By Manuj Ohri, Partner – Transformation C&O, KPMG in India and Sankara Subramanian, Partner – Lighthouse, Data and Analytics, KPMG in India

The second wave of COVID-19 across India has significantly impacted people’s lives and their ability to carry on as usual. This has led to unprecedented challenges for business as lives take precedence over livelihood. As we look forward to the future with a hope to emerge stronger from this crisis, the role of data and the right insight has become even more critical especially when a decision taken on the basis of incomplete or incorrect data can lead to wastage of critical resources.

As businesses navigate through this crisis, what are the learning areas that can help not only today but also in the future. We identified four areas that businesses can consider:

1. Move from static metrics, dashboards to actionable tools to identify internal levers

In this age of working remotely and with video call fatigue, it is no longer productive for people to spend time discussing data accuracy of dashboards or make cumbersome changes to dashboards while management awaits the data to make decisions. Leading companies are moving from dashboards to tools which allow for self service capabilities including simulations and what-if analysis.

Case in point: A leading global consumer company recently created a tool to improve transparency of fixed and variable margins across its plants in India and overseas. This immediately led to opportunities being identified from internal benchmarking. 

With more digitalisation of processes, companies are deriving insights leveraging advanced analytics techniques including AI/ML and process mining.

Case in point: A leading financial services company is able to analyse emails, chats and calls with clients to measure engagement levels and identify cross sell/upsell opportunities. 

2. Sharpen the external view, understand consumer/ channel partner requirements through behaviour and interaction tracking

COVID-19 has driven customers to online channels of interaction with companies. Leading companies today are particular about consumer experiences. Companies which are able to predict or at least identify problems early greatly improve the odds of being able to resolve issues and retain customers. Through advanced AI/ML tools, it is possible today to understand the sentiment behind customer interactions, predict problems and trigger recommendations which a human can then prioritise and execute.

Case in point: A leading auto company uses NLP (natural language processing) to understand sentiments online and based on feedback generated from customers. This system not only highlights issues but also helps company executives understand the sentiment behind a particular interaction thereby leading to more responsive customer connect.

3. Decision support systems to improve the efficiency of operational decisions

Business and operations decisions are often a combination of data analysis and heuristic/ experiential knowledge. Given the disruption caused by the pandemic, operational managers must spend more time to understand the on-ground situation as compared to before. Hence, even the same level of decision efficiency as before requires support in at least data collation and cleaning so that managers can spend an adequate amount of time discussing ideas with colleagues or assessing the on-ground situation. Decision support systems  range in their coverage from data collation, cleaning to AI/ML based recommendations. Cloud-based AI/ML tools can be built or are available off the shelf to support most operational decisions. Leading companies are taking advantage of these solutions to bring efficiencies to their business. 

Case in point: A leading foods company recently implemented a cloud-based tool to improve loading efficiency of cartons in trucks. Based on the size of the truck, the sales order and a set of business rules, the tool can recommend the most optimum loading pattern thereby improving overall efficiency by ~1-2 per cent. Further, this helped the operational managers to focus more on ensuring the availability of the right size of trucks.

4. Explore a Centre of Excellence (CoE) based approach to build capabilities and scale faster

Companies are increasingly realising that analytics and data management is an ongoing need which requires close working of diverse skillsets – from business to data science to cloud and data management. While some companies are able to attract and retain the required talent, a number of companies are today looking at external partners to help them setup, scale and operate an analytics CoE. A CoE based approach helps run the entire process from ideation to outcome delivery to capability scale up as a time bound project. This not only improves certainty of outcome but also an outside in view that helps balance realism and the art of the possible. Several companies are today evaluating different models of CoE setup from in house, outsourced or a BOT model.

In summary, the need of the hour is to integrate analytics and insights into business operations and decision making. Going forward, the success of any analytics intervention will be determined by the extent of integration with the businesses’ way of working and how seamless this integration is.