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Analytical agility: Six lessons for a post-Covid economy

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It’s often said that crisis is the great driver of innovation. Far from halting investment in analytics and AI, the shock of coronavirus could ultimately drive businesses to these solutions in greater numbers than ever. Offering between $9.5 trillion and $15.4 trillion in annual economic value, these technologies can boost efficiency, inform business responses and aid recovery on a massive scale.

However, will businesses be able to implement in time? Analytics and AI can be time-consuming to develop and deploy, and time is a luxury most don’t have in the current climate. Fortunately, there are steps organizations can take to accelerate adoption, even in the midst of disruption. What counts is building a data estate, analytics capability and workforce that can work with agility and is braced for digital transformation.

Lesson #1: Laying the analytical foundations

To bounce back effectively from COVID-19, an organization first has to understand the new business challenges that have emerged. It’s vital to stand up central analytical centers in the cloud to rapidly mobilize business and analytics resources to inform and address these challenges. This will enable companies to ingest new data streams, report on business-critical issues and guide immediate and long-term decisions. Cloud-based analytics provides more agility, ease of deployment and the ability to quickly scale up use of analytics and AI technologies.

With a fully aligned agenda and a clear view of the crucial missions, organizations can devise analytics-driven solutions that help leaders stay adaptive, and prepare more effectively for the future.

During the pandemic and in its aftermath, the most salient applications have been centered around protecting and supporting employees, informing strategic decisions, managing the supply chain, and engaging customers in new digital ways. And all this has been amid a new normal where many things and behaviors have fundamentally changed, meaning predictive analytics based on the very latest data has become crucial.

Lesson #2: Empower your workforce

Success depends greatly on the human teams behind the data, and those responsible for building the analytics solutions. Increasingly, to achieve a fast and effective response, organizations must make the move from siloed work to interdisciplinary collaboration. Indeed, 62 percent of businesses that assemble cross-functional teams are more likely to realize higher returns from AI than those that don’t.

It’s also becoming more important for these teams to be empowered with real, decision-making authority. While many organizations are based on the strict accountability of their leaders, they can no longer wait for decisions to pass slowly up the chain. Sudden changes need immediate responses, and those on the frontline have the contextual knowledge needed to make the best decision.

Lesson #3: Standardize and optimize

To do their jobs and embrace new-found authority effectively, staff need to be supported by common tooling and technologies. Standard tool sets are critical to accelerating data ingestion, standardization and ensuring consistent model development. Businesses can’t afford to wait months for these tasks to be completed, when new mission-critical decisions have to be made every day.

Lesson #4: Prioritize key workflows to make change last

Analytics and AI systems that would have taken months to develop can be built in days when the right solutions are in place, and staff are motivated and empowered with decision-making abilities. Yet, once the worst of the crisis has passed, how do organizations keep up the momentum? How do you make analytical agility business as usual rather than something extraordinary?

The key is to keep investing in the right areas. Organizations must rethink their old business models and justify the reasons to sustain these ways of working. In the first instance, organizations should reallocate analytics and AI resource to their priority domains. This doesn’t mean shoring up particular use cases, it’s about reinforcing the key workflows that drive value for your business, whether it’s procurement, personalization or the supply chain.

Lesson #5: Support your people

Investing in people is also essential. Where possible, hire when others aren’t – it’s an ideal way to acquire precious skills while supporting those who may be struggling in a difficult job market. Businesses should also take the time to reskill workers whose roles may have lost value during the pandemic. Analytics training can help educate employees on the potential of analytics and AI and how to implement these technologies. They can also build critical skills, such as the running of agile squads and continuous-improvement methodologies.

Lesson #6: Create a method for innovation

Now more than ever, companies have to build out and strengthen their data and analytics plans for the future. In the short term, conducting an audit of data and models will help to flag and resolve areas of model drift risk and error. Over time, organizations have the opportunity to use this work to better document their models and instrument them with automated model-surveillance and model-management processes.

Laying down common protocols and repeatable methodologies for analytics and AI, preserves the ability to evaluate and develop new solutions in the future. Data integration and sharing tools are vital for consolidating siloed data sources and external data, enabling employees to find the data they need quickly.

The experience of Covid-19 has shown that business transformation isn’t only possible under extreme stress, it’s essential. Those able to marshal their resources and staff to embed analytics and AI across the enterprise will be able to add resilience to operations and remain agile and competitive for whatever comes next. Many challenges remain, but they’ll give themselves the best head start as things start to change again in tomorrow’s post-Covid economy.

Dr. Laurie Miles, Director of Analytics, SAS UK & Ireland