In an Interview with BW CFO World with Tejas Mehta, VP Finance, Mondelez India, shares insights on empowering strategic decision-making and fostering a data-driven culture
In your experience, how can advanced financial analytics be most effectively leveraged to empower strategic decision-making within an organisation?
We are increasingly using advanced analytics to support decision-making, by making information available faster, generating alternate scenarios and discovering subtle trends.
The areas that analytics is most effectively being applied in finance, is planning and forecasting, management reviews and resource optimisation. We’re also exploring its potential in risk management and sensitivity analysis.
We see advanced analytics unlocking value at two ends of the spectrum, firstly, in improving the speed and accuracy for high-value decisions and secondly, in automating the low-value routine decisions.
For instance, our demand planning employs machine learning models with varying accuracy thresholds based on product value. Top 20 per cent products benefit from higher accuracy, supplemented by input from demand and finance planners, while bottom 20 per cent products rely on automated baselines with minimal manual intervention.
How would you approach fostering a data-driven culture within the finance team and how would you encourage collaboration with other departments to utilise financial analytics for better decision-making across the company?
Fostering a data driven culture will always be a continuous effort. Our focus has been on building four key pillars; leadership buy-in, data skills, data quality and collaboration. Senior management is encouraged to promote the use of data and reward data-driven behaviour. Investments in data skills such as basic statistics, data analytics tools, user adoption and the right storytelling. Managing data quality and standardising data definitions, builds trust and reliability of the results. Collaboration with and between IT teams ensures smooth data integrations and access to necessary systems.
Whereas complex analytical projects are still largely IT driven, the finance, supply chain users are slowly getting self-sufficient in building their desired models and dashboards. Today a good per cent of our users have working knowledge on different analytical tools. Some of them can even make their Power BI Dashboards.
Few ways that have helped improve collaboration with other departments are, tailoring dashboards that speak their department’s language and benefits, creating cross-functional teams that can provide a more holistic view of the business, making access to finance data easily such as through self-service analytics, data and report catalogues. creating a data hub with strong governance and focus on leveraging it appropriately.
What are your views on the role of technology and financial analytics tools in optimising financial processes and generating valuable insights?
Advancements in technology have tremendously reduced barriers to execution, in terms of adoption, cost, speed and scalability. Advanced data management and visualisation have led to faster adoption and flexibility. Low code platforms have enabled self-service and citizen development. Cloud computing has made tools easily deployable and scalable. Interoperable systems have enabled sharing of data across systems, processes and departments.
These have made analytical tools as pervasive as spreadsheets and presentations in the life of the analysts, if not replace them in their day-to-day work.
How would you integrate advanced financial analytics with existing financial processes and reporting structures to ensure seamless information flow and efficient decision-making?
A brief roadmap on how we try to integrate financial analytics to our processes is to start by mapping our existing financial processes, data sources, data flow and reports. Evaluate the quality and consistency of our data and systems. Describe what insights we want to gain. Look for a platform that seamlessly integrates with our existing financial systems and caters to our specific needs. Do not try to overhaul everything at once and allow for a smoother learning curve and adoption for the users. Regularly assess user needs and adapt the approach based on their feedback.
In your opinion, what are the emerging trends in financial analytics and how can companies leverage these trends to gain a competitive advantage?
The financial analytics landscape is constantly evolving and I do not want to hazard any guess on a specific tool or technology. One can only try to learn and unlearn and keep evolving.
To pick a few emerging trends that we think helps are:
ML / AI
The ability to analyse massive datasets (big data) is revolutionising finance. AI / ML algorithms can synthesise this data to uncover hidden patterns, predict future trends, and automate tasks. AI / ML can be used to manage risks, personalise products and services, or optimise operations.
Real-time Scenario Analysis
Financial analysis is shifting from historical data to real-time insights. This allows for quicker adjustments and more proactive decision-making. Scenario Analysis can be used to test different strategies, mitigate potential risks, and respond to market fluctuations.
Cross-functional or Integrated Analytics
Breaking down data silos and integrating financial data with other departments’ data (supply chain, sales, marketing) creates a more holistic view of the business. Resource allocation and collaboration can be improved across departments. For example, customer segments with higher profitability can be calculated combining sales and financial data.