Interview with Sachin Pendse, Data and Analytics- Global BI Modeling/Architecture and Reporting Hub Lead, Mondelēz International

5 Min Read. |

Sachin Pendse is an engineer by qualification and has over 19 years of IT leadership and management experience. He has worked in leading organizations like L&T, Accenture, Tech Mahindra, Cadbury and Mondelez International where he has successfully led and implemented serval Business Intelligence and Analytics Programs which involve descriptive, diagnostic and predictive analysis in supply chain, sales and marketing areas. Last 5 years is where every initiative in every organization is having data at its central point and that is where Sachin feels happy to see that potential (golden mine) of data is been understood.

How did you get into Data Analytics? What interested you in learning Data Analytics?

Sachin Pendse: Have been leading multiple Business warehouse/intelligence initiatives in Chemical/ Mining/Product companies and data was always at the core of it. But, the marriage of analytics with data happened in 2008-09 when one of the fortune 500 chemical company from North America had asked to structure their BI Analytical solution to satisfy growing business demand with respect to ROI for BI.

Interest in learning Data Analytics – Passion was always to look for mystery/resolution/prediction which can help in making decisions for improving numbers (bottom/top). Also, banking domain background of my father did also influence my journey in this space further.

What was the first data set you remember working with? What did you do with it?

Sachin Pendse: My first data set was inventory cross-regional/market level data which was used to generate Stock overview, Inventory ageing and other reports. It was descriptive analytics.

Was there a specific “aha” moment when you realized the power of data?

Sachin Pendse: My first aha moment was when I executed successful BI transformation which has resulted in establishing “Single Source of Truth” for primary and secondary data in supply chain processes in Sales & Distribution, Procurement and Planning space. In turn, increased utilization of data significantly.

What is your typical day-in-a-life in your current job? Where do you spend most of your time?

Sachin Pendse: Review global strategy for Data & Analytics from modeling standpoint for a number of projects which we run in multiple regions/markets. Provide help in reviewing business growth via different analytics tools. Also, look after Global Reporting hub platform which helps business in avoiding any manual generation of analytics.

How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?

Sachin Pendse: I am connected with a lot of Data Analysts on the ground to understand/learn facts. Along with that, I do attend Gartner/NASSCOM conferences. Besides, I also read through Data Science journals/books.

Share the names of 3 people that you follow in the field of Data Science.

Sachin Pendse: Bernard Marr, Andrew Ng, and D J Patil

Team, Skills and Tools

Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?

Sachin Pendse: I use Tableau, Business Object, and Power BI. In my team, many other tools like Alteryx, R, Lumira have been utilized.

What are the different roles and skills within your data team? Is it mostly Alteryx? You are SAP shop, is using SAP tools like Lumira most popular/widely used?

Sachin Pendse: Data Modelers (Supply Chain, Sales, Marketing, Big data, Finance), Visualization Experts, Data Analysts, Architects. We have now evolved into open source space as well along with SAP. We do use many SAP tools like Lumira, BO along with Big Data Visualization tools like Alteryx, Power BI, R. These are determined based on business requirements.

With SAP shop and using Alteryx, where do you see the application of open source based development like R?

Sachin Pendse: With a lot of unstructured/structured non-SAP datasets there is a need to handle them via solid ETL mechanism. That is where exploration in tools like Alteryx/R happens. Also, for building statistical models on such unstructured data, R would become important in near future.

How do you measure the performance of your team?

Sachin Pendse: 360 degree feedback cycle. On time/on budget delivery and mainly scalable/robust design for data remains a critical KPI.

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Advice to Aspiring Data Scientists

According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?

Sachin Pendse: Things will remain unclear/vague during initial period but never lose vision – “end state”. Achieve things in an agile way in bits and pieces and join them together. Patience is the key. Technically, look for any latest open source technology like R, SAS, Matlab, Spark etc.

How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?

Sachin Pendse: Everyone has to start with data and get hands dirty. Try to practically swim through and you will learn more faster. But along with that, learn “Business/Domain”. Without understanding data functionally it limits your reach to business.

What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?

Sachin Pendse: 

  1. Expertise in technologies which are in ERP space and Big Data Open Source space.
  2. Get control over one domain/business function along the way.
  3. Open source is changing fast so maybe master of all not possible but keep yourself up to date on new things coming in the space.

Need to learn tools/technologies but get into the habit of understanding data in business language. Keep in mind AI is future.

What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?

Sachin Pendse: This space is moving too fast and catching up will remain a challenge. AI will remain top of the list in near future. Do concentrate on playing with data which generates actionable insights by using latest available technologies. AI, IoT, Big Data would remain the buzz words.

Are you inspired by the opportunity of Data Analytics? Start your journey by attending our upcoming orientation session on Data Analytics for Career & Business Growth. It’s online and Free :).

Disclaimer – All the views expressed here are personal and not official. 

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