The Four Data Management Trends on the Rise

The Four Data Management Trends on the Rise

Most organizations today need help to crack the data quality problem. In a survey conducted by O’Reilly, more than half the respondents said they were still grappling with this issue. In our recently released 2022 State of Data Quality report — based on a survey of more than 1,000 executives and business users — 97% said their organization considered data quality an extremely or somewhat important priority. 

The concern is understandable. With volume steadily increasing and new data types emerging, ensuring quality is a greater challenge than ever. As a result, there have been a number of developments in data management best practices and technologies that all have one goal: to ensure data is operational and fit for enterprises and users who want to drive business value and gain a competitive advantage. Let’s look at the four rising trends, one at a time.

Unified Data Management Platforms

Gartner predicts that by 2025, “80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.” What is that modern approach? It is about using platforms instead of individual tools for various initiatives driven by data governance.

The benefits are better user adoption, smaller integration costs, and synergies between individual capabilities such as the data catalog, data quality tools, business glossary, MDM, and reporting. For example, users can take the understanding of data as a result of data discovery and immediately use it to configure data quality monitoring. Such native integration makes the whole process more efficient and automated.

Data Observability

Data quality clearly prompted a move toward unified governance and data management platforms. However, a broader concept is now included in a data observability trend. In brief, it refers to an organization’s ability to entirely comprehend the health of data in its systems. Data observability goes beyond data quality monitoring and encompasses data lineage, data classification, and AI-driven anomaly detection.

Having a finger on the pulse of what’s happening gives data stewards and engineers a better chance of catching issues earlier and rectifying them more easily. Without data observability, data teams can end up triaging broken pipelines as they pop up, which rivals having to find a needle in a haystack.

Data Fabric 

Data fabric is a management framework that has become more mainstream over the past year. It provides frictionless access to enterprise data for everyone who needs it by connecting data sources, management components, and consumers in a unified system driven by metadata and artificial intelligence (AI) to automate data provisioning. 

One of the most prominent features of the data fabric is that it spans your entire data landscape. This layer then integrates data, performs quality checks, transforms and deduplicates it if necessary, and serves the data to users and applications. It also analyzes how data consumers access data and can streamline data requests in the future.

Data Mesh

This management architecture has become popular in recent years. Data mesh complements the data fabric by giving freedom to data creators, which is key to handling scale and elevating data to the data products' status. Its decentralized architecture and governance concept assigns responsibility for data to the teams that produce and own it. Each team prepares it so that the data is ready for use if another team needs it. 

In tandem, data fabric and data mesh allow frictionless access to high-quality data – the keys to success. At the intersection of these two concepts are federated governance and data democratization. This combination enables an organization to centralize governance, quality and security in a top-down manner while giving data product creators the freedom they need to do their jobs.  

About the Author

As Ataccama’s Chief Executive Officer, Michal Klaus has supported the company's growth from its early stages to present success with 350-plus clients in the data management and data governance spaces. He has deep experience in the computer software industry and strengths in enterprise architecture, agile methodologies, ETL, databases, and IT strategy. Michal has been a pillar to the Ataccama team in developing its flagship product, Ataccama ONE. Most recently, Michal has overseen Ataccama’s $150m funding round led by Bain Capital Tech Opportunities. Ataccama enables enterprise data democratization with a unified platform for automated data quality, MDM, and metadata management across cloud and hybrid environments.

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