The world is a very different place this year than it was a few years ago. Much has changed, not only in terms of available technologies, but also how businesses use technology and the way they had to adapt to a fully remote or hybrid working environment. This has changed the way data is used, which in turn fundamentally alters the way data needs to be managed.
By Gary Allemann, MD of Master Data Management
What are the four trends shaping and affecting data management in 2022?
Changing environments, changing behaviours
Covid-19 has caused a fundamental shift in the working environment. It forced businesses to adopt remote working, proving that it was far more achievable than previously believed. In the wake of the pandemic, many organisations have adopted a hybrid workforce to enable employees to continue to work from home at least part of the time. We have also embraced online shopping like never before, which has shifted retail models of consumer behaviour.
The upshot? Dataops teams and other data management stakeholders cannot depend on face-to-face meetings to drive data outcomes. Automated collaboration tools that make it easier to find, understand, and manage data should be explored. 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.
No more monoliths
Another trend that has gained momentum over the past 18 to 24 months is the shift away from monolithic Enterprise Resource Planning (ERP) solutions delivering all front and back-office systems, toward specialist tools for specific purposes for HR, procurement and Customer Reputation Management (CRM), for example. This move from a single application structure to a multi-app environment means that data needs to be migrated into the new applications as well as shared across them.
This poses a challenge to data integrity during the migration. Without an understanding of data and your ERP’s metadata, and a solid data migration plan, there is potential for numerous data issues to creep in.
Data quality and master data management solutions have become essential tools for migrating data, and helping businesses manage data across and between platforms in these new multi-app environments.
Analytics goes cloud
Many businesses have also begun a shift away from on-premise analytics solutions toward the cloud, in an attempt to gain more meaningful insight from data.
Migrating data into the cloud has always been a challenge, especially when it comes to understanding on-prem architectures and successfully replicating key functionalities in the cloud. Streaming data pipelines will be the new normal, as companies seek actionable intelligence with increasing volumes of data. The ability to quickly identify errors and maintain data pipelines integrity will be critical.
Automated metadata harvesting tools, along with data lineage solutions, can be hugely beneficial here.
Data privacy issues must also be considered, and managed, as sensitive data needs to be protected while at the same time remaining accessible for analysis. This is especially complicated in hybrid cloud environments, which require fine-grained access control solutions that can track sensitive data and apply privacy rules across both on-prem and cloud data stores.
Regulatory impact
The Protection of Personal Information Act (POPIA) continues to impact on data strategy and needs to be considered. Emerging legislation in the EU and the US aimed at regulating the use of Artificial Intelligence (AI) and other emerging technologies must also be considered.
For the purposes of compliance, it has become essential for businesses to document what they are doing with AI and how they are doing it – which makes data privacy, data quality and data governance tools critical.
Experts agree
As data has become the cornerstone of the modern business, data integrity has become critical. Learn what 800+ data and analytics leaders shared about the choices that their organisations are making today, which appear most effective in charting a path to data governance maturity and ultimately, data integrity.