Enterprise Strategy Group research shows most organizations feel they need AI to unlock the full potential of DataOps and improve data pipeline performance.
AI and DataOps are closely related in the context of data management and operations. DataOps is an approach to improve the flow, quality and accessibility of data within an organization. At the same time, AI can play a significant role in enhancing most aspects of DataOps.
Seventy-nine percent of organizations say they must use AI in mission-critical processes to better compete, per TechTarget's Enterprise Strategy Group's "State of DataOps: Unleashing the Power of Data"research study. The May 2023 study is a survey of 361 IT decision-makers to identify trends in data management related technology use and purchasing habits. It's clear organizations see the need for generative AI, predictive AI and the use of large language models.
ESG found 79% of organizations feel they'll need to use AI in their
DataOps benefits from AI in a variety of ways. Let's explore various use cases and how applying AI may enhance performance and results.
In data quality and cleaning, AI algorithms can automate the cleaning and enhancing of data to increase its quality and eliminate many manual tasks. Machine learning models can identify and rectify errors, missing values and inconsistencies in data, which makes data more reliable and accurate for analysis.
With AI, organizations can realize new insights into data performance and pipeline efficiency from data collection through all processes to data visualization for end-user access and decision-making.
AI-powered tools can automate data integration processes. AI can help map data from various sources, resolve schema differences and create a unified data repository for analysis -- a fundamental aspect of DataOps.
AI can monitor data pipelines and systems in real time. At the same time, machine learning models can detect anomalies, data drift and potential issues in the data flow. When issues are identified, it generates alerts to trigger corrective actions, ensuring the data stays reliable and up to date.
Automated AI can validate data pipelines to ensure data transformations and integrations are correct, which reduces the risk of data errors and helps maintain data quality.
AI can help identify and mitigate data security risks and privacy concerns. Machine learning models can detect unusual access patterns and protect sensitive information, which is crucial in the context of DataOps.
AI can automate data transformation and enrichment processes. Tools such as Natural language processing can extract valuable information from unstructured data sources and present it to users logically.
Predictive analytics uses AI and machine learning to create predictive models based on historical data. Predictive models can provide valuable insights for decision-making and planning.
Data governance is a critical function of DataOps. AI can assist in data governance efforts and ensure data use is compliant and responsible. It can automate data classification, access control and compliance monitoring.
One of the key values of embedding AI into DataOps processes is the benefit of having continuous improvements. With AI, organizations can realize new insights into data performance and pipeline efficiency from data collection through all processes to data visualization for end-user access and decision-making.
AI can analyze data operations and identify areas for optimization, which helps organizations continually improve DataOps processes. Vendors across the DataOps spectrum continually embed new AI capabilities into existing products and workflows. Over the next 12 months, many AI advancements should manifest in the use cases above. It's an exciting time in the world of data.
Stephen Catanzano is a senior analyst at TechTarget's Enterprise Strategy Group, where he covers data management and analytics.
Enterprise Strategy Group is a division of TechTarget. Its analysts have business relationships with technology vendors.
