Modern Enterprises are data-driven entities functioning with data as the core asset for monetization. Data monetization methods can be direct or indirect. Some indirect data monetization methods use data to make measurable business performance improvements and informed business decisions. And few direct data monetization methods are sharing data to gain beneficial terms or conditions from business partners, information bartering, selling data directly, or offering information products and services. Therefore modern enterprises rely not just on their core products and services to generate revenue. Even the core products and services depend on data in every possible way.
The Data Challenge
The significance of data is quite intuitive to enterprises across industries. Yet, no single enterprise can be confident of its data and can say with certainty that they are harnessing data flawlessly and to its fullest potential. The real issue lies with exponentially increasing data, data sources, data security, and governance challenges. Data silos get created due to disparate data pipelines, and data integration becomes even more challenging. Below are the aspects of data management that increase the complexities:
- Data Cataloguing and Discovery
- Data quality and profiling
- Data lineage and governance
- Data exploration and governance
- Orchestration and Data processing capabilities
The solution
Data management tools have evolved from databases to data warehouses to data lakes, each developed to help solve new business problems. A data fabric is a next step in the evolution of these tools. It lets you continue using the disparate data storage repositories you’ve invested in while simplifying how you manage the data. A data fabric helps optimize data’s potential by automating data integration, embedding governance, and facilitating self-service data consumption. This fosters data sharing and helps accelerate data analytics for faster insights.
What is a data fabric?
Gartner defines data fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable, and inferences metadata assets to support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.
Data fabric leverages both human and machine capabilities to access data in place or support its consolidation where appropriate. It continuously identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points.
Challenges :
- Collecting and analyzing all forms of metadata
- Converting passive metadata to active metadata
- A robust integration backbone
Top Data Fabric Solutions
- IBM Cloud Pak for Data simplifies and automates data collection, organization, and analysis of data, accelerating the infusion of AI throughout a business
- TerminusDB is a cloud-based document-oriented knowledge graph data management platform.
- Talend Data Fabric is a unified platform for data ingestion, integration, governance, and sharing that can simplify building a data hub.
- NetApp offers the technologies and expertise to build a data fabric and shape a strategy around distinct requirements and goals.
- SAP’s enterprise data fabric solution consists of SAP Business Technology Platform capabilities anchored by SAP Data intelligence and SAP HANA.
- Informatica can scale any enterprise workload with elastic and serverless processing to gain fast insights when applying AI and ML to data and metadata.
Nilay Shrivastava 