Data Management

What Is Data Governance And Why Is It Important


You’ve already heard how important data is to your business, and you’re aware of how data can help you to grow and reach your goals. But understanding the value of data is just the first step to utilizing it well.

All organizations should prepare for how to use these vast amounts of data that are available to them. This preparation means coming up with a robust plan that ensures data is collected correctly and used properly through the business.

To build this, you will need to look more closely at data governance as well as the why, how, what, who, and where of your business’ data.

Most businesses need data governance, but many don’t realize the benefits that an effective strategy can bring.

That’s why we have written this guide. For every excellent data use case, from AI to machine learning, there should exist a robust data governance strategy. We want to help you understand how data governance can affect data quality and privacy, as well as your business goals.


What is data governance?

Data governance is a set of rules and principles that ensure data quality throughout the entire lifecycle of your data.

Data governance helps to make things more transparent when it comes to data. Without it it’s difficult for people within a business to remain on the same page. It’s harder to understand where data comes from and get the most value from data.

This is much more than a simple rule for your business – data governance is a comprehensive system that can securely control data collection, usage, and understand the quality of different data types against others.

That’s why there are many tools for businesses to add data governance to their business operations.


Why do you need data governance?

Data is quickly becoming the most valuable asset for businesses across all industries. Because of this, there is a lot of data available, and it’s not easy to understand how useful this data is.

Some business models require the purchase of data; whereas an e-commerce company, public relation agency, a software house, or similar business entities are more likely to collect their own first-party data. Either way, many businesses have no way of telling the quality of this data, if it is as accurate as they expect it to be. This can become a huge problem at a later date.

As well as this data collection can be a murky process. Many businesses don’t have the correct data collection procedures in place and fail to understand the laws around data collection, management, and processing in the regions where they operate.

But why do you need data governance?

Here’s a full list of all the areas where a sound data governance solution can have a positive impact:

  • Management – data governance can help to oversee the use of all data assets. It can illustrate their value for different areas of a business and help to understand where optimization can occur.
  • Finance – for finance departments, data governance is all about the protection of sensitive data and systems while ensuring consistency.
  • Sales – for the sales and marketing departments, the use of data is an integral part of the process. Data governance is required to ensure that data is being collected and used correctly and to identify areas of low data quality and effectiveness.
  • Planning – data governance can ensure effectiveness across the supply chain and help to reduce operational costs.
  • Production – the use of data in automated systems requires a high level of data governance.
  • Legal – businesses have more data privacy regulations to comply with, so having a robust data governance system will ensure that all data being used is collected and managed correctly.


Benefits and goals of data governance

What does this mean for my business? What exactly are the benefits of implementing a robust data governance strategy?

With bad data, it’s impossible to make the right decisions at the right time. Collecting the data isn’t enough by itself – you need data governance to bring it all together.

This unlocks your business to use data more effectively. Here are some possible use cases for data governance.

  • Consistently make confident decisions based on reliable and relevant data for the specific purpose and end-user within your business.
  • Comply with data protection legislation and other regulatory requirements by documenting the permissions and usage of data from collection through to use.
  • Built a robust data security system in which data ownership and responsibilities are clear for all involved.
  • Utilize data to improve profits. This can be simply monetizing the data but can be used to drive revenue in other, less direct ways.
  • Build a robust data distribution solution between internal and external data processors.
  • Ensure that data is in the right format and as clean as possible before use, to save time performing these tasks at the point of use.
  • Build a standardized data structure to ensure the same data is suitable for different tasks in different areas of your organization.

There are many more benefits to implementing a data governance strategy for your business.


Data governance components

Now that organizations have the opportunity to capture massive amounts of diverse internal and external data, they need the discipline to maximize that data’s value, manage its risks, and reduce the cost of its management.

Data governance is made up of three core concepts: management, quality, and privacy:


Data management

The first pillar of data governance is the management of data that flows through your organization.

There needs to be the ability to control data from a centralized location and to standardize how data is used in different areas and for different uses.

This may also involve creating a central database along with a master dataset. A key motivator for data management is to ensure that the right people can access the best data when they need it, in a form that is best suited to provide the best insights and yield the best results.


Data privacy

The rise of data privacy regulations, such as the GDPR in Europe and the CCPA in California, has had a significant effect on data governance.

Different data types and purposes come with new challenges as organizations attempt to manage their privacy solutions while making sure they can get the most from the data that goes into their operations.

In this context, data governance is about having full control over the collection, management, and use of data. It requires a system that can understand who can use use the data, how it can be used, and can track this process at it happens.


Data quality

Often, companies have no way of validating or checking the accuracy of the data that they use. Building data quality controls can have a hugely positive impact on businesses, from building better insights to paying only for data that meets the required standards.

Data governance can provide organizations with a powerful system that can help identify inaccurate or inadequate data before it enters their systems. This means that end data uses are based upon better data that has been verified and can be trusted to provide the required insights.


Data governance frameworks

So how do you implement all of this into your business strategy? It requires a data governance framework.

A data governance framework is a number of rules, role delegations, and processes that aim to ensure that everyone using data within an organization is on the same page.


Step one – The mission

The first step of data governance is to ask why. This should be a mission – why are you trying to implement data governance into your organization?

This should explain why data governance is essential. It should be related to business goals, and the relevant stakeholders in the business should endorse it.


Our mission is to build a data governance solution to manage and control the collection of data from our media properties. This will mean that we can monetize our data more effectively and build trust with our unpaid subscribers.


Step two – Choose the areas to focus on

This should include the long and short terms goals of the data governance strategy. You should also define the key metrics that you will use to measure the effectiveness of your data governance program.

As well as looking at the measurement of this, you should think about the funding and resources that are required to implement data governance.


Our long term goal is to ensure that data collected through our media inventory is compliant with data protection regulations. As well as this, our shorter-term goals are to build a filter for inaccurate data and ensure that we make consumer opt-out functionality.

We will measure this by looking at the amount of data that is collectible under data privacy regulations and by looking at the quality of data that is deemed suitable for use (passes our inaccuracy filters).


Step three – How are you going to do this?

The next step is to define the rules and definitions that will form your data governance strategy. Specifically, you will need to look at the following:

  • Data policies – who is responsible for what in the daily management of data. What’s the escalation process, and how is this managed?
  • Data standards – what does useful data look like? How can you make sure that that it’s simple for everyone within an organization to understand this?
  • Data definitions – are there any key terms that need defining to ensure that your data governance strategy works for everyone?
  • Data controls – these are the processes that you will put into action to measure the adherence to the data rules and standards, as well as how your governance strategy is helping to achieve your defined goals.

After this, you can think about the tools that you will need to bring this into operation. This varies according to the needs and the organization itself. For more information and a more detailed look at bringing your framework to life, head to the next section – how to implement data governance.


Step four – Make sure you have the right people for data governance

This should be a two-pronged approach:

Stakeholder engagement

For a data governance strategy to be effective, you need to engage with the key stakeholders as well as the data controllers and data users.

Governance team

For larger organizations, this may be a team, or in smaller cases, it may be a single person. This team should be able to support the activities and governance across different areas of the business. They should also be able to engage with the key stakeholders and be able to process suggestions and manage support.


Step five – Standardise and define where your framework should be applied

The final step is to standardize this framework in the way that it applies to your business. It must be repeatable and easy to implement for every employee who uses data within an organization.

Your framework should be easy to enable but also functional enough to support every employee’s data needs.


How to bring data governance framework to your business

So you have designed a framework, but how can you add this to your company?

There are tools that aim to take control of how data is used in your business. Some of these are extremely expensive and overly complicated.

We built a tool that aims to make data governance accessible – Wult.

Wult takes control of your data tasks. But it does more than this. Wult brings built-in data governance solutions alongside these tasks.

So, for example, Wult doesn’t just help you to find the data you need, clean it, and integrate it into your business’ workflows. It also adds a layer of data governance so that you can understand privacy requirements, create roles, and uses and ensure that your companies procedures are followed throughout the process.


What stage of data governance am I at?

Identifying the stage of data governance your organization is at is a significant step to understand your business needs.

Measuring your organization up against a data governance maturity model can be a handy element in making the roadmap and communicating the as-is and to-be part of the data governance initiative and the context for deploying a data governance framework.

You can use this resource as a guide to understand exactly where your business is and how you can best incorporate a robust data governance strategy based on this.


Best practices for data governance

Every organization is different when it comes to data governance. However, we have compiled some essential tips that will apply to some.

  • Keep it simple – always take a step back and ask yourself if you are overcomplicating things. Data governance needs to meet your goals. To do this, you may need a simpler solution, or it might be easier to change to a data solution that has governance baked in.
  • When it comes to your data governance goals, you need to choose clear and measurable targets. Make sure you celebrate when you meet these and understand why it is when you don’t.
  • Clearly define the roles and responsibilities of everyone who is involved with the data governance strategy. Extend this to everyone within the organization who uses data at any point.
  • Internal documentation and definitions of critical data terms are hugely important for data governance. Try and bake this into your employee handbook and make sure it’s easy to access and makes sense.
  • Build a business case for data governance. Sometimes it can be easy to lose track of why you need a data governance solution in the first place. Linking this to clear business goals is a great way to keep data governance relevant in everything that you do as a business.
  • Educate key stakeholders and get them to buy into your data governance project.

This should be more than enough to get started with your data governance strategy. Remember, you can use Wult to build powerful, governance ready, data solutions for your business.

What is data governance?

Data governance is a set of rules and principles that ensure data quality throughout the entire lifecycle of your data.

Why do companies need data governance?

It's important for businesses to understand the laws around data collection, management, and processing in the regions where they operate.

What are the main benefits of data governance?

There are many, from unified access, better use of data, saved time and money, and more powerful uses for data within an organization.

What's a data governance framework?

A data governance framework is a number of rules, role delegations, and processes that aim to ensure that everyone using data within an organization is on the same page.