Data intelligence critical for digital transformation

[JoannStarke]

Data fabric and intelligence technology deliver trusted insights and increased productivity for BI, analytic and data science teams. 

Organizations of all sizes collect, process, store and share customer, vendor, and employee data. Frequently, this data contains sensitive or personal information that may or may not be protected from unauthorized access. Data that could harm, embarrass, be inconvenient or unfair to an individual or business when exposed or in the wrong hands.  

Understanding the origins of data, what happens to it, and where it moves over time is known as data lineage. It provides a record of inputs, entities, systems, and processes that change and alter datasets available for analytics. It also simplifies data analytic processes by providing visibility to the root cause of errors. Historically, when a senior leader needed to validate or understand the lineage of their data, the result was a long manual process of inspecting each data source, poor results, and underutilization of business intelligence.

What’s changed? Digital transformation, double-digit data growth rates and increasing breaches have resulted in new regulations such as GDPR, PII, PI, MNPI, PHI, ePHI and SPI. Many of these regulations overlap resulting in compliance complexity. The other change is the critical nature of analytics, AI, and ML for digital transformation. Unfortunately, many of these initiatives are failing and one of the culprits is the non-productive busy work that needs to be performed by BI, data and analytics teams.

Data is spread across distributed environments:  core, multiple cloud, and edge. Analysts estimate that 70-80% of data engineers and data scientist time is spent negotiating access to multiple data silos, copying the data, cleansing it, then normalizing it with data from other silos. Then there’s the time they spend downloading, installing, configuring, and integrating these tools into their development environments without violating security, compliance, or government regulations.   

This process has inherent soft costs that don’t appear on any balance sheet, but they do impact the business. They slow down time to insights and raise a level of data distrust. Then there’s the potential of scraping everything when open source tools don’t integrate or work resulting in starting over.  

This is exactly where HPE Ezmeral Data Fabric and BigID come into play.

HPE Ezmeral Data Fabric unifies different data types and formats across core, multiple clouds, and edge into a logical data plane. The built-in namespace enables global access to the data, and the multi-modal database allows users to write in one API and read using another. Security, geo-fencing, data locality and placement is controlled through automated policies. HPE curates and supports an ecosystem of popular open source tools, preferred by data engineers and scientists, that can be layered on top of the data fabric to process data in-place as well as use Apache NiFi pull in data from mainframes and IoT sensors.

BigID layers onto the data fabric a data discovery engine that combines ML-discovery and foundation-based cataloging, classification, cluster analysis, and correlation into a single solution. This enables customers to design and enact automated privacy actions, increases security, and achieve compliance providing data teams with a better perspective of data lineage.   Without BigID, customers use siloed solutions and spend hundreds of FTE hours manually inspecting data repositories. This leads to high data storage costs, exposure to privacy and security risks. A Forrester Total Economic Impact Report verifies that investment in BigID results in faster discovery scans, reduction in legacy tools for data privacy, security, data breaches and greater insight into data. 

Watch this webinar to learn how HPE Ezmeral Data Fabric and BigID work together to provide trusted data insights without breaking security, privacy, or compliance regulations. 

Learn more at www.hpe.com/datafabric

Joann Starke
Hewlett Packard Enterprise

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