Hu Yoshida

New Data Sources and Usage Requires New Data Governance

Blog Post created by Hu Yoshida on Aug 30, 2017

Just over 10 years ago we were looking at two types of data, structured and unstructured data. Anticipating the explosion of unstructured data Hitachi began the development of object storage with rich meta data capabilities to handle the management, search, and governance of massive amounts of data that could no longer be handled by traditional technology architectures and hierarchical file systems. In 2007 we delivered the first version of our object storage platform HCP.

 

Today, there are many more forms of data and data usage that require extensions to how it must be managed and governed. Henry Peyret of Forrester research, has been researching the need for Data Governance 2.0, adapting to a new data governance framework based upon the new types of data and uses. In his research he identifies four different types of data governance by systems of insight. I have taken his four basic systems of insights and expanded on them here:

 

Systems of record. These are our traditional types of structured databases supporting OLTP and OLAP where governance is focused on quality, Master Data Management, and compliance.

 

Systems of Engagement. This is driven by new systems of customer, employee, and partner engagement, social media, chatbots, mobile apps, etc. Here governance deals with the need for data personalization versus the need for data privacy.

 

Systems of Automation. This is driven by IoT integration, OT data, event correlation, analytics and AI. Data governance not only has to be concerned about data context, as Mr. Peyret identifies, but also the source and quality of the data which may be created and processed on the edge of your business or outside of your normal realm of responsibility. What governance is required for an automated system like self-driving vehicles where data compromise may result in loss of life?

 

Systems of design. These are systems for creativity and innovation where the stake holders may be product designers or researchers. Applying standards of data governance to creativity may not be as well defined as in the previous systems. Data governance in this case may entail social, legal, ethical or moral questions.

 

Systems of Shared data. This is a category that I have added to address the phenomena of blockchain. Blockchain is a shared database for recording transactions in a way that does not allow a record to be altered at a later date. Bitcoin is a well-known example of blockchain. It is a system of value transfer that is outside the control of central banks and enables actors to exchange value independent of traditional government or regulatory oversight. While there is probably no way to govern public blockchains such as crypto currencies, there may be requirements for data governance of private blockchains in enterprises or consortium of enterprises. For instance, there are industry discussions about how banks could maintain their own privacy in a shared blockchain database. This begs the question: what is the appropriate regulatory input into a blockchain system that can automatically execute smart contracts?

 

Forrester recommends that vendors should develop a new data governance domain to address these new systems. In the picture below, previous data governance is illustrated by the blue icons, where governance was based on the processing of data and meta data. The new data governance domain shown in green, must now consider data context.

 

Forrester Governance 2.0.jpg

Forrester Henry Peyret, Principal Analyst     

 

Hitachi agrees with this assessment and has expanded their Hitachi Content Platform suite to include Hitachi Content Intelligence to surface business insights and expand the governance domain for new and future systems.

 

HCI data governance.jpg

Hitachi Content Intelligence is purpose-built to help you along your governance and analytics journey, regardless of the degree to which these five systems may be integrated into your organizational architectures. With Content Intelligence, Hitachi is focused on helping you answer the following:

 

  1. What value exists in your data or can be extracted from it?
  2. Can you trust the contents of the data?
  3. Based on your data, what has happened, what might happen next, and what is the right
      answer for your business?
  4. Is insight being delivered to the right people at the right time?
  5. How do you embed data analytics more pervasively into your organization?

 

These are questions that are current, and will continue to, plague every modern organization relying on digital assets.  As the producers and consumers of data continue to grow at exponential rates, answering these questions accurately and rapidly will place greater reliance on digital advisory and recommendation solutions.  To that end, not only does Hitachi Content Intelligence help an organization answer those question, it can also be partnered with Hitachi’s Pentaho business intelligence solution. Together, you’ll have your “databases” covered with a navigable view of your data real estate.  Together these solutions deliver:

 

HCI Pentaho Table.png

The regulatory requirements for these new systems are still a work in process in many cases. Data governance will require co-creation as we are charting new territories. By partnering with Hitachi, we can help you discover, assure, describe, predict, optimize, empower, and embed data of the highest quality and relevancy throughout your organization. We will work with you to gain greater data insights and work through new data governance requirements.

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