Data governance is foundational to all data and analytics programs. While not as complex or technologically demanding as data management, data engineering, or analytic modeling, its importance is immeasurable and often understated. Relevant and effective data governance programs save time, increase accuracy, and enable faster evolving analytics programs. 

The term “data governance” often elicits thoughts of bureaucracy and documentation… and more documentation. However, I tend to think of governance more as a process.  What are the frameworks that an organization needs to have in place to enable dynamic data and analytics programs?  What process steps exist (and how do the people in the organization follow them) to maintain accurate and evolving data?  This is the foundation of data governance. 

Below, I discuss what type of data governance process management needs exist at various levels of data and analytics maturity. Effective data governance programs are more dependent on process than technology or even documentation. The processes an organization establishes around its data management and analytics is the true structure of data governance and from which the organization can glean its benefits. 

Data Governance Processes at Various Levels 

Establishing a Foundation for Data Governance 

A question asked at the foundational stage might be, “What is data governance?” In short, data governance is having clear decision rights on managed data assets and having a process in place to implement those decision rights.

At this stage, it is mostly about people’s behaviors and establishing more clear ownership of data. And, here’s a hint – data needs to be owned by the business. If we think of the full data pipeline, it starts with managed data (which is business owned), there is the ELT, [extract, load, and transform] process (which is IT owned), and it usually ends with reports at this maturity stage (which are again, owned by the business). The key data governance question at this stage is about how to engage the business in strengthening the value of the organization’s data assets.

Addressing this need is really about increasing data literacy and engaging people across the organization, as well as establishing more individual ownership of data. How is this done?  At this stage, it is not about the technical tool. These are really behavior changes, and even small shifts in these behaviors will lead to immediate improvements in the data accuracy and management efficiency. By starting with the behavior changes, the movement to become a more data-focused organization will start to gain momentum – momentum that will be reinforced by the immediate ‘wins’ of more accurate data and time saved in managing that data.

Shoring Up Your Data Governance Program 

How do you strengthen and improve a data governance program? At this stage, your organization likely already has a data governance program in place, many people across the organization are aware that it exists, and even more people are noticing the benefits of improved accuracy in the analytics they consume.  The next step is to build out the program, which often happens in two ways. 

One option is to expand the program’s scope to new systems and new data domains. For instance, in the earlier stage, perhaps the governance program established clear ownership across key data areas of the main ERP system, and in this stage, the organization chooses to focus on establishing similar governance in other key systems across the organization, such as PLM, vendor management, or CRM systems. 

The second option is to deepen the program, which means to establish governance at a more granular level, in a domain that is already governed. As an example, at the earlier foundational stage, perhaps the data governance lead established clear ownership of the Product data domain in the ERP system and achieved collaborative alignment with that key data owner to actively engage in governance. At this stage, to strengthen the program, the data governance lead might support that Product domain data owner (and their team) to define clearer field-specific decision rights and change management processes within that domain. 

Strengthening the data governance program could be in one or both of these areas. At this stage, formalizing this process with a systematic process tool could be very beneficial. One approach could be to adopt the tool already used by another team in the organization. Obviously, this offers some clear benefits: adoption would be much easier since the tool is already being utilized; there could be cost savings; and there would be notable efficiencies in terms of training, security and ongoing management of the tool. However, if your organization has not yet adopted a process management software, this could be a good time to invest in a data governance platform. 

Establishing a Robust Program  

How do we design and implement a ‘best in class’ governance program? At this stage, the organization is widely aware of the governance program and individuals actively engage with it on a daily basis. It’s now time to build out more robust structures and frameworks to institutionalize the governance program across the organization.   

Firstly, not all organizations will need to adopt this level of governance. Data governance leaders will know that they need this level of governance if: 

1. there is a need to be more efficient and robust in meeting compliance requirements; 
2. the data governance council is navigating complex decisions about how best to assign resources to deliver the various data governance needs across the organization; or 
3. the data and analytics program is incorporating data from across many different systems, including structured and unstructured data, third-party data, and/or the data and analytics program is implementing more advanced, predictive analytics. 

These are all indications that a more robust data governance program is needed, and a systematic process tool could be key to deliver this program in an efficient and relevant way. Technology can certainly support a data governance program, and the nuanced needs and benefits of the organization at this stage, informed by its longer-term strategy, would greatly impact the next focus of their evolving data governance program. 

Establishing Data Governance Processes 

Data governance is instrumental today and a key enabler for a successful and evolving data and analytics program. It requires organization-wide support, and the most successful programs leverage efficient, well-established processes. I would even contend that these processes can be more important than the documentation. 

If you’re looking to establish or build upon your data governance processes, the next question might be, “How?” How to define which processes are most relevant? How to engage people and teams across the organization to embrace and engage in the governance programs? How to facilitate the process implementation and manage it ongoing? How do we start? Clarkston Consulting can help here; reach out to our data strategy and governance experts today.