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Building resilience through data-driven decision making

(Image credit: Image source: Shutterstock/alexskopje)

Six months ago, nobody could have expected the global business world to be evolving as fast as it is. To cope with the unprecedented impact of Covid-19, most organizations have had to drastically flex their business model and operate in new ways. We’ve seen the critical role of technology more urgently than we ever anticipated in our daily lives. And the successful recovery from locked-down economies will require us to maintain this increased level of adaptability for a long time yet.

To fuel bottom lines in the months ahead, we can expect companies to look to innovation more than ever. From digitization and automation, to AI and advanced analytics, technologies that have accelerated as a result of the pandemic will undoubtedly play a big role in driving growth in 2021 and beyond, enabling companies to drive operational efficiencies at scale.

But crucial to building business resilience is also being prepared for future disruption, able to sense and respond accordingly. That’s where the importance of building intelligence-based capabilities comes in. In the same way that we had to reconstruct our financial and risk models after the 2008 economic crisis, organizations today will have to reassess their use of AI and analytics to ensure our businesses are resilient enough to withstand further shock from future black swan events.

Better data, better models

The new differentiator in business will be our ability to collect, organize, analyze and react to data. The radical changes in professional, personal and societal routines have resulted in unprecedented shifts in consumer behavior. As a result, the historical data that fed many of our analytical models has quickly become out of date, incomplete and unsound. Organizations will look to conduct data and model audits to identify errors and weaknesses in operational, financial and risk areas.

A new emphasis on data governance is required to underpin this. We must take a forensic approach to real-time data capture across all forms and formats, both internally and externally. I also expect to see a new cloud-first business model become the standard. Cloud computing allows for data to be verified and stored securely, while still being available across multiple zones for deep analysis and insights.

Analytics at the core of operations

Prior to the pandemic, there was a frequent disconnect between an organization’s analytics and its strategic priorities. Now, even non-digitally native companies must put analytics at the very core of their operations. Accurate and timely data will form the backbone of all business units, from sales and performance forecasts, to procurement and supply chain optimization.

Achieving this will require organizations to develop an analytics-led culture. This means not only including analytics leaders in all strategic discussions, but also embedding data-driven decision making from the ground up. I’ve seen first-hand how Covid-19 has created flatter, more agile organizations with empowered frontline employees. Having such dynamic teams is often the most effective and scalable way to build resiliency into business operations. However, also important is to equip these employees with the analytical tools they need to make autonomous and informed decisions.

Greater adoption of digital twins

Digital twins can improve organizations’ predictive powers while reducing cost of service. As we emerge from the pandemic, we will therefore see more businesses turning to digital twins of their supply chains to better prepare for unexpected shocks and to build an intelligent and resilient ecosystem. However, with the increase of real-time data from rapid digitization, there’s a very real possibility that digital twins will be used not just in supply chains and manufacturing, but throughout all modern businesses. Using these models, we can experiment with a number of key variables, testing different scenarios and contingencies to proactively mitigate risks and extend capabilities across research and development, engineering, product management, and even sales and marketing.

Transforming the customer experience through automation

With consumer call volumes surging during Covid-19, the limitations of focusing only on human interaction in customer support systems has become clear. Post-pandemic, we will inevitably see a rise in automation in this area, with tools like voice biometrics and natural language speech recognition freeing up agent time and improving customers’ ability to self-serve. These are moving chat interactions into the mainstream, ranging from simple virtual agents to fully automated personal advisors.

In addition to this, analytics will play a key role in enhancing the customer experience. By collecting data from a wide range of touchpoints, companies can gain a 360-degree view of their engagement levels. This is where AI seems particularly transformational – helping to pinpoint customers who are likely to leave or those that need additional support, and finding the best offers to retain or assist them.

Calculating risk through synthetic data

Even the most well-honed and calibrated models can quickly lose their predictive power in the midst of black swan events. For example, financial models that use time-series, oil-price, or unemployment data will need to be rebuilt entirely. Several organizations are looking to increase the quantity and diversity of data available by using machine learning techniques to generate ‘synthetic’ data for model development. These vast sets of realistic data can be used to calculate risk measures, train predictive systems and stress test portfolios. Much of this work is still at the research stage, but I expect to see a growing interest in this area as we grapple with the impact of unprecedented events like Covid-19.

No industry has been left untouched by the global pandemic. It has undoubtedly forced business leaders to reassess their analytics capabilities and accelerate their journey to digitization. But to gain greater intelligence and build lasting business resilience, leaders should look to embed data-driven decision making into all levels of their organization.

Andrew Duncan, Partner and UK Head, Infosys Consulting