Before I get into the semantics of data governance and which model is correct for your business, hopefully you had the chance to read the prior data governance blog postings from Chris Grenz and Deepak Ramanathan. I highly recommend these posts as they give you a great explanation on the importance of data governance and what to expect. With that said, you’re probably asking yourself why should I read this blog posting? Before I get into that, let’s think about what truly is data governance? Wikipedia defines data governance as an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Now that you know the core definition, hopefully you were able to pick up two important key words from the definition which are “evolving definition.” Data governance is not static and is constantly changing due the importance of data, changing of data, and it also being somewhat new and still misunderstood. Today, I hope, everyone who reads this posting can walk away understanding the different data governance models to choose from and understand the difference between the models, and maybe see if improvement and changes can be made to the conventional models we see today.
If we look at some companies today that have not put any importance around data governance or just didn’t know of it because of the time, what you may see is a decentralized governance model, where data is untamed with very minimal organization and understanding of the data.
This alone would can make anyone crazy and pull a few hairs out just looking at the data, but what about, data consolidation?
Imagine you need to submit financial earnings such as 10Q or a 10K that needs to be filed with the SEC with a decentralized governance model. Do you know how much longer and how difficult this would be? Before these reports can be consolidated for submission, you would need to ask the simple questions about the data such as what does it mean? Does it mean the same thing for all business units or just for a few? What are the translation rules to get everything consolidated so that they can mean the same thing? While companies are now becoming global companies, data needs to be more synchronized and to start, data needs standards, but at what level?
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If we look at data governance there are two conventional data governance models, enterprise and global. But before we get into the details of the different data governance models lets look at the initial structure of the model.
The conventional data governance model consists of operational layer, tactical layer, strategic layer, executive layer, and a data governance office layer. Within each of these layers are roles of individuals. Before we can get into that, you still need to chose a governance model whether it be enterprise or global for your business.
The enterprise data governance model is broken down into the following structure, Enterprise or 1 corporate/headquarters office, Business which or the business lines of the organization and Location, which are the individual locations of the business units. You will not see the data governance office layer because they play a role within all these layers, which I will discuss, in later posts.
Now when and where do we use the enterprise data governance model? Well, the enterprise data governance model approach is taken more by small or midsize companies. There also has been a case where larger global companies would also take on this approach for a line of business that has put importance around data governance. For an example I worked with a US client and the procurement side of the business wanted data governance and wanted standards, process and etc. Since they didn’t want to move forward with a global approach, the enterprise model was able to fit their needs and wants for the procurement team and their initiatives. We set the local US corporate office executive employees to be set at the executive level leaving the strategic and tactical level at the 3 different business entities they owned. Lastly, the operational level was all operated at the individual plant level. This allowed the strategic and tactical level representatives to either push forward the standards and new process to the operational level while also bring up new changes to process and requirements to the executive level to make decisions and approvals on new updates and changes around the data and process.
Now that we looked at the enterprise model, lets look at the global model and see how it can help global businesses today.
When you look at the global model you will see it has the same layers as the enterprise model here but the approach is handled differently. When you look at the executive layer for the global model, you will come to find out this is handled by global corporate offices and not just by 1 corporate entity. Giving the global corporate offices executive decision making on data standards and control allows for a uniformed global approach. For example, let’s say I’m a global company and I’m in the executive layer in North America and I want to standardize my Base Unit of Measure for finished goods. Using the global data governance model I would work with my executive committee in Asia, Latin America, and Europe to approve the standards that may or may have not been put fourth by the strategic committee. This means that the company, no matter where it is located, or which different business unit will be using the same standards put in place for Base Unit of Measure for finished goods as it would be set as a global data standard term. Similar to the enterprise model, the global model strategic and tactical level representatives will still push forward the standards and new process to the operational level while also bring up new changes to process and requirements to the executive level to make decisions and approvals on new updates and changes around the data and process. However, they would need to work with each GEO at those layers before either pushing forward the new recommended changes to the executive committee or training and pushing forward the new standards and changes to the operational layer as a global push. This help keeps everything in sync for the organization.
Let’s remember the two key words from the definition of data governance, “evolving definition.” This goes for the same for data governance models. Just because we have two conventional data governance models doesn’t mean we cannot alter and change them to fit your needs our clients needs. For example, I was once on a client who wanted to go with the global data governance model but want to incorporate some of the enterprise attributes within the strategic and tactical layer of the model creating a hybrid data governance model. This hybrid model was able to fit all their needs still keeping in place standards, process, and etc. for business need. So with that said, don’t be afraid to try to make improvements or changes to the conventional models we see today. As data, process and usability is always changing the base foundation we work with may also have to change.
Hopefully you enjoyed the following post. Please check out the great posts within the site and please look out for my next data governance posting getting into more of the roles and responsibilities within the layers of the data governance model.