Expert Talks with Maavrus | Analytics, AI and Transformation

In conversation with V Ganapathy, Vice President and Head – Global Advanced Analytics CoE, Holcim | Expert Talks - 12

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Cross-industry knowledge/exposure could be a great asset for Analytics & Transformation leaders. Your customers are getting influenced not just by particular competition trends in your industry, but also through touchpoints from other industries.  So let's say you are in a B2B company and are interacting with a client’s Procurement manager. That person in his personal life is shopping online, and is exposed to single-click ordering, real-time delivery status updates and feedback collection etc. It is very normal that the same person now wears the hat of a B2B customer and then expects a similar experience from you as a  supplier. - Excerpt from the Expert Talks interview with V Ganapathy

 

Today is Episode 12 of the Interview series on Expert-Talks, with Leaders in the Analytics, AI and Transformation space.  For this episode, our CEO Mahadevann Iyerr (Mahaa) is in conversation with V Ganapathy, Vice President and Head of Global Advanced Analytics CoE at Holcim. Prior to Holcim, Ganapathy was Senior Director and Head of Business & Enterprise Analytics at Philips. Previous to that, Ganapathy has worked at  Dell, AOL, Ford and MRF. He is a thought leader who frequently speaks at AL & Analytics Forums.

 

We are sure you will enjoy listening to Ganapathy and his perspectives. A few key points from the interview :

 

  • For analytics to be impactful, there are a few factors which are important. Firstly Culture, secondly AI & Analytics ecosystem Capabilities and thirdly Domain knowledge & Business connect.

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    • Culture should encourage continuous learning of new research and best practices; an experimentation approach to continuously test & learn and fail fast/cheap; and a curious mindset which is scanning the status quo to identify new opportunities.

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      • AI and Analytics ecosystem capability that covers agile ways of working, aligning and executing to business priorities; secondly data assets that are clean and harmonised to identified initiatives; platforms that enable rapid experimentation and flexibility in seamless scaling; embedded analytics capability in each business unit; and the governance to track the performance linkages based on the above constituents.

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        • One of the decision points for AI leaders is about buy vs build. What are those industry-agnostic models that can be customised and deployed quickly, and what needs to be built grounds-up? A great approach is to do the feature engineering in discussion with the business. This is where domain knowledge and the ability to engage business stakeholders become extremely important

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          • Both quick turnaround, as well as strategic projects, are important. Quick wins help build business confidence in the AI teams, which is important to get investment to support the long terms strategic ambitions of the AI team for the business. Quick wins also play a role in bolstering the confidence of the Analytics teams, which is important for them to pursue a mindset of curiosity and experimentation.

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            • When prioritising investments in data ecosystems & AI projects, using a 2 X 2 of Data Intensity Vs Enterprise Value opportunity of that function /process, is a great approach. Ideally where both data intensity and enterprise value is high, is a good sweet spot.

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              • Cross-domain projects typically provide great monetary value as well as strategic leverage. A customer journey value stream, is a great way to identify such projects. Having said that such projects could be complicated given the need to establish multiple stakeholder buy-ins and possibly differing levels of data availability & maturity across the functions.
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                Expert Talks with Maavrus | Analytics, AI and TransformationBy Maavrus