#40 ServiceNow's Vijay Kotu on the Power of Micro Decisions and Aligning Data to Business
Vijay Kotu is the SVP of Data and Analytics for ServiceNow, a company that is helping enterprises manage digital workflows. In this episode of the Data Chief, Vijay discusses how he is building a high-growth “mathematical enterprise” where frontline workers are empowered to make smarter business decisions with data and AI. He also speaks about the impact of ecosystems, the need for businesses to have a holistic view of their data in order to create positive outcomes, and why being intentional about analytics use cases is absolutely essential.
Key Takeaways
Don’t underestimate the impact of micro-decisions: We all want to be more data-driven, but don’t fall into the trap of thinking that data and analytics can only be applied to once-a-quarter, boardroom-level decisions. Enabling frontline employees to be more data-driven in their everyday work is a hugely powerful way to make a positive impact across your entire business.
Evaluate how data can improve workflows: The holy grail of analytics is converting insights to action. One of the most effective ways to do this is by automating workflows whenever and wherever possible. With automation, you help everyone in the business be more efficient without adding any extra work or manual decision-making.
Data becomes exponentially more powerful when it’s connected: Having all of your proprietary data in one place is a great way to start your data journey but it becomes exponentially more valuable when you connect it to outside data sources. Bringing together multiple sources of data gives you an even richer insights about your customers, employees, and products.
Data serves the business: At the end of the day, your data goals should align with that of businesses. Data and analytics professionals must remember that data is there to serve sales, marketing, product, IT, etc. into making better decisions for the business. They are the ones running the functions and the data and analytics teams are the backbone of that. Therefore, data teams should be designing products with that in mind.
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