Data governance is the set of policies and rules that organizations implement for managing their data. The goal of data governance is to ensure usability, availability, consistency, and quality of the data. Bad data can cost companies millions of dollars in wasted resources. A robust data governance initiative sets the framework to reduce risk and waste and maximize data effectiveness.
Putting this process in your CRM can seem like a daunting task which is why it’s often left for someone else to figure out after data is sourced. Focusing on three concepts can help your team focus on how to get there.
1. Look beyond the shiny objects
The phrase “People, process, and technology” has been around for the last 20 years. When it comes to data however, shiny object syndrome often takes over and the people and process part of the equation is thrown out the window. It is definitely exciting to see how data can be leveraged through technology but ignoring the people and process it takes to keep these cool new tech toys current can render them just about as ineffective as what they were bought to replace.
An overemphasis on the application of data over the management of data adds up to wasted money either on technology or the data itself and often leads to frustration when long-standing data issues creep back up. You must focus as much on the people and tools you’ll need to manage the data driving your applications as you do on selecting the right new technologies.
2. Think about process early and often
Many times, people think about data and how it flows through to their customer platforms at the start of a new program or technology implementation. During this time teams are typically forced into some level of process conversation to identify where their data comes from and who maintains it in order to get the new project off the ground.
It’s pretty common for organizations to find out at this point that no one has a current data dictionary or even that there isn’t one. At this point, you’ll be wasting time and money tracking down why there are five different sources of birthdate data with no clarity on differences or priority of sources. Trying to resolve these differences in the midst of a new project, or failing to resolve them all together, will introduce costly delays, create additional phases of work, or jeopardize your ability to have a sustainable ecosystem. Thinking about the process early and regularly will avoid this problem.
3. Use compliance as a way to drive towards more clarity about your data
If reducing cost, complexity, and risks associated with a poorly functioning data ecosystem isn’t enough reason to push you to create a solid policy to manage your data ecosystem, the fear of fines and having your companies name in the headlines may just be the motivator you need! Obviously, compliance takes this risk and need to a whole new level however many of the pieces you need to have in place to comply with regulations such as GDPR or the upcoming California Consumer Privacy Act (CCPA) can be leveraged right now to make your data ecosystem more sustainable.
This recent article from Mobile Marketing Magazine does a nice job of explaining what has changed since GDPR, what hasn’t, and what’s likely to come. In the end, there is no escaping that more stringent data compliance regulations are coming. The sooner you focus on identifying where your data is coming from and how it gets into your delivery channels the better off your data ecosystem will be now and the less work you’ll have to do when new regulations come along later.
There is no denying that implementing a formal data ecosystem can be a lot of work and making it sustainable will be even harder. It doesn’t have to be an all or nothing proposition though. With a team focused on the people and process part of data governance, in addition to the technology, you’ll have the right makings for a plan to be able to create a sustainable data ecosystem within your CRM systems.
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