Some Data Sector Predictions For 2022

“Execution focus for 2022 will remain on how to efficiently deliver self-service access to data, navigating the increasingly complex web of regulatory and legal requirements including data sovereignty, data protection and industry specific regulations. Through use of modern data provisioning tools, more data will be available for decision making and innovation in a way that ensures consumer trust.

At the same time, board rooms will give more thought to the ethical considerations around data use – how do we ensure the sins of the past are not propagated by AI at massive scale, how do we ensure data is not inappropriately weaponized to drive unbalanced outcomes and how do we safeguard civil liberties under threat through mass surveillance? Ethical considerations are inextricably tied to culture, and so while there will be no single solution to these challenges, work must be done to ensure we have a common framework of understanding and the right checks and balances are embedded.

We have seen hype over big data, the promise of AI, and the need for privacy. The pandemic has accelerated our paths here and forced some key realizations:

If we want to realize the value of data we need discipline and control. We need to think carefully about the data we need, the cost to appropriate, store and protect it and balance this with the potential upside. A policy driven approach to the controls we put on data use will become the norm.

Value creation happens at the point of consumption. For data to realize a return, we need to get it efficiently into the hands of analysts and data scientists and time to data is a key metric here. More business intelligence and data science teams will carry targets aligned to commercial success.

Organizations that value data, will value privacy. The idea that data protection needs to be intrinsic to systems and consistently applied at all points of consumption will go mainstream.”

“With more and more organizations looking to gain access to meaningful data for analytics and ML, streamlining provisioning processes will play a key role in making this possible. Organizations can only create value from their data if they can quickly and reliably get the right data to the right people at the right time. They also need to make sure that data is safe to use…you can’t have one without the other. In 2022, we’ll see organizations streamlining their data provisioning processes so that they can accelerate and broaden access to data, while integrating privacy and accountability, enabling them to safely and efficiently leverage data for insights and innovation.”

“Organizations are increasingly embracing data mesh and data fabric methodologies as the basis of their modern data stacks. As they continue to do so into 2022, they will realize the critical importance of having trusted data as the foundation of these platforms. Data access and privacy tools that facilitate access to safe and trusted data will be essential for organizations to ensure that their data stays safe when working across their organization and beyond.”

“In the last 10 years, the time it takes for an organization to deploy a data warehouse or data lake has dramatically been reduced, going from 2/ 2 / 50 – 2 years, $2M and 50% failure to mere days and thousands of dollars on cloud-based infrastructures. At the same time, the time for approval processes to use the data now available has increased to 2-3 months. Now that the technical barriers to self-service data access have been removed, we’ll see organizations refocus their efforts to remove the provisioning and compliance barriers in 2022 and beyond, so that they are finally able to maximize the usage and sharing of their data.”

“Organizations have long appreciated the value of data and analytics to drive business insights. Many are investing in building best of breed systems to store, protect, and analyze that data, but for most, it is still a patchwork of systems. To maximize value from that data, organizations will focus on connecting the dots to unite all of their diverse ecosystems at every stage of the data journey- from data ingestion to consumption.

This will be of particular focus for international companies, who want to stitch together data across jurisdictions, have both regional and global views into their data, and still be in compliance with data protection and privacy regulations.”

–Nick Curcuru, Vice President of Advisory Services, Privitar

“Analytics in HR has traditionally been about reporting. As organizations grapple with employee turnover amidst the “Great Resignation,” they will increasingly look to predictive analytics to help save the day. By embracing predictive analytics, organizations can identify key trends in employee engagement and key moments in time to intervene, enabling them to take action and possibly save relationships that might be on the brink. It can cost as much as four times an employee salary to replace someone as it does to keep them, so being able to prevent churn can provide a huge value to an organization.

As part of predictive HR analytics efforts, organizations need to make sure that they are taking proper precautions to protect the people underneath the data and have full transparency on the policies used to protect the data.  HR data can be amongst some of the most sensitive and incredibly useful data to derive insights. If companies use it both safely and ethically, they can reap the benefits of being able to leverage that powerful data, support happier employees, and increase engagement and longevity.”