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Forming Data Governance Strategies for the Enterprise

When starting a data governance program or working to comply with regulations such as GDPR, enterprise stakeholders may not fully grasp the complexity of their data architecture.

In fact, data governance may mean different things to the business stakeholders versus the technical stakeholders.

Business leaders need to collaborate with the data architecture team to create useful business process models, establish a common knowledge base, and define a consistent data governance strategy for the organization.

KMWorld recently held a webinar with Kim Brushaber, senior product manager, IDERA, who discussed how to successfully kick off a data governance program.

The benefits of data governance are vast and include increasing consistency and confidence in making data decisions, a decrease in the risk of regulatory fines, improvement of data security, maximizing the revenue generation potential of data, designated accountability for information quality, better data planning, and reduced data redundancy.

According to Brushaber there are 5 types of data:

  • Big data – Predictive Analytics
  • Fast data – Information that can be quickly analyzed (e.g. coupon upon checkout)
  • Dark data – Information that you can’t easily access (e.g. videos)
  • Lost data – Information that is collected but never reviewed
  • New data – Information that you could have but aren’t harvesting

When considering a data governance plan, there are several company data sets to include:

  • Marketing Analytics/Demographics
  • Product Information
  • Regulated Information
  • Operational Data
  • Financial Data
  •  HR Data
  • Legal Data

For companies to proceed with creating successful data governance protocols there’s a ton of misinformation and confusion.

According to Brushaber outside regulations don’t provide guidance on how to handle the data, leaving companies to figure it out for themselves.

Most companies compensate by archiving all of their data on central file servers without understanding what they have or need, leaving themselves open to greater risk.

Companies tend to ignore data points that live outside their firewalls.  In most organizations, data quality is siloed and poor to begin with.  Data becomes fragmented, inconsistent, and redundant.

When creating a data governance strategy it’s important to define the data governance processes so everyone is on the same page, Brushaber explained.

Business Process Models (pictures) can make collaboration easier and improve understanding. And a good Data Governance strategy will help you demonstrate compliance for important regulations.

An archived on-demand replay of this webinar is available here.

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