6 Ways Generative AI Will Impact Data Management
ByVasu Sattenapalli, Founder and CEO at RightData
As businesses focus more and more on uncoveringnew ways to unlock the value of their data, generative AI (genAI) is presentingsome new opportunities to do so, particularly when it comes to data managementand how organizations collect, process, analyze, and derive insights from theirassets. In the near future, I expect to see six key ways in which genAI willreshape our current data management landscape, ranging from enhancing baselinedata accuracy to enabling the more widespread use of natural languageprocessing, helping to democratize data use for all.
1. Enhancing data accuracyand reliability for better overall quality
First, one of the most primary benefits ofgenAI in that it can help organizations train models, due to its ability togenerate synthetic data that closely resembles real-world datasets. Byreferencing synthetic datasets full of large volumes of high-quality data, thesemodels can now be trained to more successfully capture underlying patterns andcharacteristics when analyzing actual data. Beyond just training, thesegenerated datasets can also be used for numerous other purposes, such asstress-testing data pipelines.
Similarly, we’ll see these samecapabilities employed to improve anomaly detection techniques, in turn leadingto better overall data quality. Traditional anomaly detection requires usingset rules or statistical thresholds to identify outliers in data, whereas genAImodels can learn from underlying patterns and data distributions to detect thoseanomalies that may not conform to predefined norms. More thorough anomalydetection like this will enable organizations to more accurately pinpoint anydata inconsistencies, errors, or outliers, thereby enhancing the reliability ofthe entire dataset, as well as their other assets.
2. Enabling widespread useof natural language queries in data analytics
GenAI will also prove useful for analyticsby introducing query assistance techniques that can guide users of varyingskill levels through the process of formulating queries. Users will be able tosubmit query requests in plain English, while genAI models work to analyze theinput and intent behind it. That analysis will lead the model to suggestrelevant query formulations or provide real-time feedback to users as theyrefine their queries.
From the user’s perspective, this not onlysimplifies the query-writing process, but it also means that those of anytechnical skill level will find it easier to interact with data—and quicklygrasp the most important aspects of their analysis. And from the organization’sperspective, this means that more users will feel comfortable with and findmore value from regular data use, leading to better business decision makingacross the board.
3. Bridging the skills gapin data engineering through NLP
We can also expect to see these naturallanguage processing (NLP) capabilities put to use to facilitate communicationbetween technical and non-technical stakeholders—especially in regards to dataintegration. Integrating data from multiple disparate sources has historicallybeen an intricate process that requires technical expertise in data formats,schemas, and integration protocols. But with NLP, much like the above, non-technicalusers will be able to express their data integration requirements in plainEnglish. For instance, business analysts or domain experts can submit querieslike “combine sales data from CRM with inventory data from ERP," allowingdata engineers to efficiently interpret and execute these requests.
In the data transformation phase, we’ll seeNLP streamline the often-complex coding and scripts tasks during data manipulationand conversion. With NLP-driven data transformation frameworks, data engineerscan interpret transformation rules in natural language and automaticallytranslate them into code, accelerating the development of data transformationpipelines.
4. Aiding in the enrichmentof data catalogs
Lackluster or incomplete metadata in datacatalogs can be easily addressed through the addition of genAI. After analyzingthe content, structure, and context of datasets, genAI models can populatemetadata fields like data types, column names, relationships, and semanticmeanings, helping business users to discover relevant datasets faster than theycould before. The models can also generate natural language descriptions orsummaries for those datasets, so users can understand the content and contextof the data they’ve searched for. Beyond this, because of genAI’s ability tocreate synthetic datasets, organizations can also use these synthetic datasamples to train their search and recommendation algorithms, yielding bettersearch results for users.
5. Streamlining informationgovernance for metadata
Much like the analysis and enrichment ofmetadata for data catalogs, businesses can identify key features, patterns, andcharacteristics in datasets, and then assign tags or labels to acceleratemetadata management. We can expect to see much faster and more accurateorganization and categorization of data assets, with genAI populating moredescriptive metadata attributes. Those attributes will also feed into genAImodels’ understanding of relationships between different types of metadata,drawing out new connections, dependencies, and associations between attributes.Together, these capabilities will support companies looking to build morecomprehensive and interconnected metadata schemas, in turn allowing theirbusiness users to navigate and explore metadata more intuitively.
6. Redefining documentationprocesses
And finally, we’ll again see those naturallanguage abilities deployed for documentation purposes. Rather thanlabor-intensive manual creation of complex documents, language models can betrained on textual data to understand key concepts and produce text that explainsit accurately. As a result, organizations can automate documentation tasks suchas writing technical reports, user manuals, and system documentation, which canachieve both a greater number of documents produced and more consistency acrossa suite of documents. These documentation efforts can also easily scale overtime to keep pace with the rapid evolution of technology while still adheringto their documentation standards.
With genAI’s ability to automate tasks andstreamline processes, it will prove incredibly useful for businesses looking toimprove their data management procedures—in the short term and the long term.Add in its natural language processing and generation capabilities, and it willyield the added benefit of democratizing data access for technical andnon-technical users alike. For organizations looking to embrace genAItechnologies, using it in these six key ways will help to unlock the greatestopportunities for efficiency and collaboration in data management.
