When data is not viable for integration across systems and processes, business users will seldom have the right coverage of data. If people lack knowledge about data and its importance logically, it often becomes a challenge, which leads to less impactful decisions. A data fabric is an architectural capability that can give organizations a “data advantage,” if best practices are followed.
Enterprises are facing a growing challenge to better manage the ever-growing amount of data that is now available. By 2025, data is expected to double, but most of it will stay in silos as it is yet to be discovered. Most of this data will be distributed across storage, on-premises, and in the cloud. Databases can be anything from SQL servers to graph databases, which require ready and seamless connectivity to access this data.
Whenever organizations seek to use data, they encounter challenges resulting from distributed data sources, data types, structures, and platforms. Currently, most data integration tools are repurposed as data fabric toolsets; however, there is more science that needs to be done to create a true fabric that can be adopted by an organization. Discoverability, semantics, and integration are at the heart of this science.
Discoverability: Through discovery, data generated within a company is made available automatically in a central namespace, such as a data catalog. By exposing this data, it will be searchable by staff looking for data.
Enhancing Meaning: As data is discovered, it has to be defined by personnel having knowledge about its generation or usage. Additionally, relationships to people and processes have to be established in the business glossary. Business terms will then need to be tagged with the physical data discovered from the application storage.
Semantics: In spite of the fact that most of the data discovered comes from silos, they are generated or consumed in contexts. It is important, however, to generalize data so as to remove product-specificity and customer associations in order to ease data ownership and management. By combining these terms with their specific and general context, a business information model can be formed. Upon defining a complete business information model, the data fabric becomes complete as well. This activity involves collaboration between business owners, data stewards, information technology, consumers, data architects, and business analysts.
While a data fabric can semantically make sense of any data that exists in an organization, it will also need to have capabilities that can make it accessible wherever it is. Data integration tools, either ETL or ELT, often have the capability to wrangle data that comes from various sources while having to make it available after transforming and deriving it. Even if the data is in the cloud or on-premise, it should be easy to consume in a popular form. Moreover, the data fabric has to provide real-time access to data streams as it is generated from the sources of origin.
Best Practices to Manage and Govern Data Fabric
To have a well-governed data fabric, active management of business, operational, and technical metadata is vital. This requires everyone in the organization to have access to a data catalog and business glossary. This enables everyone in the organization to contribute their knowledge of data as they consume it. It’s also crucial that a schedule be maintained so that all the sources of origin be ingested for their metadata at a frequency that data makes reasonable drift.
Data Literacy needs to be imparted to business divisions through responsible personnel (e.g., data stewards) so that grass-roots personnel can have access to data and its meaning, as well as to toolsets to self-service simple needs.
Data Governance is imperative while creating a data fabric, as it provides guidance to people through process capabilities such as workflows. By doing so, consistent and repeatable results can be achieved when creating and using a data fabric. Until the metadata associated with the data is actively managed, a business information model surrounding a fabric will not be effective. With robust change management in a data landscape, Data Governance can make maintaining business information models an essential.
The core of the data fabric architecture is a Data Management platform that enables the full breadth of integrated Data Management capabilities including discovery, governance, integration, semantics, and distribution. Thus, following data fabric best practices can facilitate the use of data as an enterprise asset.