Ansar Kassim, Head of Analytics and Insights, Verizon
Describe your career to date
I started my career as a Technology Consultant in India and quickly rose through the ranks as a Project Head and Onsite Delivery Head with added responsibilities of Business Transformation Consultant and Solutions Architect. I then joined another consulting firm as the Global Head of a Business Unit, with end-to-end responsibilities including sales, delivery, quality, and P&L.
At Verizon, I held multiple roles across Technology, Mobile, Digital Marketing, Finance and Data. In 2018, I joined as the Head of Consumer Analytics and was promoted to the Head of Analytics & Insights at Verizon, where he currently leads a team of up to 150 data analysts and data scientists.
I earned an MBA from University of Warwick, UK, and my Doctorate from Temple University, Philadelphia, US.
Data literacy is a key enabler of the value and impact from data. How are you approaching this within your organization?
Data literacy is a key enabler of business value. Most people who need to make a business decision are non-technical. Our approach is to:
1. Make data assets easily accessible to business users who need that data.
2. Simplify the complexity of advanced analytics, make advanced analytics accessible to business users through tools and train business users to the tools.
Our team at Verizon deployed a self-service system that allowed end users to directly interact with data without the help of SQL, eliminating more than 95% of the static KPI reports and 80% of dashboards. This solution was rolled out organization-wide successfully.
We developed automated and interactive simulation and planning tools that leveraged the historical relationship between multiple drivers to generate granular outcomes and forecasts. These tools simulate multiple scenarios generation business cases with full-blown financials for a quicker end-to-end decision process.
The impact of this was:
1. Adoption: 20,000 users with over 3,000 daily unique active employees across our toolset.
2. Efficient planning cycles: There has been an 80% reduction in turnaround time through automated forecasts.
3. Improved accuracy: Forecast accuracy improved significantly when compared to legacy methods.
4. Investment analysis: Our toolset can quickly identify and test win-win-win solutions.
5. Eliminated silos: The tool allows data from disparate sources to be assimilated for AI.
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