Novel Approaches for a Scalable and Effective Data Literacy Program
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Novel Approaches for a Scalable and Effective Data Literacy Program

According to Gartner, Data Literacy is the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application, and resulting value.

The above definition from Gartner is pretty comprehensive and has various important elements that often get overlooked while running the Data Literacy programs.

Let’s talk about some of the novel and effective approaches the organizations can take to succeed, after implementing their Data Literacy framework.

1. Create a group of infinite and explicit learners –

Identify key individuals in your enterprise who are infinite learners and have a killer passion to share the knowledge with their peers and external communities. These individuals have infectious energy around them, command a lot of respect internally, are quite active in the external professional forums, and demonstrate a lot of perseverance when it comes to managing their day-to-day operations.

Call this group of people your Data Literacy Champions and have a dotted line to the Chief Data Officer within your organization.

2. Create a culture of experimentation –

Among the various responsibilities of this core group, the fundamental expectation would be to create a data-driven culture of experimentation; and bring transparency, responsiveness, predictability, and speed across the organization’s value chain.

The core group would meet fortnightly and plan out the series of experiments that need to be conducted in the coming quarter. These experiments would be driven by this core group and executed by the nominated individuals in the various business functions.

The idea behind running these experiments is to challenge the status quo and look for opportunities to optimize the operational workflows by leveraging the incumbent technology infrastructure and available data.

3. Decentralize your analytics function –

Even today, more than 75% of organizations have a centralized Analytics and BI function which needs to be dismantled and restructured into a HUB-AND-SPOKE model.

This data democratization would allow the analysts and data scientists to be positioned in different business functions as a frontline team and be equally responsible for delivering on the operational KPIs.

This would enable them to empathize with the business challenges, and their day-to-day frustrations with respect to data.

4. Set up a closed-feedback loop and productize your process and operations knowledge –

In any organization, a large percentage of valuable knowledge sits in the middle management layer and unfortunately, a significant percentage of this knowledge resides in the conscious and sub-conscious mind of the workforce and hardly get productized and leveraged to its maximum potential. This knowledge is also extremely vulnerable because it is subject to employee attrition.

By decentralizing the Analytics and BI function, and creating a Data Literacy Champion group, we can set up a closed-feedback loop among Business, IT, BI and Advanced Analytics functions, which can be leveraged to create an organization-wide Knowledge Graph (Ontology Framework) which can sit on top of the enterprise data bus and can enable the Advanced Analytics teams to bring additional context, relevancy, and actionability in their insights.

Process and Operations knowledge

5. Augment your data stories for better adoption –

A significant percentage of Data Stories communicated to business users and executives are factual and lack relevancy, context, and actionability.

What business is demanding is Speed to Actionable Insights, Faster Action TAT, and Self-Service BI Adoption, and these expectations can be met by investing in an AI-powered augmented analytics solution (e.g. Course5 Discovery), which promises proactive anomaly detection, causal insights, data simulations, and prescriptive recommendations.

Since 2018, there has been a massive investment in the Augmented Analytics category, and it’s going to get a decent percentage of the digital budget in the coming decade.

6. Develop product mindset when it comes to data and its consumption –

Business users and executives are living in a highly complex hyper-connected world where their needs are evolving rapidly. They have embraced the idea of context-switching; they expect speed, relevancy, and actionability in whatever they consume; and more than anything, they expect an analyst-on-demand.

To meet the needs of this audience, corporates need to develop a Product Mindset when it comes to their data and start looking at the data beyond dashboards and scorecards. This is a time to invest in building point Solutions and Apps that focus exclusively on the unmet business needs to make the consumption process easier by publishing the insights via Chat, Voice, Search, Portals, Mobile Apps, WhatsApp and Email, and expedite the decision-making process.

These are some of the critical initiatives organizations can take to formulate an effective data literacy framework, deliver a scalable digital literacy program, and develop an organizational culture where insights are prioritized and business value is generated through data-backed decisions.

Sushant Ajmani
Sushant Ajmani
Sushant has over 20 years of experience in the digital analytics industry with a strong background in Customer Analytics, Marketing Analytics, and Logistics Analytics functions....
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