Data Governance From The Inside Out (It’s All About The Business)

Are your data governance initiatives getting bogged down by committees and endless policy-making? There is another way...

Are your data governance initiatives getting bogged down by committees and endless policy-making? There is another way…

Patrick Dewald is a data governance practitioner and director at Diaku, an innovative Data Governance software and consulting services company that is the creator of the Axon Data Governance software solution.

Why do so many organisations falter with data governance at the first attempt? 

In this article, Data Governance practitioner Patrick Dewald of Diaku draws on his extensive industry experience to put forward alternative approaches and useful tactics to help you create success in your data governance programme.


Patrick Dewald will present Diaku Axon, their flagship Data Governance software solution, in our next 30 minute Data Governance Tech Briefing  webinar on 17th July 2013 – find out more…


The Data Governance Status Quo

 

Most data governance implementations are conducted through roles, committees, policies and maybe the odd data dictionary. While this approach may interest the data crowd it neither engages with nor brings much value to the day-to-day of most business users.

How can you get data governance out of the committee dark room and into the workplace?

The main challenges concerning data for the average business user are the lack of overall visibility and understanding. They face issues such as:

  • Where should I source a given data element from?

  • Are fields A and B across these systems used for the same purpose?

  • How does my data stay in sync?

  • What is the provenance of my data?

Think about your organisation for a moment. Do you face these kind of issues? Chances are there’s no ‘go-to’ point to quickly answer these kinds of questions, at least not yet.

What Lies Beneath Your Data Landscape?

Data does not occur in a vacuum; nor is it intrinsically complex. It is the environment and business reality in which it operates which makes it challenging. The typical data landscape at a large firm is fragmented across a few hundred systems, thousands of Excel and Access data sources and a few thousand processes on top of that.

If only you could make this fragmented, largely obscured, landscape a more visible and better understood whole.

It turns out that you can.

Developing a Shared Understanding

You do this by leveraging and incentivising business users. The key to a firm doing better around data is to create a better shared understanding of data and its usage beyond the data crowd. Shared understanding brings people closer together, builds engagement, fosters collaboration and lowers barriers to changing behaviours.

If data governance is at the core of bringing visibility and control around data then the foundation of any data governance function should be to make data and its usage understandable to all. An inclusive approach promotes collaboration, encourages greater responsibility and empowers people to contribute to a better understood and leaner data landscape.

Imagine data, its connections, usage, dependencies and broad business context being described in business understandable terms and recorded in a single easily accessible knowledge repository for all to utilise and contribute to.

Is this such an insurmountable task? No, we already know it’s not.

The Collaborative Model is Universally Proven

In the consumer world millions of people are collaborating together on valuable open source projects, wikipedias, networking groups and community portals like Data Quality Pro without ever meeting or even speaking to one another. Meanwhile the corporate world is falling behind. Colleagues working for the same firm and having regular face-to-face meetings still content themselves with writing up data requirements in Word, following up with a few email ping pong sessions with track changes before putting the document safely on their own little project network drive.

How can we expect this traditional approach to be leveraged going forward or form an integrated, accessible and insightful, business-wide whole? Surely we can do better!

Liberate, Collate and Connect Your Corporate Knowledge

Up-to-date knowledge on data and its usage is present in your firm right now. The challenge is that this knowledge is segmented and trapped in the heads of individuals across many functions and disciplines. That knowledge needs to be documented and shared across the business, rather than staying and eventually leaving with the people who have it. What’s needed is a framework and platform to liberate, collate and connect that understanding.

Understanding requires context. The business context of the data and how it is key to business processes, reports, projects, regulation etc can be built up collaboratively by lots of small contributions from a broad and diverse business community within the firm. Matching the distributed nature of data and its usage with a distributed and collaborative approach to building that understanding allows one to harness the knowledge already there and paid for.

In return the community gets an increasingly rich and comprehensive living body of knowledge to enable them to make sound, joined-up decisions around data.

A collaborative, systemic approach to data governance means that data knowledge is captured, made available and continually grows with time.

Data Ownership is Critical to Data Governance Success

As part of a data governance initiative the business community needs to take ownership of their data. Having people take ownership and stay engaged on the basis of a shared understanding is not only more productive, it also enables those people to make far more informed decisions around data. Our business is data, but let’s remember our data is about the business.

Taking Action

If you are reading this then chances are you may be responsible for maturing the governance, quality or compliance of your data.

To help you implement some of the ideas in this article I’ve created some questions below that I personally use within organisations to help them gauge potential areas for improvement.

Feel free to copy and adapt them where necessary. For example, I find they work well for performing an early benchmark of maturity.

  • What data knowledge assets exist in your organisation e.g. data dictionaries, business glossaries, logical data models, data quality registers? How joined-up and widely adopted are they?

  • What other business knowledge asset exist e.g. process modelling tools, policy directories, change project registers etc.? Do they allow business users to construct a connected data view across assets?

  • Do data owners and other responsible parties in your organisation feel they have a sufficiently integrated understanding of data and its usage? Is there an integrated view of critical data elements and the business context in which they are critical?

  • How engaged is the business around data? Does the business have a standard way of communicating their data requirements?

If you find these questions useful then do reach out, I have more questions that can help you dig a little deeper, they’re very useful if you’re just starting out with compliance or data governance initiatives. Just contact me via the Data Quality Pro team for more information. 


Patrick Dewald, Director and Data Governance Practitioner, Diaku

Patrick Dewald, Director and Data Governance Practitioner, Diaku

Patrick Dewald – Diaku 

 

Patrick Dewald is a Data Governance Architect and founding partner of Diaku, the creators of Data Governance software solution –  Axon.

Patrick has a wealth of experience designing Master Data Management and Data Governance solutions for financial institutions.

He has been heading up Data Governance initiatives, designing and implementing group-wide data services from the ground up for the best part of 15 years.

Patrick is recognised by his peers as a thought leader in the field of data governance.


About the Author

I am the editor and founder of Data Quality Pro. 20+ years experience of data quality initiatives.

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