Harness data’s value from edge to cloud

[JoannStarke]

Businesses are under mandates from senior leadership to harness the power of data, which is easy to say but hard to achieve.

Companies can do interesting things with analytics. In fact, across all parts of the organization, data and analytics are playing a key role in optimizing resilience by helping business respond faster to change. But if you’re like most, there are several obstacles in your way.

The data conundrum

Data is sprawled across data lakes, warehouses, multicloud, edge, and on premises in proprietary infrastructures known as data silos. Often owned by multiple entities in different geographic locations, siloed data can’t be fixed through organizational hierarchy because no single executive is accountable for managing it across different entities. Because of this, it can take four to six weeks to stitch together and normalize the data—which is not fast enough to bring real-time value.  

The demand for analytics is growing across all groups within the organization; however, traditional infrastructure and analytic solutions can’t keep pace with the large volumes and type of data being generated every day. It’s difficult stitching together data from multiple systems and solutions and then normalizing it fast enough to bring real-time insights to the business. When data trust and quality are low, business outcomes, customer satisfaction, and operational costs are impacted the most.[1]  

Because data has become the engine to business, organizations need to go beyond compliance and protection and treat data as the important asset it is. That means it needs to be centralized, de-risked, and available for reuse by multiple teams within the organization.[2]

What is a data fabric?

Data fabric is defined as:

"Orchestrating disparate data sources intelligently and securely in a self-service manner, leveraging data platforms such as data lakes, data warehouses, NoSQL, translytical, and others to deliver a unified, trusted, and comprehensive view of customer and business data across the enterprise to support applications and insights."[3]

In other words, data fabric is an enterprise technology that creates a single layer of cleansed data available for reuse across multiple use cases, users, and groups.

Delivering connected data across siloed initiatives is hard. Centralizing data spread across multiple entities and geographic locations is time consuming and results in poorly integrated, and potentially, inaccurate data.  Therefore, decision makers often don’t trust the data, which leads to sub-optimal experiences and reduced competitive advantage. It can also place an organization’s integrity in question, especially if the data is used in financial reports to shareholders and government agencies.

HPE Ezmeral Data Fabric

HPE Ezmeral Data Fabric weaves different data types across multicloud, edge, and on-premises infrastructures into a single logical data layer that increases data’s accuracy and trust. It works around data silos by ingesting files, objects, NoSQL databases, and streams into a single data store while maintaining data access, data placement, and security policies. A single data layer enables reuse of data across different use cases and applications and reduces the need to create and maintain separate infrastructure by data type.

HPE Ezmeral Data Fabric simplifies the four pillars of security with an integrated model that automates authentication, authorization, auditing, and encryption for both data in-motion and at-rest. Using Boolean expressions (ACEs), both course and fine-grained access controls are transparently enforced. This process simplifies large-scale deployments that span multiple data sets, users, and applications. HPE Ezmeral Data Fabric is FIPS 140-2 Level 1 compliant for Red Hat and Ubuntu, allowing customers to leverage their existing cryptographic libraries.  

HPE Ezmeral Data Fabric is optimized for analytics by accelerating AI and ML with pre-integrated and tested open source and third-party tools and libraries that can be layered directly onto the data fabric. The HPE Ezmeral Ecosystem Pack reduces time spent integrating and configuring open source tools for analytics while at the same time allowing for real-time, in-place analytics -- no matter where the data is located.

The multi-model NoSQL database management system delivers flexibility by supporting real-time and operational workloads from a single solution. Native JSON support reduces application development complexity on data models that span a variety of use cases. Real-time data masks are applied in transit to hide sensitive or PII query results without impacting the integrity of original data sets.

One of HPE Ezmeral Data Fabric’s critical principles is its ability to work with existing data centers, lakes, warehouses, cloud, edge, and management platforms without impacting security and data governance practices. This capability allows organizations to get more value from existing data estates by making data available for reuse for a variety of use cases, apps, and users.

Simplify data management while enabling data reuse by multiple data and analytic teams. The multi-model database management system reduces the need to create and maintain dedicated infrastructures by each database and data type, reducing the amount of infrastructure required for analytics. HPE Ezmeral Data Fabric can help you stop spending endless cycles of money and staff effort to merge, remerge, and redeploy silos of data management with new silos.

Watch this video to learn more:

Joann

[1,2] In Data We Trust—Or Do We?  IDC, 2022

[3] Enterprise Data Fabric Enables Data Ops, Forrester, 2021

Hewlett Packard Enterprise

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