Data is power. In this modern technological era, companies need to rely heavily on the ability to efficiently extract knowledge from data to gain an edge over the competition. The main challenge is to reduce the complexity arising from boundaries between the massive pool of data and the technology and people involved.
The data pool is ever-expanding. And research in technologies, such as quantum computing are being conducted to make it possible to develop means to generate adequate processing power to cope with the sheer volume of information.
So, What Is a Data Fabric?
A data fabric is a virtual software architecture that aims to deliver integrated information that is more specific and relevant to the field an enterprise user demands. It essentially makes it possible to access quality data quickly and on demand from all available sources—a herculean task if an organization has to deal with a constantly expanding data ecosystem that makes it significantly complex to extract relevant information.
So, in essence, it can be considered a composite piece of technological architecture that is imperative to enterprise data integration and management.
Why Do We Need Data Fabrics?
Every piece of data that exists in an ecosystem is first created then ingested, transformed, and extracted. This cycle is repeated over and over on a variety of systems that, with time, get replaced by something different, modifying the basic process to a certain degree. All this happens while newer data gets layered on top of existing information until they all coexist within the same ecosystem. Note, however, that the data doesn’t get integrated homogeneously.
This scenario leads to issues, such as:
- Maintaining several different kinds of systems that have been used for data integration and management over time—all of which costs a lot of money.
- Data integration becomes slower due to multiple data layers. It thus takes a significant fraction of IT development time just trying to make them work together.
- Extracting specific data in real-time becomes more difficult and time-consuming, not to mention that more than half of the total amount of information actually remains unused.
- System implementation becomes more costly.
The knowledge era primarily aims to close the gap between the total amount of available data in an ecosystem and the information that actually contributes to insights. The insights gained eventually lead to the development of more efficient business strategies, more dynamic customer services, and personalized customer experiences.
How Does a Data Fabric Work?
The principal functions of a data fabric closely mirrors the AI Ladder Methodology. It simplifies the process of automating and converting enterprise data into information that is useful and relevant. It has the following components:
- Collection: To provide access to the complete data pool and facilitate hassle-free collection of useful data.
- Organization: Verifies data quality and catalogs information so it can be integrated into an AI model. This step is followed by securing the data collected so access to it can be restricted.
- Analysis: Building an AI model based on selected algorithms and testing its capabilities in a simulated workflow. Upon a successful trial, a more in-depth study of performance is conducted with a more complex workload to eliminate chances of bias or unexpected errors.
- Infusion: Implementation of various AI models, each with a unique purpose across the entire data pool.
Similarly, top data fabric vendors will ensure access to all information sources and domains and then comprehensively monitor the data so that collection, organization, analysis, and infusion are done efficiently. That leads to proper utilization and quick processing of a vast amount of data. Finally, it enforces business policies across the entire data pool so that all the information is only available to authorized users upon request.
Conclusion
In sum, a data fabric strategy aims to utilize both human and IT resources more efficiently to collect and process vast amounts of information to find the most relevant and desirable excerpts that will contribute to the knowledge an enterprise possesses. That leads to an overall improvement in business efficiency, the formation and execution of business models, and better customer services and experiences. It also brings significant benefits to the table, such as reduced costs and time spent on maintenance and data integration. As such, it has become the go-to strategy for data management.
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