A confluence of events in the data management and AI landscape is bearing down on companies, no matter their size, industry or geographical location. Some of these, such as the continued sprawl of data across multicloud environments have been looming for years, if not decades. Others have come into sharper focus relatively recently: a global effort to create new data privacy laws, a post-pandemic expectation by customers to know them individually across all touchpoints, and increased attention on any racial, gender-based, or socioeconomic bias in AI models.
While individual point solutions have been able to address some of these concerns in the past, it is rapidly becoming clear that a more robust solution is needed – one that can address a business’s most pressing data and AI need while providing an easy path to solve additional challenges. That solution is the data fabric.
A data fabric is an architectural approach to simplify data access in an organization to facilitate self-service data consumption. This architecture is agnostic to data environments, processes, utility and geography, all while integrating end-to-end data-management capabilities. A data fabric automates data discovery, governance and consumption, enabling enterprises to use data to maximize their value chain. With a data fabric, enterprises elevate the value of their data by providing the right data, at the right time, regardless of where it resides. We’ve identified four of the top use cases for the data fabric below along with a brief overview and links to a more in-depth eBook and trial. These use cases provide a foundation that delivers a rich and intuitive data shopping experience. This data marketplace capability will enable organizations to efficiently deliver high quality governed data products at scale across the enterprise.
The rapid growth of data continues to proceed unabated and is now accompanied by not only the issue of siloed data but a plethora of different repositories across numerous clouds. The reasoning is simple and well justified with the exception of data silos – more data allows the opportunity to provide more accurate insights while using multiple clouds helps avoid vendor lock-in and allows data to be stored where it best fits. The challenge, of course, is the added complexity which hinders the actual use of that data for analysis and AI.
As part of a data fabric, multicloud data integration aims to ensure that the right data can be delivered to the right person at the right time. The availability of integration strategies including ETL and ELT, data replication, change data capture and data virtualization are key so that the widest possible range of data integration can be enacted. Similarly, data cataloging and governance helps establish what the “right data” is in any given situation and who the “right people” are that should have access. As for data delivery at the “right time” automated data engineering tasks, workload balancing and elastic scaling should provide the needed alacrity for all businesses.
Data privacy laws such as the GDPR in the EU, CCPA in California and PIPEDA in Canada have been enacted at the same time businesses are revitalizing efforts to establish data quality, not just data volume. The cost of ignoring these imperatives is staggering. Poor data quality costs organizations an average of $12.9 million each year[1] and $1.2 billion in fines have been issued due to GDPR non-compliance since Jan. 28, 2021.[2]
The governance and privacy component of the data fabric focuses on organization and automation. As discussed in the previous section data virtualization and data cataloging help get the right data to the right people by making it easier to find the data that best fits their needs and access it.
