operations Management
10. Data Development
11. Data Architecture Management
12. Document and Content Management

It is important to understand that almost all organizations will emphasis some of these knowledge areas more than others. From the standpoint of data literacy, it will be important for an organization to have some roles and responsibilities defined and staffed around the knowledge areas that are most important.

A holistic approach should be taken when it comes to roles and responsibilities. It will be vital to define how each job role will be affected working with data and ensure that this message is fed back to them. Roles in data governance and data stewardship are often the first places successful organizations start on the data literacy path.

#5 Overcome the “report factory” mentality and focus on data and its usage

Most companies have a report factory mentality. They are highly skilled at pushing out lots of reports. On the other hand, the reports may use the same field and define it multiple ways, the data will be inaccurate, decision makers will not understand the data underlying the reports, and promise of data-driven decision making through advanced analytics will not be realized. Organizations with high data literacy realize that the need for a global definition for each attribute and valid domain values for key business terms.

What is even more critical is that the data presented is beneficial to the company. Organizations are drowning in the ever-growing number of reports and are mostly disappointed by the lack of expected or promised insights from their data. The skill of data literacy is needed to determine which content is valuable and what is meaningful material.

Data Governance and Data Literacy

Data governance and data literacy are two important building blocks in the knowledge base of information professionals, as both address data quality and general data management. Applying data governance practices and instilling data literacy education helps in delineating decision domains and defining accountability for decision making.

Data governance enables data literacy as the data governance program develops and implements data rules, policies, standards; decision rights; accountabilities and methods of enforcement. Effective data governance programs contribute to the improvement of an organization’s data literacy when the program is enterprise-wide and built using industry best practices.

Conclusion

Building a data literate organization will not happen on its own. It takes a focused effort, and a well-funded and sustained enterprise data management program to be successful.