Finance as Business partner with Data Analytics
In conversation with Charulatha, Correspondent, Siliconindia Magazine. Sachin shares his views on key pillars of Data Analytics in addition to Data of course and the importance of Data visualization and what tools can be used to have right visualization
You are a chartered Account and lead the Business Analytics team as part of finance organization– How did this function start
Everything starts with a great story and for us also it was an interesting one. When COVID struck in 2018, there was lot of uncertainty in different parts of business with respect to hiring, customer pipeline, productivity, Delivery, and collection of money. We as some members of finance team pulled out 2008 data during sub prime crisis on what happened that time and gave some interesting insights in terms of time to proposal conversion, time to collect cash or cash discount to be offered, employee attrition. Though the scenario was not same but similar, it helped business to study data and make some decision and handle the situation well. This lead to establishment of a full fledged data analytics in finance supporting business
Unveiling the Power of Business Advanced Analytics: Revolutionizing Organizational Insights and Strategies
Lot of organizations is investing NLP, Machine learning, python and cognitive capabilities with Data visualization tools to establish a robust advanced analytics function. However, we have kept it very simple. For us, Analytics is also about a business problem and falling in love with that problem. Further then tools don’t matter, it can be as simple as excel with visualization in a Power BI or Tableau to provide insights on business problem. The insights should help make decisions and solve the problem. The data visualization has to be provoking enough and very action oriented for business to use it.
Lot of organizations is investing NLP, Machine learning, python and cognitive capabilities with Data visualization tools to establish a robust advanced analytics function
What are the key pillars of Data Analytics in addition to Data of course?
The most important aspect of Analytics is having a team with right mind set. You may have people with great skill set around excel, programming or visualization tools but if you don’t have people with right mind set who can understand what business wants top down and then work bottom up to understand and normalize data then we are not there yet.
• Another important pillar is foundational data with single source of truth.
• Use case owners and write sponsorship of use case is very important.
• Finally, trial and error is very important but fail fast and fail cheap and keep learning on the use cases
How is Data visualization important and what tools can be used to have right visualization?
Once you have understood the problem – the end state data visualization can be a phased delivery. We have come across situation where business only knows the problem but is not able to visualize what data presented in what way will help. Everytime you develop a visual, we need to ask ourselves and our sponsors – is it helping taking action or solve the problem – if the answer is no or Partial No – then keep improving based on feedbacks. There are various tools available in market – while for finance the simplest is excel but tools like Microsoft Power BI and Tableau are widely used for trend analysis and interactive discussions.
Can you give some examples on powerful Data analytics use cases which are visible in industry?
We need not go to any industry but our Govt. is doing great job in use of data in lot of areas. One of the best examples which is normally given is The Tax department. Look at the way, they have evolved—PAN number is the common key between GST and Direct tax – Infosys is the common vendor between GST and Income tax to design architecture and finally they have been using data to prevent Revenue leakage very effectively using initiatives like AIS, 26AS, eway bill, invoicing and faceless assessments. They are now auto populating your income tax returns based on data.
"Develop your network within and outside the organization – it helps validate your use cases and do smell tests"
Another great example is Account aggregators like CAMS who act as a bridge to share data from Mutual funds, Banks or Insurance companies with portfolio advisors or wealth advisors to help planning or provide you capital gain statements or automated KYC
How should one groom talent for this kind of function?
Developing mindset is very important and hence a CFO should encourage their teams to closely work with business to develop this mindset
• A culture of brainstorming / experimentation helps develop critical thinking
• Develop your network within and outside the organization – it helps validate your use cases and do smell tests
• One should avoid analysis – paralysis and reconciliation but provide directional view to business
• Developing case studies for both success and failures helps proper socialization and learning.
• Finally exit mode is important – once a use case is industrialized – the delivery should be managed by a separate team or handed over to use case owner to preserve creativity
Again, everything starts with a problem and post one year of COVID, the software industry had phenomenal growth. With growth, attrition was big problem and we were asked to develop an Attrition prediction tool. We developed a very simple tool based immediate past / distant past data behavior like night shift exits, high performer exits etc. and then extrapolated on current data to predict. We further improvised this tool using machine learning once we had the data for last 5 years which improved predictability.
Data security and compliance like GDPRM
Since the team doing Business analytics is privy to lot of organization data. Having right approvals from Data protection officers with proper controls and security is very important. Having data backups and following proper data storage policies will ensure that right trust and standards are maintained