Data stewardship is a challenge for most organizations, especially those that operate in heavily regulated environments, such as financial services and healthcare. The struggle is stitching together disparate data silos to enable enterprise analytics. What's missing is a well-managed data fabric. Data fabric is a concept that refers to the architecture of data and associated services to help organizations effectively manage and analyze their data across all sources, including hybrid and multi-cloud platforms.
The concept has arisen in response to a desperate need for a cohesive data strategy. Chief analytics officers and chief data officers have emerged in companies seeking to unite all functional areas responsible for data management. Likewise, data analysts and data scientists have become hot commodities, as organizations search for people who can deliver data-driven insights. Despite these investments, data strategies are hardly congruent. Hundreds of different data technologies are now available, leaving companies with more questions than answers as they attempt to convert their structured and unstructured data into winning customer experiences.
One necessary component to execute on this vision is a robust data fabric.
The vital role of metadata
A data fabric provides companies with a single set of data management technologies and a single way of using data across the enterprise. It stitches together the many silos of data into a congruent fabric; a very important solution for modern enterprises. There are prerequisites to establishing a data fabric. First, a data catalog is required to organize the data in a specific format and provide a single inventory of all data assets for analysis. But how is a data catalog created? Can one platform accomplish all of this?
One platform alone can’t accomplish this without metadata, or “data about the data.” Metadata looks across all the different iterations of data, including its usage and management, and allows for the treatment and use of metadata in various functions. This creates an architecture for data and leads to the architecture of a robust data fabric.
Data fabric in healthcare
The healthcare industry is loaded with sensitive data. Healthcare exchanges are centers for large volumes of this sensitive data from many different sources. When a hospital joins a healthcare exchange, it introduces a new volume of data, which includes personal identifying information (PII). This sensitive data must merge with other data which may expose it to developers and risk breaching privacy and HIPAA regulations. This is unacceptable.
With automated data engineering, a data fabric technology, the presence of PII is flagged and automatically quarantined—exposing only the metadata. By doing this, hospitals can ensure compliance while providing the hospital data and analytics community with robust metadata to do things like improve care and bolster the patient experience. With a strong data fabric, organizations across every industry stand to benefit from huge efficiency gains. For data officers and workers, much of their manual work is done for them. Engaging in this way with a robust automated solution yields greater productivity and a discernible return on investment.
Lumada for data fabric
Hitachi Vantara’s Lumada DataOps Suite automates DataOps for infrastructure, data engineering, governance, and analytics. The software suite manages data pipelines at enterprise scale and optimizes data movements and tiering across edge, data center and any cloud, producing a strong data fabric.
Lumada discovers metadata and helps protect sensitive data. And it simplifies data discovery and analytics to fuel your business operations. To explore the Lumada Intelligent DataOps Suite, visit hitachivantara.com/intelligent-dataops.