Trends in Digital Transformation: Our Predictions for 2022
As the year comes to a close, it’s time to look ahead to the key trends shaping data and analytics in 2022.
Cloud Migration’s Inconvenient Truth
The urgent imperative for companies to leverage the cloud (including hybrid- and multi-cloud) to enable scale and agility of analytics, be increasingly data-driven and drive digital transformation, is evidenced by the migration of data and analytics to the cloud. The cloud migration services market — valued at $88B in 2019 — is expected to grow to $515B by 2027, reflecting a CAGR of nearly 25%. However, in 2022, many of these initiatives will fall well short of expectation and business objectives, costing tens to hundreds of millions of dollars and burning countless hours of expensive data talent in the process. Legacy methodologies and point tools to drive migration efforts will provide very expensive efforts in failure.
2022: An Urgent Need for a Modern Data Infrastructure
The experiences of the past 18+ months have underscored the critical need of a modern data infrastructure in both private and public sectors. Data-based decisions and data-driven digital interactions cannot be made without the availability of the right data, at the right place, at the right time. We will recognize 2022 as an inflection point for adoption of comprehensive, automated solutions for hybrid multi-cloud data operations and orchestration.
Operationalization of Data Fabric Technologies
2022 will see significant growth and interest in data fabric solutions as companies seek to leverage a common management layer to accelerate analytics migration to the cloud, ensure security and governance, quickly deliver business value by supporting real-time, trusted data across hybrid-multi-cloud – all in driving digital transformation. We believe this technology will be broadly adopted over the next five years.
Automation v. Opportunity Cost: Real Value for Data Talent
A lot has been written recently about the “Great Resignation” – against this backdrop, consider the particular implications regarding scarce data engineering and data science talent, already in very high demand and commanding significant compensation. Though critical to driving digital transformation, currently nearly 80% of their time is spent on lower-value activity such as ingesting, incrementally updating, organizing and managing data. In the coming year, adoption of new automated approaches to data operations and orchestration will free this critical talent pool from the mundane and focus these valuable professionals on creating and delivering business value.