Data Governance Comes in All Shapes and Sizes

Data Governance Comes in All Shapes and Sizes

Data Governance Comes in All Shapes and Sizes

Having attended, sponsored, exhibited and spoken at the DGIQ (Data Governance and Information Quality) 2016 Conference in San Diego, CA, our BackOffice Associates team engaged in the latest and greatest discussions in the world of data governance and data quality. Taking a glance at attendees, there was a wide variety of industries and company sizes represented, with executives looking to gain the competitive advantage of enforcing and setting an effective data governance solution. Conference topics reflected the mindset of many attendees based on topics such as, “How to get started with Data Governance” to “Governing data in the cloud.” There was quite a span in the levels of data governance maturity and expertise.

With the overwhelming variation of size and expertise in data governance, is there really a significant difference between small and mid-market data governance initiatives compared to those at the enterprise data level?

When choosing the right data governance strategy, it is important to understand where your organization stands in the data journey, whether a small program or an enterprise-level deployment. In the end, it comes down to understanding the needs of your organization, the people involved in the decision-making process and of course budget.

Read Also:
A perfect illustration of how the big data value chain works

At DGIQ, I met all types of industry representatives, titles, lines of businesses and more. Reflecting on those conversations, it dawned on me that many people and organizations were very interested in data governance, but many didn’t know what it actually meant, where they should start, how mature they currently are, or even how mature they want to be. While these findings may not be fully accurate to every organization, overall these were the most common realities when engaging with anyone from either an enterprise organization (over $1 billion yearly revenue) or a smaller mid-market organization. 

Small to mid-market organizations typically start with defining their business terms and using a business glossary, and maybe some policy and metadata management thrown in there.   Overall most organizations felt very comfortable that defining and managing their business terms and policies made them very mature from a governance standpoint.

Larger enterprises were concerned with policy enforcement through application and/or master data management and data quality. Solving immediate problems related to the creation and maintenance of their daily business operations data were the real drivers of their initiatives, as well as gaining more business value from governance.

Read Also:
Big Data: 17 Predictions Everyone Should Read

Both are highly related to data governance and data stewardship yet their approaches and priorities are very different.

 



Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
Data Frankenstein: Bringing Old Business Data Back to Life

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
A perfect illustration of how the big data value chain works

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
The Seven Deadly Sins Of Enterprise Data Quality

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Healthcare Data Storage Options: On-Premise, Cloud and Hybrid Data Storage

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Streaming to better data quality

Leave a Reply

Your email address will not be published. Required fields are marked *