Sham Kashikar, Executive AI Advisor and Chief Data and Analytics Officer at The AIAG, speaks with Gavroshe Founder Derek Strauss, in a video interview about embedding data literacy in organizational culture, a use case of making data discoverable and actionable, and the role of the data leader.

Kashikar emphasizes the critical importance of not only training employees but also truly integrating data literacy into the organizational culture. He underscores that while training is a necessary component, it is far from sufficient on its own. 

Organizations need a broader strategy that is embedded in their day-to-day operations, says Kashikar. He highlights the importance of tailoring data literacy efforts to the specific needs of different functions. He also notes the need to “understand your organization, how people work, and embed the data thing into the fabric of their work.”

“You need to define your literacy program. You need to define the objective of the program and make sure that you understand the organization’s needs,” Kashikar adds.

Adding on, he points out that data literacy cannot be one-size-fits-all. From marketing to engineering teams, everyone’s needs are different. This diversity in requirements calls for a standardized but flexible plan that respects the unique needs of each team.

To drive effective implementation, Kashikar advocates for:

• Tool and technology standardization: “There could be tools and technologies that need to be standardized and accessible.”
• Leadership role modeling: “Leaders should be telling the people in their organization why we are doing something and the benefits, and also incentivize the team.”

Embedding training at scale

Scalable training solutions are crucial to ensure consistent literacy across the organization. According to Kashikar: “Once we have those things in place, then you can have role-specific training. That too can be implemented in a scaled manner.”

He suggests embedding data literacy into onboarding processes, tailored to the roles of new employees, as a way to scale efforts effectively. In continuation, Kashikar stresses two often-overlooked pillars of data literacy: trust and accessibility. To build trust, he recommends investment in data quality, cataloging, and effective data management. He also urges focusing on eliminating friction in data access. 

Making data discoverable and actionable

Sharing an instance where an organization has been successful in creating a data-literate workforce, Kashikar highlights the Wiki program and how information has been made easily available for someone who wants to know. He explains that employees could navigate the Wiki with ease, regardless of their query. 

From customer acquisition to product adoption, everything is structured and tagged with metadata. This metadata, powered by back-end technology, allows users not just to find content but to act on it.

Personalized, role-based experiences

What set the Wiki system apart was its built-in intelligence to personalize the experience based on user roles. Kashikar illustrated this with a use case: “If I’m a marketing analyst, when I click on anything like cross-sell, upsell, or new customer buying prediction, it understands I’m a marketing analyst, and it will take me to the respective system and provide me the insights that are available and accessible to my role.”

This meant that marketing, engineering, or sales professionals could each have tailored access to the insights most relevant to them. Underlying the system were core principles that ensured the program’s effectiveness, says Kaahikar. This includes information, accessibility, and discoverability, and its integration with business processes to make it actionable. 

Kashikar criticizes fragmented systems that force users to jump between dashboards and tools. However, he acknowledges that their approach was successfully implemented and scaled.

From point solutions to ecosystem thinking

AI has become a staple in business conversations today, and Kashikar sees this growing interest as a positive sign of progress. While this widespread awareness is a good starting point, he cautions that focusing solely on models and technologies only scratches the surface, or can provide a quick win.

To move from quick wins to lasting impact, Kashikar believes that data leaders must take on the role of integrators. He says, “The data leaders need to consider themselves as facilitators or connectors where they have to take a look at the entire ecosystem and how they leverage this ecosystem to create the greatest business impact which is sustainable as well.”

This means thinking beyond technology and engaging in thoughtful orchestration of people, processes, infrastructure, and partnerships. Kashikar likens this to conducting an orchestra.

He says, “In the orchestra, the master has to really make sure that every player is playing to the tune.”

Concluding, Kashikar says that while there are a lot of risks involved, learning from that is important. He adds, “Data and AI programs are most effective when you take advantage of the entire ecosystem, internal as well as external.”

CDO Magazine appreciates Sham Kashikar for sharing his insights with our global community.