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Recovering from COVID-19 will involve finally fixing your organization’s data backbone
In a recent HFS study surveying 631 enterprises, smart analytics emerged as a top three spending priority, and AI was among the top five technologies (Exhibit 1). Business leaders must prioritize data and analytics investments toward better customer handling, forecasting and business planning, and operational process re-design and improvement for any chance of success in the post-COVID-19 business environment.
COVID-19 is forcing the transition to self-serve analytics
Dealing with COVID-19, most enterprises that put initial measures into place to keep the lights on must now plan for performance and growth during potentially extended quarantine conditions and in the post-COVID-19 world. As an example, consumer electronics retailer Best Buy initially announced store closures and curbside pick-ups for customers in March. Only a few short weeks later, it is now having to announce further operational changes as survival measures.
Exhibit 1: Smart analytics and AI are among the five top technologies to tackle COVID-19
Source: HFS Research April 6, 2020
Sample: Coping with COVID-19 study, 631 major enterprises
Making these tough but crucial business decisions and keeping up with fluctuating market needs will define our new business reality. Enterprises need all their employees, across operational layers, to have the best possible information and data to make these calls with self-serve analytics. They also need to become more responsive, all while dealing with broken processes and challenges in infrastructure due to distancing. The time for fixing the data backbone and investing in smart analytics is finally upon us, and enterprises realize that these technologies have never been a “nice to have,” but instead a necessity.
Three business priorities for analytics right now: business planning, customer handling, and re-jigging core ops
HFS sees three business priorities getting pushed to the forefront as enterprises grapple with the many implications of coronavirus on their operations.
- Forecasting and business planning needs wider datasets to predict better:
Supply chain and finance departments have long relied on traditional statistics to undertake forecasting and business planning activities. Existing models will go out the window as we deal with unprecedented disruptions to supply chains and business environments due to COVID-19.
- We see the need for better analytics from the CFO’s office to improve working capital, liquidity, and risk management decision making.
- Similarly, supply chain leaders need an infusion of more datasets and better analytics techniques, such as scenario modeling, to prepare for dramatic supply and demand shifts. In a conversation with Glenn Finch, Global Leader, Big Data & Analytics at IBM, he brought up an example of how this is shaping up, “We recently built a COVID-19 recovery index using machine learning and deep learning, initially focused on county-level data for North America.” The models IBM has developed ingest a wide variety of datasets and can predict demand sensing at a granular level, allowing CPG clients to have more guidance on promotion strategies, product availability, and forecasting. Most importantly, the outputs are integrated into existing systems, so planners can holistically look at how outlets will perform, at the county level.
- Customer-facing services need all the AI-enabled help they can get:
Many industries face an urgent need to augment their ability to serve COVID-19 related customer queries. Healthcare, government, travel, hospitality, and ecommerce firms continue to be the hardest hit, although we expect spikes in demand across many industries (e.g., auto insurance customers calling about reducing premiums). The contact center industry faces a challenging crisis along these lines, and it sees a sharp need for smart analytics and AI. The use cases include creating digital self-service interfaces to reduce incoming calls, giving agents access to more customer data, and investing in digital associates that are conversational, cognitive, and context-enabled to respond to queries.
This is an area where IBM sees exponential demand, as Glenn outlined, “We have over 100 State, Local and Commercial clients that have engaged with us to deploy Watson virtual agents and messaging, that are pre-configured with CDC-sanctioned COVID-19 information. These pre-trained and customized agents are being used to address the overwhelming amount of information coming to hospitals, health systems and public systems in particular.”
- Operational process simulation and re-design will become a top data and analytics priority during and post-COVID:
As the world transitioned to working from home over the last couple of months, we exposed the fragility of our business processes, held up by antiquated legacy systems, manual fixes, and unsustainable workarounds. Many enterprises are taking this time to re-visit how their core businesses processes work, screening for the highest risks and exposures, and simulating and implementing new workflows that are executable with remote teams, cut costs, and improve results.
Gero Decker, CEO at process mining firm Signavio is seeing the effect of applying analytics to operational processes. He commented, “In the last few weeks we have seen our clients use extreme creativity and mass mobilization of people to change and collaborate. Now we see more of an uptick of process mining because the moment you switch to the new normal, you want to get back to monitoring your operational metrics and understand how you are delivering. We are part of the response to COVID-19, be it SCM re-configuration, setting up production in a different way… we are also working with a leading hospital who is fighting COVID-19 with a low mortality rate. We are the platform for re-configuring or re-publishing guidelines for operating procedures to manage capacity demand for clinical pathways, emergency services, and ICUs.”
These are the biggest pushes in enterprise analytics in response to COVID-19. However, this is not to say that other use cases for analytics are falling away. Each industry is feeling the pain in a different and nuanced way and will need to apply analytics to solve their unique business problems.
The Bottom Line: The time to drain the data swamps is upon us. Enterprises must gear up every employee’s response times with data-backed insights to anticipate and respond to the new abnormal that COVID-19 has prompted.
The number of variables that impact your business will continue to grow as we find out just how interdependent our economies, borders, supply chains, partners, and customer communities are. AI and analytics can start to help enterprises plan and predict with a foundational base in data, and a feedback loop can layer in iterations based on how things change over the next few months. In a time where the speed of response can make all the difference, we simply cannot afford to move forward without the right data backbone to support decisions for growth and performance.