What is Data Literacy, and Why Do We Need It

Data Literacy is the ability to understand, consume and produce data. Today, no one goes through one day without having to understand and act upon data, but for organizations, it takes on a vital role. Data Literacy is the enabler of Data Culture, the ability to ask business questions and use the answer for a more accurate decision-making process. It is one of the main pillars of a data-driven organization and touches upon the better part of any organization.

Data Literacy needs differ according to a role – CFO, CDO, and VP of Customer Success all have different perspectives and applications for Data Literacy: the correct factors for consideration when purchasing a D&A software, the correct method to attribute revenue to specific data projects, prioritizing the most necessary data initiatives, correct churn calculation, etc. 

The domains of Data Literacy should include (the list is not exhaustive):

• Planning/implementation/measurement/management of the D&A function;

• Adjusting management & training to focus on D&A-based decision-making;

• Communication of the D&A initiatives achievements;

• D&A Governance (coverage, monitoring, diagnostic, Data Contracts resolution);

• D&A Asset Management (Governance, Security, Privacy, Monetization);

• D&A stack (infrastructure, teams, processes);

• D&A applications workbench (BI, AutoML, AI, vertical decision supporting);

 
The roles and responsibilities around Data Literacy that enable and support Data Culture in the organization are (again, the list is not exhaustive):

1. D&A producers (architects, developers, data engineers, DS/ML/AI teams):
   Their role is to create, integrate, aggregate, build, analyze, run, monitor, and fix the D&A. All of these tasks should have (preferably augmented ways of) documentation – active metadata tools can take on most of the documentation tasks, monitoring, and diagnostics.
   
   
2. D&A consumers (everyone in the organization):
   Their roles in facilitating work processes and decision-making with D&A evidence require those inputs from the D&A producers if self-service is unavailable.
   BI tools could be helpful here for self-service dashboards and ad-hoc reporting  and querying. Vertical applications can also be helpful in more tailored scenarios. Semantic Layer and Metric Stores could be used for analysis and metric discovery, documentation, and resolution.
   
   
3. Governance/Data Stewards:
   Their responsibilities include management changes, communication, business value assessment, GRC of D&A assets, Data Contracts conflict resolution, D&A requirements gathering, and roadmap implementation.
   Relevant tools: active metadata management, catalogs, lineage, Semantic Layer, Access Management, Metric and Feature stores, and analytics applications.
   
   
4. Citizen D&A builders:
   We see growing demand from business functions not only consume but also produce and integrate D&A by themselves. The tools and tasks are similar to those of the D&A producers, with the addition of low-code/no-code environments for analysis/metric/Ontology/modeling.
   
   

All of these roles should have different components of the Data Literacy domains listed above, and to some extent, they should be defined explicitly as part of the position requirements. Many of the Data Literacy and Data Culture (and thus Digital Transformation) projects fail because the employees’ “day job” does not include any components of the Data Literacy domains. Some people do it voluntarily, but organizations should not rely on the employees’ intrinsic motivation only – they should actively add capacity for Data Literacy-related tasks.

Last but not least, Data Literacy initiatives should be “budgeted” under each of the strategic planning items: If customer segmentation is the number one priority for the next year, you should budget tools, FTEs, and the relevant Data Literacy domains (GRC, Communication, D&A planning, implementation, and monitoring, etc). Pairing strategic business initiatives with Data literacy initiatives will help to prioritize, scope the projects, and attribute value instead of “boiling the ocean.”

A list of useful resources if you are looking to expand your knowledge around Data Literacy:

A great resource of Data Literacy concepts and practical guides from Arizona State University
https://www.youtube.com/playlist?list=PLNrrxHpJhC8m_ifiOWl1hquDmdgvcviOt

CDO guide to Data Literacy
https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy

5 Key Actions for IT Leaders for Effective Decision Making https://www.gartner.com/en/publications/what-effective-decision-making-looks-like

• These other “Smarter with Gartner” references (not behind the Gartner paywall) may also be a helpful kick-start for your own client engagements “playbook”:

• • https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy
  • https://www.gartner.com/smarterwithgartner/cdos-must-take-the-lead-to-improve-data-literacy
  • https://www.gartner.com/smarterwithgartner/create-a-data-driven-culture-by-influencing-3-areas
  • https://www.gartner.com/en/doc/730277-how-to-increase-data-literacy-for-success-with-analytics
  • https://www.gartner.com/smarterwithgartner/build-a-data-driven-organization
  • https://www.gartner.com/en/publications/what-effective-decision-making-looks-like
  • https://www.gartner.com/webinar/4004157
  • https://www.gartner.com/webinar/4013201/player?commId=538536&channelId=5502&srcId=null&webinarType=UpcomingEvent