Championing Workforce Data Literacy

On July 15, over 150 Chief Data and Analytics leaders from North America’s top companies came together to exchange ideas, shared challenges, and best practices for establishing, implementing, and measuring the outcomes of data literacy programs. 

The panel was moderated by Fawad Butt, chief data officer for UnitedHealth Group, with Mano Manoochahr, chief data and analytics officer for Traveler’s; Rupinder Dhillon, chief data and AI officer at Hudson’s Bay company; Shanthi Iyer, chief data and analytics officer at Cisco; and Jayant Dhamne, chief data officer for Ecolab providing perspectives on effective data literacy programs in the workplace.

Defining Data Literacy

The key foundational element to a successful data literacy program is defining data literacy within your organization and ensuring a shared taxonomy throughout the organization. A large number of Fortune 500 companies have been in existence for 50+ years and over the course of time have gone through a tremendous amount of acquisitions. 

This results in a number of disparate systems, policies and definitions. In order to ensure an organization understands how critical data is to the business, it comes down to basic data governance to create the kind of top-down culture that enables the enterprise to speak and understand the same data language — and remain competitive. 
 

Establishing a Training Program 

The second, and more practical component of a successful data literacy program, is establishment of a training curriculum, whether that’s inclusion of certification and belts, or incorporating the right programs for your organization. Either approach ensures the right level of growth at the right levels of engagement at the right time. Although the immediate implementation of a massive governance program may not be the most productive starting point, an organization should be wary of traveling too far down the path of their data literacy journey before implementing or overhauling its data governance program. Starting small is a way to gain buy-in from all data users. 

One year into their data literacy journey, after undertaking the initial foundational work of integrating disparate systems into a central cloud system, one organization has begun offering office hours for new team members to learn what capabilities are available via self-serve analytics tools. Another organization that is further along in their journey invites new hires to rotate through programming to better understand a host of data-related questions for the end user, such as: Where is the data? How do I locate the data I want? What does the state of the data mean? This type of hands-on, embedded program builds out analytics capabilities within all business teams and prevents user knowledge from centralizing in one team.

Other components of a data literacy training program could include: 

• Establishment of a governance council with leaders from all functions and regions represented to drive relevant education to their divisions and functions 

• Focusing on narrative writing and data storytelling

• Incorporating training certifications into job profiles to establish individual qualifications for internal candidates

• Introducing scratchboards within the data ecosystem for individual business units to create their own analytics and share back into the central model across the organization, which has the dual benefit of alleviating silos — merchandising benefits from marketing’s insights; marketing from stores’ insights, etc.
   

Data Stewardship

Data literacy has real, long-term value. One organization formed a data governance council with two distinct arms: education and governance. The latter was created to ensure the company treats data like an asset, which means understanding data hygiene and the data lifecycle, forcing users to answer questions about creating, storing, sharing, securing and destroying data. Through this governance arm, it became increasingly obvious how important data stewardship is within an organization — and simultaneously showed the end-user how important their data is, and that they need to treat it with care.
 

Funding a Data Literacy Program 

Financing at a central level can solve the common challenge of funding a data literacy program. Realizing the value of early leadership buy-in and central program development, prior to committing thousands of others to the program, helped one data leader successfully fund their program. Although the conversation around funding can be complex, if the value is presented adequately, the funding equation solves itself. 

Another organization created a structured program in partnership with a university focusing on business leaders as the initial cohort and seeding the program from a central funding pool. They discovered that, through structured training, business leaders could be taught how to look at data capabilities as real enablers of the business in new ways. Every subsequent cohort was not funded from a central pool, but because the training was so highly recommended by the initial group, teams found the money to fund this literacy program. 
 

Measuring Program Success

There are several approaches to consider when measuring the success of a data literacy program, including:

• Benchmarking. An organization can measure the number of people who have gone through training, pulled data, and other numbers from a business value perspective. By calibrating your program this way, and repeating benchmarking at a later date, the organization is able to track the success of your program. 

• Evaluating Program Maturity. This approach enables the organization to better incorporate operational metrics like data quality and availability, and can be presented as: this is how you did it before, and this is how it is done now.

• Scorecards. These can be used to measure things like the availability of data, cycle time, speed and provisioning, data quality and data trust. 

• Efficiency Metrics. Teach your executives how much money can be made or saved through better use of data, and enable them to see the data through identification of cost savings and new revenue opportunities.
   

Qualitative Measurement 

Value comes in both qualitative and quantitative measures, and qualitative measurements are just as important as quantitative ones. People not only have to get trained, but they also have to adopt the processes and approaches that are being taught. When end-users can come back to the CDAO and say that their team is able to answer questions, and want to know the whys, it becomes a culture shift within the organization; it is only then that you can start to truly recognize the value of data literacy.

Qualitative methods for measuring the success of a data literacy program can include: 

• Breaking down silos, which can help enterprises realize the value of data-driven decision making

• Turning data into compelling stories — this can result in greater adoption throughout the organization

• Fostering internal talent to create superusers saves time and money, and the intrinsic knowledge of the organization's culture and processes becomes invaluable
   

Demystifying Data & Analytics for Measurable Business Value

Having the CDAO function at an organization does not mean the organization understands the role, scope of responsibility and the value that can be brought to the business. It is important to take the education and literacy journey seriously because it helps strengthen the team and the work needed to move the business forward. 

Key takeaways from this virtual session include:

• Chief Data & Analytics Leaders must be agents of change in order to demystify data by standardizing the language of D&A across the enterprise.   

• Ownership and stewardship of data are crucial for enterprise-wide data literacy and program success.  

• Successful data literacy programs must understand their audience, leverage technology and talent, and empower the business to make improved data-driven business decisions.  

by CDAOs, for CDAOs

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