Enterprises today want to have real-time business insights into what’s happening to take actions that ultimately increase operational efficiencies, improve customer engagement, and grow revenue. However, the promise of data-driven enterprises may be just that, a promise, if they cannot overcome their data management challenges.
Problem: Data complexity
The problem: Data today doesn’t exist behind firewalls or within on-premises locations. It is globally distributed in siloed databases, data warehouses, and more across on-premises, network edge, and multiple clouds. To unlock value, applications need access to that data to contextualize it and correlate it across different datasets. But at the same time, enterprises must ensure data integrity, protect the data, and guarantee all users have the most current version of the data at any given time.
As a result, enterprises struggle to balance democratizing data and protecting it to ensure regulatory compliance. And they must provide a comprehensive view of that data to all that need to use it.
Unfortunately, the dynamic nature of data practically ensures enterprises are spending all their time trying to figure out what they have, where it is, and who should have access to it. Just that task alone makes it hard to react quickly to market changes and meet new business needs. In terms of digital transformation, this data friction is hopeless.
Simply put, enterprises today must deal with many data silos and disparate apps with data, which makes data engineering a nightmare. A typical approach to managing and using such data requires multiple tools with millions of custom-coded scripts to bring the data together. Not knowing the meaning behind all the data makes it difficult to integrate it for the right use-cases.
Such an approach does not scale well as data volumes grow. What’s needed is a fundamental shift in the way enterprises use and manage their data. Specifically, what’s needed is a new data management architecture and ongoing management processes to manage data in this new world.
Solution: Build a data fabric
Enter the data fabric. Gartner identified the data fabric as one of its top data and analytics trends in 2021. At that time, it defined data fabrics as “enabling frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage.”
It also predicted that by 2022, “bespoke data fabric designs will be deployed primarily as a static infrastructure, forcing organizations into a new wave of cost to completely re-design for more dynamic data mesh approaches.”
That vision is spot on in today’s world dominated by distributed, data-driven applications and services. In fact, the data fabric is an emerging modern data management architectural concept for attaining a single comprehensive platform to create low-code, flexible data pipelines.
One reason the data fabric is gaining great attention now is that much of the data enterprises use is in motion. It continuously identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points.
In particular, a data fabric is built on metadata providing a unified view across all the different silos by having a common understanding of all data. To accomplish this, a data fabric uses continuous analytics and advanced artificial intelligence and machine learning capabilities.
The data fabric is an emerging design concept for data management that addresses the challenges of data complexity. It provides an agile enterprise data foundation to support a wide variety of business use cases. The notion of a data fabric is closely tied to DataOps and initiatives for data modernization and digital innovation at large.
Managing data through DataOps
Building a data fabric is just the start. Enterprises need to look at people, processes, and technology changes to operationalize data management by bringing in automation and collaboration. Essentially, what’s needed is to bring a DevOps culture of CI/CD to data. In that way, the numerous data producers and data consumers can come together, breaking down organizational silos by leveraging the data fabric.
Such an approach enables collaboration and sharing. The sharing goes beyond the data itself. By eliminating the barriers to data access and enabling democratized access to it, another barrier must be removed. In most enterprises, there is a great amount of institutional tribal knowledge locked in different functional groups. As an enterprise brings data together in a single fabric, it also needs to bring this tribal knowledge together. This process can be aided by driving DataOps across the enterprise.
Additionally, a data fabric simplifies data complexity by automating data integration, governance, and processing. As part of DataOps, a data fabric helps manage all the data, unlocking its value by operationalizing data management across the fabric. It brings developers and operational teams together, breaking siloes, and allowing collaboration on the same data across the fabric.
Delivering lasting benefits
Compared to the manual process of data pipeline creation, which leads to slow, error-prone, redundant work, a DataOps approach in conjunction with the data fabric helps fully automate data management. This can eliminate data silos and provide a single environment for accessing and collecting all data, no matter where it’s located and no matter how it’s stored. This, in turn, allows data engineers to better service data consumers.
As a result, a data fabric helps provide applications, business units, and people with the right data at the right time and with the right quality. This provides enterprises with an agility that is often lacking. By removing the complexity of data management, enterprises can focus on delivering new products to market faster, improving customer experiences, and building a competitive advantage.
These are all areas where data fabric and data management solutions from Hitachi Ventura can help. To learn more, visit: https://www.hitachivantara.com/intelligent-dataops
About the Author: Madhup Mishra
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