Recently, I got a chance to publish a digital article for Analytics India Magazine. I picked up a topic – Democratization of data and AI, which organizations wish to do but often face resistance from internal workforce due to cultural reluctance and lack of robust strategy. For democratization strategy to flourish, it is important to understand what it is, why do we need it and who are the stakeholders. Well, if I talk about its success, its huge. It has led to the business transformation of enterprises like Continental, Airbnb, Netflix, Viacom, and many others.
Check out the full version of the article at Analytics India Magazine here. From the article –
“DATA DEMOCRATIZATION” MEANS “LIBERATING DATA” AND “GETTING DATA IN THE HANDS OF DECISION-MAKERS; IT MEANS PUTTING DATA SCIENCE TOOLS IN THE HANDS OF PEOPLE WHO ARE NOT DATA SCIENTISTS,” – Chris McGrath – SVP, Data strategy and consumer intelligence, Viacom.
Five point strategy to democratize data science includes below steps-
Democratization enables the sharing of tools, knowledge, and skills to consume data and back your decisions with data insights. In the current scenario, almost every industry hold large volumes of data and those who make sense of data succeed. While small central team of data and AI specialists bring standardization, democratizing tools and skills to generalists across the organization brings empowerment, builds culture of collaboration, and the ability to exploit data rapidly at scale.
Democratization of data and AI is a program whose benefits and impact are zealously debated by many enterprises. Remember, democratization is not for everyone but for those who have large volumes of data and the intent to impact business decisions with data insights. What plays the major role is the in such programs is the strategy to evangelize data, share toolkit, and transform culture. I hope I could write more on AI product management and leading & building data science teams.
Since the concept of data/AI democratization is new to the organizations that were accumulating data until now, I’m sure it’ll interesting to know which is the toughest knot in the chain.
Data-org 