Data quality management efforts — tied to disrupting innovations, rapid market shifts and regulation pressures — will continue to grow in 2023 and take on a more dominant role in the data management ecosystem. Turning to the cloud, edge, 5G and machine learning, hybrid worldwide workforces and global customers are generating data at levels never experienced before.
SEE: Data governance checklist for your organization (TechRepublic Premium)
The success of data quality management depends on deployment, infrastructure and modernization strategies. The 2022 State of Data Quality report from Ataccama reveals that automation and modernization efforts have still not been universally adopted. While seven in ten enterprises surveyed (69%) have begun their DQM journeys, they still have not achieved high maturity levels. The technology is there, but companies are struggling to use it and are only scratching the surface of DQM’s potential.
Companies are increasingly realizing that if they don’t keep up with the latest trends and technology in data quality management, they will be left behind by their competitors. If you find yourself in a precarious data quality position and are looking to catch up with the most successful data-driven companies, these data quality trends are important ones to watch and implement.
What is data quality?
“Data quality measures how appropriately a data set serves its specific purpose in the organization,” said Aarti Dhapte, senior analyst of ICT Domain at Market Research Future, in an interview with TechRepublic. “Data quality measures are based on accuracy, completeness, consistency, validity, reliability, uniqueness and timeliness.”
Data quality, then, is not just a measure of how good the data looks but rather how effectively that data works for your organization’s data-driven projects and operations.
Common data quality issues organizations face include duplicate, incomplete, inconsistent, incorrect or insecure data. The consequences of these data quality problems can be vast and severe. Imagine how your organization or company would perform if decisions, sales, products and/or collaborations were based on data that is not fit for business use. Poor data quality can cause problems ranging from inconsistent production models to lost customer trust and reputation.
Furthermore, customers and government authorities expect corporate data to comply with privacy and security legislation. Companies that fail to meet these standards can be impacted by negative perceptions, lawsuits, hefty fines and customer losses.
As companies continue to grapple with growing data sets and data use cases, not to mention growing data problems and consequences, the data quality market is growing to meet those needs. Data from Verified Market Research reveals that the global data quality tool market will reach approximately $3.67 billion by 2028.
Top data quality trends
Leading companies are not just deploying cutting-edge data quality technology but are building DQM integral strategies that align with their business goals. Because every organization has its unique set of challenges and targets, every DQM approach requires a thoughtful, catered development strategy.
Several trends are emerging in the data quality market to support these companies as they optimize their data quality management techniques. These are the top data quality trends we’re seeing now:
Building a strong data culture alongside DQM strategy
The Talend 2022 Data Health Barometer survey revealed that 99% of companies recognize data as crucial for success, yet one-third of those surveyed say that not everyone in the company understands the data they work with, and half say that using data to drive business impact is not easy. Surveyed companies and many others are recognizing that data literacy and a stronger data culture must be prioritized if data quality efforts are to succeed.
Creating a strong data culture throughout an organization is difficult, but it is key to the success of DQM and other data-driven business strategies. Even the most advanced data technologies on the market can be inefficient if an organization does not have a solid DQM strategy built upon both trained people and organized processes.
Taking cloud data technology to new heights
The cloud is no longer merely a solution for data storage but the go-to place for services, digital solutions, automation and innovation tools. Top cloud providers — such as Google, Microsoft Azure and AWS — have been leveling up their built-in cloud services to differentiate themselves in the growing cloud data management market.
SEE: Cloud data warehouse guide and checklist (TechRepublic Premium)
With new cloud data solutions focusing on everything from automatic translations to machine learning, security, rapid migration, data quality automated checks, governance integration and AI-driven data operations, corporate data teams are benefiting from the competition amongst cloud providers and the release of disruptive new data solutions. This is also fueling an acceleration in the modernization of data warehouses.
The trend to modernize data warehouses is growing quickly as more organizations move toward digital processes. Data hubs are also trending as enterprises use them to connect data systems. The demand for structured data warehousing and management has led to a wide array of modern data hubs that offer various balances of advanced tools.
“These hubs provide a holistic approach to data management, from curation to orchestration,” Dhapte said.
Relying on AI/ML models for data quality management efforts
Developing and deploying AI and machine learning models used to be a manual and time-consuming process, but data teams can now deploy AI features in just a few clicks.
Researchers from McKinsey have explained that AI saw $165 billion in investments in 2021 as companies discovered how to use these AI models to solve real-world problems. The improvement in training these models has increased by 94.4% since 2018, McKinsey added.
DQM processes and technologies are starting to lean more heavily on ML and AI to solve common data quality issues. With the right artificial intelligence models, companies can automate and augment tasks like data classification, predictive analytics and data quality control.
And ML and AI features can go beyond text and structured data management needs. These models are often able to rapidly automate data functions related to computer vision, natural-language processing, knowledge graphs and other types of unstructured data.
Investing in trust architecture and other governance opportunities
The McKinsey Technology Trends Outlook for 2022 explains that digital-trust technologies are emerging solutions that enable organizations to build, scale and maintain stakeholder trust for their data-driven products and services. The trust architecture and digital identity sector has grown to $34 billion in investment, primarily focused on cybersecurity.
However, despite increased investment and external pressure, trust, compliance and governance tools have not yet reached full adoption levels. Even in companies where these solutions are being implemented, they aren’t always working optimally because of internal data problems.
Trust architecture can only work effectively with good-quality data. As more companies realize the importance of feeding good data into their trust infrastructure, DQM solutions are increasingly focusing on data governance and trust efforts.
Intelligent data warehouses are increasingly being used to automate and integrate trust requirements and tools that drive data. Tools that drive data encryption, or AI applications that can automate governance and check data for security vulnerabilities, are also now being used by leading organizations to gain a competitive advantage.
How are companies implementing these trends?
With innovations that support hybrid, edge computing or on-premises solutions, data quality management costs and time spent are going down. Companies can now clean up, migrate, load and profile data in hours for a fraction of the cost for operations that used to take months. Built-in plug-and-play or drop-and-load machine learning models and AI applications are now being used throughout the entire data quality lifecycle.
Organizations that are leading the way in DQM are building upon three main trend categories: An integral DQM strategy; technology and innovation; and data governance, security and trust. These trends are strengthening postures, automating processes, and ultimately empowering workforces and leaders as they enhance data quality management.
Organizations that recognize and implement these data quality trends will certainly have the edge over their less data-driven competitors in an evolving digital world.
Read next: Top data quality tools (TechRepublic)