Data governance trends in 2021 reflect a changing data management landscape, one focused on centralization, improved data security, and streamlined remote access. In large part, data governance trends are being impacted by continued fallout from the 2020 pandemic response, when enterprises were faced with abruptly shifting their workplace structures to remote setups.
Our five data governance trends impacting the software marketplace in 2021 makes it clear that companies are focused on compliance to meet the requirements of a growing list of data privacy regulations as well as improving data management, performance, and efficiency related to data governance tasks.
Data governance today
Data governance trends in 2021 are likely to resemble drivers that have remained constant for the past several years, according to the erwin’s “2021 State of Data Governance and Empowerment” report, including:
- Data analytics capabilities
- Regulatory compliance
- Better decision making
The firm notes that there are two newly identified drivers for implementing data governance policies as well:
- Improved data security
- Improved data quality
These new drivers reflect unique data security challenges posed by the COVID-19 pandemic. Widespread remote access to corporate data assets introduced new vulnerabilities across enterprise networks, including those that affect data governance goals like data privacy compliance.
As in years past, data governance will continue to play a complementary or integrated role alongside other data oversight tools, including those featured in end-to-end data management platforms.
5 trends in data governance software
1. Increased focus on establishing a single source of truth (SSoT)
Enterprises are recognizing that disparate data silos frequently lead to significant issues, especially when it comes to quick access, auditing, and reporting. Consolidated data repositories are easier to oversee and protect.
Seventy-three percent of IT decision makers said they rely on data more than ever when making business decisions, according to a recent survey by Druva. However, 41% of those respondents indicated they do not have readily available access to the data they need.
Cloud-based solutions are increasingly popular for streamlined data access and storage. Cloud data repositories allow remote workers and clients to access data from anywhere and are more scalable than many on-premises solutions.
While organizations are trending toward centralization, it’s important to note that data security becomes even more critical, as more extensive data pools are more attractive targets for bad actors. Centralized data must also be safeguarded against data loss due to natural disasters, accidental erasure, and machine failure.
2. Formalizing data collection
An ever-expanding pool of data is impacting the way organizations collect data. Whereas before, enterprises might have used many different methods for data collection, often based on client preference, more formalized data collection helps to ensure data integrity in data governance.
Consistent data collection from the outset significantly reduces the time involved with data oversight, because analysts don’t need to manually adjust data for uniformity. Uniform data collection also benefits certain artificial intelligence (AI)-enhanced software solutions, especially those involving rules-based machine learning (ML), an issue that affects network security platforms as well as data governance tools. Clean data is critical for machine learning software — the risk of missing huge stores of data is very real when data structures are inconsistent.
Declan Owens, digital analytics expert at Piano, a global analytics and activation platform, said that data quality is an approach that must be sustained to guarantee the reliability of data over time.
“Consider creating a data governance body to monitor the efficiency of your processes on a permanent basis,” Owens said. “If a data item contains an error, study it, correct it, record it and then adopt the appropriate rules so that it does not happen again.”
3. Increased focus on data literacy
Organizations are increasingly focused on improving data literacy across their workforces as a way to improve the overall care and handling of data across departments and roles. This holistic approach can improve data governance across the board.
Enterprises are educating employees more deeply about the data they use daily, imparting knowledge about data security, best practices for data processing and provisioning, and training on tools that help companies achieve better data governance.
The idea around data literacy in this context is that data will be handled more carefully from the moment it is added to enterprise networks. A company culture that prioritizes data integrity and security is sure to lead to better auditing, reporting, and compliance.
4. Cloud integration
Data management in general has shifted toward cloud-based models where encrypted data can be accessed and stored remotely. Not only do these setups result in more streamlined and efficient business practices, but they also help enterprises monetize data in various ways.
Data governance in the cloud is a must no matter how the data is used. In the future, industry analysts predict that data privacy regulations will likely evolve to consider how cloud storage impacts risk. Companies can expect to see new regulations on the horizon that specifically address cloud data.
Most enterprises conducting data governance tasks in the cloud operate within a hybrid cloud or multiple cloud environment, which can drive down overall costs, as not all data needs to be tightly protected in more expensive private cloud networks. By the end of 2021, over 90% of enterprises worldwide could rely on hybrid cloud models that include private and public clouds combined with legacy platforms, according to IDC.
5. AI and ML
AI and machine learning have become a norm for data governance tasks for many enterprises. ML platforms can automate tasks like data organization and compliance auditing, freeing up analyst time for higher-priority issues like security functions.
The increase in the use of machine learning tools is highly related to data integrity and uniformity. These platforms are only as reliable as the data that is fed into them. Modern machine learning is better at ferreting out hidden data, but it still performs much more accurately when data is stored uniformly.
Current data governance trends primarily focus on shared goals around data handling as well as organized and uniform data collection and storage methods. Seeking out software solutions that keep these priorities in mind will help prepare enterprises as data ingress/egress and governance requirements increase over time.