Senior management and data teams are turning to AI-driven data management to help them manage their ever-growing data library, become more efficient, and reduce costs.
Data is one of the most important assets for an organization, and its volume and importance are continually increasing. The process of data management – cleaning, extracting, integrating, labeling and organizing data – is expensive, labor-intensive and presents many challenges. Data teams can invest in tools that use AI to automate aspects of the data management process.
“Adoption is still in its early stages right now, but leaders across companies are prioritizing investments in data and AI technologies,” said Zakir Hussain, Chief Data Officer of EY Americas. “Therefore, I think adoption will accelerate very quickly.”
Automating tedious parts of the data management process with AI can reduce the amount of manual effort required for tasks, speed up execution, increase the accuracy of results, and reduce costs. The demand for data management will continue to grow, as data quality is a need for successful use of AI.
Data management process
Data management involves many steps, which fall into several categories:
Data collection and storage. Businesses must collect, process, validate and store data. Data entry includes the integration of structured, unstructured and semi-structured data from multiple internal and external sources.
Data quality. Management also defines data quality standards. Quality needs typically vary based on data usage, and teams must maintain data to meet quality standards.
Data governance. The collection and use of data by an organization must respect Privacy and Security regulations and standards.
Data transformation. The accumulated data must be ready and available for use by the organization. Quality data sets are imperative for machine learning applications and other automated systems, as well as for data-driven decision making.
For years, data teams have used various technologies to support data management tasks. Many are still struggling to scale their operations as the amount of data increases and the use of enterprise data expands rapidly.
Automate data management with AI
AI helps streamline data reporting and simplify data interpretation. It can support metadata management. AI can also automate tasks such as data modeling, developing access policies, and generating schema rules.
Additionally, AI can facilitate data classification, cataloging, integration, quality and security. It can also enrich basic datawhich constitute the basic non-transactional data that describes the components of a business and its activities.
“Any increase to make these steps faster and easier for data scientists is valuable, because the less time they spend on these steps, the more time they can spend on models,” said Sumit Agarwal, vice president analyst at research firm Gartner.
The application of AI to data management tasks includes the following:
Data cleaning and quality. Cleansing resolves problems in a data set, such as incorrect, incomplete, or duplicate data. AI-powered tools can detect and fix duplicate, missing, and inconsistent data. AI – particularly generative AI (GenAI) – can further facilitate processes by analyzing data quality requirements, creating data validation rules and flagging errors, Hussain said.
Cataloguing.Data cataloging is the process of taking inventory of all of an organization’s data. This may include collecting, storing and labeling data. “We do data cataloging for governance and literacy, but it’s always been a struggle,” said Matt Sweetnam, chief architect at consultancy AHEAD. AI-guided software can replace the administrator’s task of labeling, identifying and quantifying data, and doing so as it comes in, he said.
Labeling. Labeling, sometimes called data annotation, is an essential part of preparing data for business use, especially in machine learning models. This involves identifying and labeling raw data, regardless of its type. Labeling is a large amount of work, especially when processing images as well as increasing the volume of raw data so that it is ready for use, Argawal said. Implementing AI to annotate data is virtually non-negotiable. “This improves the accuracy and time it takes to complete these jobs,” he said.
Visualization. AI-based tools can graphically represent relationships between data and weight data in a 3D display. Visualization can help teams better understand data.
Increase. Some tools have AI-supported data augmentation. AI can automate the data enrichment process and create synthetic data to extend existing data sets, in addition to offering augmented data discovery.
Any increase to make these steps faster and easier for data scientists is valuable because the less time they spend on these steps, the more time they can spend on models.
Sumit AgarwalVice President Analyst, Gartner
Data Management Tools Market
The demand for data management technologies is showing significant growth. The global enterprise data management market was worth $89.34 billion in 2022 and could grow at a compound annual growth rate of 12.1% until 2030, according to a study. report from Grand View Research.
Part of this growth comes from data teams introducing new tools that incorporate AI. Data teams typically look for data management tool providers that integrate AI into their products and platforms. However, some teams build their own models to meet the unique needs of their organization.
According to market experts, data teams can expect an increasing number of AI options to support their work in the future. For example, AWS integrates AI in integrations between database services to eliminate tedious extraction, transformation and loading tasks. Informatica uses AI-powered governance tools to automate the data a user can access. SAP is working to improve data quality and access by integrating GenAI in SAP DataSphere. These vendors are among many leading players in the enterprise data management space, according to the Grand View Research report.
“Every tool vendor is trying to figure out how to inject this technology to make their product better,” Sweetnam said.
Mary K. Pratt is an award-winning freelance journalist focused on enterprise IT and cybersecurity management.
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