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The Value of Data Governance

Posted by Jeff Gorball on 6/2/16 7:30 AM

I am often asked “what is the value of data governance?” and I am reminded of a statement I once read that said “the value of the data is directly proportional to the quality of its provenance, and the completeness and accuracy of its description”. Unfortunately, I did not make note of the source of this quote but I’d like to explore this concept today. (If this is your quote, please contact me so I can appropriately attribute it.)

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Topics: Data Governance

Data Management: What is blocking your success?

Posted by Alex Olson on 5/24/16 9:00 AM

A six year old knows Indiana and India aren't the same. 

Data management is one of the few disciplines where the state (population exceeding 6 million and known for the movie Hoosiers) and country (population exceeding 1 billion and more than 22 official languages) could be confused as being similar.

Let me explain. A client of mine noticed a hair salon transaction from India on a credit card statement. Unfortunately, the business manually entered India instead of Indiana - where he actually spent the money - providing a moment of frantic thought as he wondered if his information was stolen. Fortunately, he was able to laugh off the error because he works in the data sciences field and understood the challenges of data management.

But this leads to a potentially larger question – what causes data projects to fail?

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Topics: Data Governance

Metadata is Data Too

Posted by Jeff Gorball on 5/23/16 6:30 AM

One thing I often observe with organizations that are relatively new to data governance is their oversight to recognize that metadata is data that also needs to be managed.

Metadata, otherwise known as ‘information about the data’ is just as critical as the data itself. Metadata includes a wide range of things from who or how a piece of data was created, to transformations that were made to the data, to entitlement rights, and everything in between. Without this type of information, a piece of critical data may lack necessary integrity to be trusted, or present an inability to understand the lineage of the data. In other cases it may prevent us from being able to determine if the data is fit for purpose, such as knowledge of the point in time at which a price was set on a financial security, or sampling rate for a scientific data sample.

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Topics: Data Governance, Enterprise Data

Alignment Reveals Value of Data Governance

Posted by Tony Brownlee on 5/19/16 12:00 PM

The value of data governance is a question I’ve repeatedly been asked the last 10 years and it consistently comes up on panels, keynotes, and at cocktail receptions at different data conferences or events. Just last month I was talking with a respected industry expert about the value of data governance and data management assessments such as those that use either the DCAM or DMM models and the question came up again. What’s the value of doing a formal assessment?

The classic answer to this question is to understand where your gaps are. These models provide a great, comprehensive framework for determining what practices, processes, policies, or standards being used may not be working well. Assessments tell you where to focus resources to make measurable improvement in your plans. Bringing in a third party provides you an objective point of view that Chief Data Officers can point at to prove their enterprise data management program is making progress. 

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Topics: Data Governance, C-Suite

Let's Clear the "Gobbledygook" With Data Management Maturity

Posted by Jeff Gorball on 4/11/16 9:10 AM

Not long ago during a data management maturity workshop at a client site, I was struck by the irony that the team said they felt that the DMM Model seemed like ‘gobbledygook’ and they didn’t understand what the model was expecting them to do. This team was made up of IT professionals, scientific data repository professionals, and bonafide scientists all of whom were extremely intelligent, well respected by their peers (and me) and running a highly regarded and well-used repository of scientific data.   

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Topics: Data Quality, Data Governance

Data Governance vs. Data Management

Posted by Jeff Gorball on 3/24/16 4:10 PM

We’ve all heard the words, data governance and data management and we understand that we need these things, but what does it actually mean to say we need programs for these and are they the same thing, or different? I submit that a data management program encompasses data governance, but they are different and data governance comes first. It is a horse and cart situation – sequence matters. 

A dictionary definition of ‘governance’ says that it is “the way that a city, company, etc., is controlled by the people who run it”. A definition of ‘management’ is “The act of managing” i.e “to have control of something”. The DMM Model refers to data governance as “the senior oversight… [for the]… effectiveness of data management”. Translated, these definitions mean that data governance is all about the objectives, criteria, standards, policies, processes, resources etc… that define the rules for how we expect our data-related activities to be guided. Clear indication that data governance is the horse – it must come first.

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Topics: Data Governance

How to Scope Your Data Governance Program

Posted by Jeff Gorball on 3/3/16 8:12 AM

This blog is for you if you are responsible for the data governance program in your organization. To ensure your program is scoped correctly, ask yourself the following questions. These four simple questions will narrow the data to that which is truly important, tied to your strategic goals, and ensure that your governance program is right-sized. We can also use this exercise to help us prioritize on-boarding of data into the governance umbrella.

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Topics: Data Governance

Data Quality Requires Data Governance

Posted by Jeff Gorball on 3/2/16 7:30 AM

I’ve seen many firms throwing good money after bad at their data quality problems, cleaning the data over and over, but spending little effort getting to the root cause –poor governance over the data lifecycle.

Data quality starts in the top right quadrant in the graphic below. It starts with defining what quality requirements are, how the data is intended to be used, the criteria by which it will be measured and the standards and expectations for use. Then it requires the processes and resources to ensure that those things are understood and consistently followed – lower right quadrant.  

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Topics: Data Quality, Data Governance

4 Steps to Data Governance Success

Posted by Jeff Gorball on 2/25/16 3:24 PM

Implementing data governance is difficult, but having a program that survives and provides recognizable value is more difficult. Here are four steps to ensure success for your data governance program. 

#1 TIE TO STRATEGIC GOALS

Regardless of the reasons used to initiate the data governance program, if you can’t communicate how it supports the main business and strategic goals, you can’t sustain support. Governance initially adds work to the organization. That work must be able to tie directly to the main purpose of the business and directly support strategic goals to be positioned as value-add. 

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Topics: Data Governance

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