Like Imperial Cruisers chasing the Millennium Falcon in Star Wars, COVID-19 forced enterprises to “jump to lightspeed” with their data analytics strategies – a trend that will continue after we leave the virus behind.

“Data elite” enterprises survived this year through agility, efficiency and intelligence. These leaders accelerated digital transformations, adopted cloud data platforms and embraced advanced analytics. They automated and integrated processes to rapidly serve customers, and strengthened data pipelines to meet analytics needs at scale. They also operationalized and democratized analytics to help the business navigate turbulent markets. As these data leaders continue their momentum next year, the laggards will strain to catch up. The result: digital transformation, cloud data platforms and advanced analytics have become table stakes to compete in the post-COVID-19 era.

To understand why, consider the business requirements. First is agility. Erratic customers, broken supply chains and vacillating regulators all force enterprises to rejigger their business models, people and processes on short notice. Second, enterprises must improve the efficiency of the complex data architectures so they have the resources to survive and place new bets. The third requirement is intelligence: enterprises must reduce uncertainty by studying their business and the landscape.

To meet those requirements, enterprises will double down on the following strategic initiatives in coming years.

Digital Transformation

Ecommerce shoppers, telecommuters, factory operators and other stakeholders depend on digital services more than ever. Enterprises must use digital transformation to rapidly engage, transact with, and deliver value to those stakeholders. For example:

• Intelligent Process Automation (IPA). Business, IT and operational managers can extend Robotic Process Automation (RPA), which automates discrete tasks for product orders, employee onboarding, etc., to the realm of IPA. This means automating complex processes end to end, using AI and predictive analytics, as Andrew Sohn suggests in our 2021 Predictions blog. 

• Integrated customer experiences. Marketing, sales and BI managers can combine 360-degree customer views and various engagement channels – website, storefront, social media and dial-in support – to analyze and manage the customer experience on a more holistic basis, often with context-driven responses to real-time events.

• Smart factories & supply chains. Manufacturers can use IoT telemetry, automation and advanced/predictive analytics to make factories more autonomous. Logistics companies can use similar technologies to track and manage supply chains. In both cases they improve efficiency and accommodate future disruptions.

Cloud Data Platforms

As IT organizations migrate operational and analytics workloads to new cloud data platforms, they complicate the task of data integration because old stuff like mainframe lives on. They must embrace tools and procedures that meet analytics needs with scalable, elastic data pipelines for diverse environments. Here are three examples.

• Hybrid and multi cloud integration. Data engineers and architects can reduce administrative overhead by abstracting from heterogeneous storage and compute resources. Data warehouses, DataOps tools, and query engines can all integrate data across on-premises, cloud and multi-cloud platforms.

• Data pipeline observability. Site reliability engineers, platform engineers and data engineers can use new data pipeline observability solutions to gain integrated, multi-layer and cross-sectional views of data workloads. This improves production analytics and AI performance.

• Machine Learning Operations (MLOps). Data scientists, developers, ML engineers and data engineers can use MLOps to streamline the deployment, management and governance of ML models. They can more easily adapt and re-train machine learning models in a volatile business environment. 

AI & Advanced Analytics

Business managers struggle to understand sudden market changes and respond to events whose value perishes fast. Enterprises must take steps such as the following, in most cases leveraging real-time insights to ensure decision makers act on the very latest intelligence.

• Continuous intelligence. Data scientists, developers and data engineers can apply historical and real-time analytics to both internal and external data, then operationalize the resulting recommendations. They can generate real-time customer offers, identify fraud risk, deliver preventive maintenance, and address myriad other use cases.

• Augmented and embedded analytics. Many commercial analytics products now augment data preparation, analytics and visualization with machine learning and AI. This makes business analysts and data scientists alike more productive. Embedded analytics, meanwhile, can enhance and speed up functional workflows – finance, sales, supply chain management, etc. – leveraging real-time updates.

• Democratized analytics and AI. Business-oriented analysts can use internal data marketplaces, external data exchanges and increasingly intuitive data preparation and BI tools to enrich their analytics processes. In addition, business managers can tap AI marketplaces, where data scientists and developers offer widely-applicable models to improve operations.

Star Wars fans will recall two aspects of making the jump to lightspeed: first, it is tricky and plagued with malfunctions. Second, you cannot hit the reverse button. Both these lessons apply to the future of data analytics in the aftermath of COVID-19.