How to create a comprehensive data governance plan
Consolidated, retrievable, operable data is a key business initiative. Here’s what it takes, from nailing your data strategy to implementing your data governance framework in full.
Data governance is essential in order for your company to operate to its highest capacity.
Regardless of what industry your company operates in, data provides the evidence needed to make optimal business decisions. As your organization continues to put data front-and-center throughout your overall operations, you’ll inherently be able to make more informed decisions that have a positive impact on your customers and your business.
If you’ve been following our series on data ownership since the beginning, you already know that:
- True data ownership exists only when raw data is collected directly by your company
- Maintaining true ownership of your data can only occur with a proper data governance plan in place
In this article, we’re going to dive deep into all that goes into developing and enforcing a comprehensive data governance plan.
Without further ado, let’s dig in.
Building a data governance framework
The first “phase” of data governance is to build a framework on which to operate.
The goal of this phase is to ensure that all the pieces of the puzzle are in place, which in turn will allow your data governance initiatives and efforts to operate to their highest capacity.
Let’s take a look at what this entails.
A prerequisite step: nailing down your data strategy
Before building out your data governance framework, there are a few things you’ll need to iron out.
First, you need to truly understand what you’re aiming to accomplish through the use of your data. Some key goals, here, include:
- Improving processes revolving around data collection, organization, and storage
- Empowering your various teams and team members
- Enhancing communication and collaboration throughout your organization
You’ll also need to take an objective look at your current data-related processes, and think about what needs to change and/or improve moving forward. To be sure, there will likely be a variety of strengths and weaknesses to consider in this regard. In both cases, the idea is to set goals for future growth (as opposed to keeping things status quo).
Another vital prerequisite step is to get all team members and stakeholders throughout your organization involved in the process of data governance. Since the goal is to ensure your business operations revolve around data, you need to be sure that all team members are dedicated to doing so. If a lack of cohesion exists in this regard, your entire approach to data governance can easily unravel.
Finally, you’ll want to gain an understanding of the resources needed to ensure your data governance initiatives go as planned. While this will become more clear as you build out your data governance framework, you’ll at least want to have a ballpark idea of the investment that needs to be made moving forward.
Once you’ve gone through these initial steps, you’ll be ready to get to the nitty-gritty of building your data governance framework.
Creating organizational policies and standards
In order for your data governance initiatives to run like a finely-tuned machine, you’ll need to have clearly-defined processes in place.
This means setting in stone rules for how your team will go about:
- Collecting various pieces of data
- Organizing and storing different sets of data
- Retrieving, communicating, and delivering data to your various teams
In addition to developing standardized processes for handling data, you’ll need to ensure your team members know how to work through these processes.
- Providing training sessions and other professional development opportunities
- Demonstrating and facilitating proper data-related processes
- Connecting team-wide efforts to overall business growth
- Incentivize exemplary adherence to new data governance processes
As we mentioned earlier, it’s vital that all stakeholders and team members are on the same page, here. If your various teams go about the above processes in a non-standardized manner, the chances of your data being corrupted in some way or another will increase substantially. Needless to say, this is the exact opposite of what you’ll have hoped for your data governance initiatives.
Ensuring data quality
While the initial data collection phase will ideally have you collecting raw, untouched data, you’ll still need to plan for a bit of “cleanup” on an as-needed basis.
So, you’ll need to set processes in place to ensure that the data you collect is accurate, meaningful, and usable. This means having a plan for:
- Assessing incoming data from an objective standpoint
- Identifying redundancies and discrepancies within your incoming data—and the potential reasons said issues have come about
- Digging into potential anomalies and outliers, and validating/invalidating data as appropriate
Developing data architecture and integration
Your incoming data will eventually be pumped into a variety of platforms used within your organization.
This, of course, can’t happen if your various tools aren’t connected with one another.
So, it’s vital that you develop a strong, systemic architecture that integrates and syncs the tools in your tech stack with one another. This step involves defining what tools are to be used for what function(s), and determining how and why each will integrate and work together.
Creating data warehouses and data marts
While your data will essentially be flowing through the various tools in your martech stack on a continuous basis, you’ll also need to have a centralized repository in place where your data can be stored for safekeeping.
Enter data warehouses and data marts.
Your data warehouse is the central repository for any and all data flowing through your organization. A data warehouse is meant to hold any and all raw and processed/modeled data, and is a bit more broad in scope. Basically, if the data exists, the main place it will go is your data warehouse.
Data marts, on the other hand, are subsets of data warehouses. Data marts are more specific, in that they’re used to house a certain type of data, or data that centers around a certain theme. For example, you might have one data mart that houses customer-facing data, while another would house internal data.
While your overarching data warehouse is necessary for housing all of your data—and enabling you to access, analyze, and categorize it accordingly—data marts are essential for quick and easy accessibility of pertinent data.
Ensuring data security and privacy
This ties in a lot of what we’ve talked about thus far, including:
- Systematizing your data governance processes
- Maintaining alignment throughout your organization
- Integrating tools and technology to streamline and protect your data at all times
Moreover, you might also decide to assign specific team members as a “data governance committee” of sorts. This team would be responsible for ensuring that proper protocol is being followed at all times—and that continual improvements are made not just “as needed,” but on a proactive and intentional basis.
Implementing data governance
Once you’ve developed your data governance framework, the next step will be to actually put your plan into action.
This process involves:
- Collecting and cleansing your incoming data
- Organizing your collected data for organization-wide use
- Automating delivery of data via efficient pipelines
- Ensuring adherence to data governance processes
- Testing and refining your data governance plan
Note that, while these steps are sequential per each instance of data collection, etc., the process as a whole is cyclical—with some parts of the process actually occurring in tandem with one another.
For example, the fourth and fifth “steps” are actually ongoing, in that you’ll be focusing on them throughout the overall process of governing your data. (This will become a bit more clear momentarily.)
With that in mind, let’s take a look at what the process of data governance entails.
Discover and cleanse your data
The first stage of active data governance is to collect raw data directly from the source, and “cleanse” it. The process of cleansing raw data involves analyzing and assessing it to ensure accuracy, consistency, reliability, and relevance.
The step-by-step process of data cleansing involves:
- Defining what data should be collected in the first place
- Collecting, categorizing, and organizing data in accordance with your current purposes
- Marking redundant, irrelevant, or otherwise unnecessary data for deletion
- Identifying gaps in the current data set (and repeating steps 1-3 in order to find the necessary missing information)
- Systematically reviewing the efficiency of your data cleansing processes—and improving them as necessary
Organize your data for company-wide use
While the previous step had us organizing data to be used for a specific, individualized purpose, this isn’t to say that such data becomes worthless after it’s used for this initial purpose.
Rather, you’ll want to continue using this data in some way or another for as long as possible. As long as the data remains accurate, reliable, and up-to-date, there will always be more value to squeeze out of it—as long as you’re prepared to take full advantage of this data in a variety of ways.
Now, before we get ahead of ourselves, let’s make clear that your first order of business, here, is to ensure that the original data remains intact. This is why having a centralized data warehouse is essential.
When organizing this data for different purposes, however, you may need to present it in different ways to different teams within your company. This may mean filtering out certain parts of the data, emphasizing certain parts, or otherwise tailoring your collected data to focus your various team’s attention on what the data means to them.
Again, while the original data will be held in a centralized repository, this more “tailored” data will subsequently be placed in smaller data marts as appropriate, so as to ensure that those who need to access the tailored data can do so with ease.
Automate your data pipelines
Now, it’s worth pointing out that organizing your incoming data for multiple purposes is a time- and resource-consuming process.
What’s more, undertaking this process completely manually opens the door for a myriad of things to go wrong at any given time. Every instance in which your data is manually interacted with presents a chance for the data to be corrupted, misplaced, or lost completely.
In the interest of saving time and human resources—and also mitigating potential data corruption and loss—you’ll want to bring automation into your data governance processes. This way, data will automatically be piped to the right data marts after it’s been collected and cleansed—and can be put to immediate use by whoever needs to use it, however they need to do so.
Automating your data pipelines also ensures that the above processes become systematized and normalized within your organization—with less investment needed on the part of your employees. In turn, this ensures that the use of data within your organization never ends up as an afterthought—and always remains the central focus of your teams across the board.
Ensure company-wide adherence, compliance, and communication
Piggybacking off that last point, it’s essential that all team members remain on board with your data governance processes as time goes on.
As we’ve touched on, the entire point of implementing a data governance plan in the first place is to drive business growth. If only some members of your team are focused on these processes, this just isn’t going to happen.
In looking to keep everyone on the same page, you’ll likely want to assign certain team members the task of acting as a “data governance leader.” These individuals—ideally, one for each department—will be in charge of ensuring data remains at the center of all operations within their respective teams.
What’s more, you’ll also want to minimize—and ideally eradicate—any communication silos that exist between your teams. This means:
- Adopting technology to facilitate communication throughout your organization
- Building processes that require teams to open lines of communication with one another
- Ensuring all teams understand not just how incoming data applies to them, but how it applies to all teams—and the organization as a whole
Testing and refining your data governance plan
Let’s be clear: Your data governance plan isn’t going to be perfect from the get-go.
In fact, it will never be perfect—because it’s impossible for it to be so.
There will always be something about your approach to data governance that could be improved in some way or another. Not only will your team never be perfect (no team is), but ever-evolving technology ensures that “perfect” today and “perfect” tomorrow will always mean two different things.
The takeaway: Never rest on your laurels when it comes to data governance (or any other aspect of your business, for that matter). Instead, make testing and refining your data governance plan, in a “meta” sort of way, a continuous part of your data governance processes.
Simply put, if you’re always looking for ways to improve, you’ll always find them. In turn, you can implement the changes as needed—and get back to work in finding more areas in need of improvement.