What will be driving data management in 2022?

With the new year, it seems appropriate to consider the business and IITtrends that I believe will be influencing data management this year.

  1. COVID management.
  2. The shift from ERP to specialist applications
  3. Analytics in the cloud
  4. Regulations
Image from pxfuel

COVID management

COVID has fundamentally changed the way that we work, shop and play. As Omicron seems likely to become the dominant variant we can expect more breakthrough infections – even amongst the vaccinated. Although people are less likely to suffer from severe illness it still seems likely that remote working, and shopping, will remain a popular choice, even if only part of the time.

From a data management perspective, we see automated collaboration tools as the area offering the biggest benefits to remote workforces, and will also help to embed XXXops approaches across distributed teams

Companies must look to tools that make it easy to find, access and manage data without dependencies on internal subject matter experts.

Data security is another concern that must be managed for remote teams., ideally in a way that does not lock people down from working effectively.

At the same time, companies will continue to battle for the hearts and minds of customers, through data analytics.

The shift from monolithic ERP to specialist applications

Over the last several years we have been witnessing the emergence of speciliast applications, many of them cloud-based, challenging the dominance of traditional ERP vendors.

It is no longer fashionable to have a single, monolithic ERP application delivering all front and back-office capabilities, Applications like Workday (for HR), Coupa (for procurement) and Salesforce (for CRM) are replacing capabilities in these areas.

This means that organisations must address data integrity issues between data previously stored in the ERP and data that must now be moved to the new platforms. Data quality and master data management solutions take on new importance in order to manage data across and between platforms.

The ability to properly understand your ERP system’s metadata is also vital in order to plan your data migration.

Analytics in the cloud

Leading on from the above – analytics in the cloud in the cloud is the new normal.

Successfully migrating data to the cloud presents its own challenges

The ability to untangle on-premise data architectures and replicate key capabilities in the cloud is essential. Automated metadata harvesting and lineage for existing data stores is essential.

The increasing volumes of data and the need for actionable intelligence will continue to drive streaming data solutions, which must, in turn, be reliable and traceable.

Data privacy must also be managed, ensuring that sensitive data is protected without blocking legitmate access. Hybrid cloud solutions require fine-grained access control solutions that can track sensitive data and apply privacy rules across both your on-premise and cloud data stores.


Data privacy regulations, like PoPIA, will continue to shift behaviour and must be considered when planning your data strategy.

Businesses must also consider emerging regulations, in the EU and US that seeks to govern the use of Artificial Intelligence and other emerging technologies that have created grey areas in existing regulations.

Proposed legislation to regulate AI in the US, like the Algorithmic Accountability Act and the Algorithmic Fairness Act, seek to regulate any system that facilitates human decision-making based on consumer evaluations or involves personal information based on race, religion, health, gender, etc.

Meanwhile, the EU has defined any use case that includes biometric identification or operates in critical infrastructure, education, employment, public and private services, law enforcement, migration, or state administration as high risk.

This means that companies investing in AI solutions must not only document how AI systems are developed, trained and perform over time, but must also show an assessment of the quality of data used for these purposes.

Data governance, data quality and data privacy solutions must become more mainstream if companies investing in AI wish to avoid the attention of regulators.