5 Data Governance Mistakes to Avoid

By Shane Barker

Published on April 25, 2023

A confused reader reading about the 5 data governance mistakes on his laptop.

It’d be difficult to exaggerate the importance of data in today’s global marketplace, especially for firms which are going through digital transformation (DT). As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results.

Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8% between 2022 and 2029.

And the rise in data valuation has been compared to that of oil during the 19th century. The comparison makes sense because, like petroleum, data has enormous potential. But it only becomes valuable if it’s obtained, stored, processed, and transported effectively.

What does that mean for your business?

That depends on who you serve, where you operate, and what types of data you’re trying to manage. But whatever your industry, perfecting your processes for making important decisions about how to handle data is crucial.

Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

That means if you haven’t already incorporated a plan for data governance into your long-term vision for your business, the time is now.

Let’s take a closer look at what data governance is — and the top five mistakes to avoid when implementing it.

Defining Data Governance

Broadly speaking, data governance defines how data can be gathered and used within an organization. More specifically, it describes the process of creating, administering, and adapting a comprehensive plan for how an organization’s data will be managed. In this way, data governance has implications for a wide range of data management disciplines, including data architecture, quality, security, metadata, and more.

Data Governance tool infographic from Toolbox.com

This governance plan must contain protocols and policies for obtaining and storing data. It should also provide guidelines for accessing, transmitting, and retiring data according to the unique needs of both your business and the local compliance regulations where you operate.

A data governance plan also describes data users’ roles and responsibilities, answering questions like:

  • Who’s responsible for setting and overseeing these policies?

  • What tools and processes will this plan require and who will establish them?

  • Who will monitor information about our organization’s data needs and ensure the quality of the data we maintain?

All of this matters because data governance can impact your organization’s ability to meet vital digital demands, such as the need to maintain cybersecurity or the need to demonstrate compliance to pass audits. Effectively managing your organization’s data may improve your overall efficiency and productivity as well.

It can also help you gain key insights so you can make the most out of the data you have.

But, to enjoy these benefits, you’ll need to learn to avoid some common data governance pitfalls.

5 common data governance mistakes

1. Confining data governance to IT

Only involving your IT department in your data management plan is a common mistake. It creates a system of data “haves” and “have nots” in which those outside IT must request access to data, and those within IT must manage that access. This leads to data bottlenecks, breadlines… and lost opportunities.

For these reasons, many successful, data-centric firms will:

  • Place data management under purview of the business

  • Build out a distributed stewardship model

  • Place data management between business and IT functions to avoid the “us versus them” arguments

Indeed, using and storing data responsibly and at the highest level of sophistication requires participation and commitment from everyone in your business.

Isolating IT staff with these responsibilities (also known as “siloing” these tasks) can prevent you from constructing a broad enough framework to support your data governance efforts and help them truly thrive.

Data governance silos infographic from spiceworks.com highlighting how common silos.

In other words, siloing data governance creates accountability gaps that could have negative consequences.

For example, if you’re managing data for a massive email marketing campaign, it’s important to make sure that everyone on the account knows what’s expected of them. Don’t leave all of this work to your IT personnel alone.

Survey the organizational structure of your business and ask:

  • Who is responsible for data governance?

  • Who is already informally governing data?

  • Who else should be included and how can we make this happen?

  • Where are our data governance accountability gaps and how can we close them?

By discovering your informal network of people governing and stewarding data, you can begin to build more formal processes around what’s already being done.

2. Separating business plans and data governance

Data governance shouldn’t be seen as the sole responsibility of one department. Its potential to help your business achieve its goals should be made clear to everyone on your team.

In other words, always consider data governance when creating business plans, marketing initiatives, and project timelines. Likewise, your organization’s primary goals should also be the goals of your data governance plan!

As you create, refine, and adapt your data management policies, remember to ask:

  • How are we implementing data governance in each phase of our current operation?

  • How is our data governance plan helping us meet our clients’ needs?

  • How does it help us expand our reach?

  • How does it help us improve our products and services?

Thinking critically about your answers to these questions can help make sure that the time you spend developing your business’ data governance is worthwhile.

3. Overlooking day-to-day details

While the aspirations of your data governance plan should be big, the daily actions of putting them into place may often seem small.

Aim high while creating and improving your plan. But remember to set achievable and measurable goals along the way. Achieving these goals will help demonstrate the value of data governance to your employees.

This level of focus can also help you improve other processes. For instance, you may consider how data governance should factor into your hiring and training processes. Or how it can elevate your sales and marketing strategies.

Develop a habit of asking:

  • What data governance concerns should we consider as part of this specific project or initiative?

  • What recurring problems could improved data governance policies solve?

  • Where are we seeing the greatest success with data governance and how can we build on it?

Many governance leaders have found success tracking critical tasks, like number of articles curated or data assets cleaned. Those seeking to make a strong case for efficiency to business leadership might also consider a comparison metric that shows how much time is saved on data discovery and understanding after a data governance initiative.

4. Neglecting resources

Planning, policies, and conversations will only take you so far. Seeing your data governance plan in action will require you to select the right tools and put them to use.

Data Management

Use the goals and procedures you’ve created to determine whether your current physical data center infrastructure meets your needs. Also take stock of future plans. For example, does your business plan to migrate its data to the cloud in the next five years? What other modernization initiatives can you anticipate, perhaps in response to new or forthcoming regulations? If your infrastructure doesn’t measure up, consider upgrading or replacing it.

Illustration of a datacenter management from hashroot.com with servers and network equipment.
Data Storage

Storing large amounts of valuable data securely requires a well-thought-out ETL (extract, transform, load) process. This concept has been widely understood for decades.

But improvements to the model, such as CDC (change data capture) processes, can have a huge impact on efficiency. CDC allows the transfer of data in small, real-time increments rather than bulk loads.

This reduces wait time, conserves resources, and allows you to put that data to work for your business faster.

Data Extraction

If you choose to maintain ownership of your data, you’ll need a way to transfer it from your central data warehouse to the operational tool that’ll help put it to use. This is where a reverse ETL process is needed.

Illustration from hightouch.com of Snowflake's Cloud Data Warehouse connected to multiple data sources like Salesforce, Marketo, Oracle, Zendesk, and Google Ads.

The process can also improve efficiency by syncing the data it extracts to the tools and applications your business uses most.

Consider these questions and revisit them as needed:

  • Does our current data center infrastructure meet our needs?

  • Are we storing and extracting our data in ways that produce maximum value?

  • What aspects of our data governance plan could be improved by accessing the latest tools?

5. Underestimating legal and security needs

It’s also essential to consider your organization’s cybersecurity needs.

As applications for using and sharing data have grown more complex, cybercriminals have also found ways to expand their reach. Check Point Research reports that global cyberattacks increased by 28% during the third quarter of 2022.

Well-designed data governance plans provide safeguards against external threats and human error that could pose a serious threat to your assets.

Your data governance framework must also take privacy concerns into account. Make sure that you’re building trust with your clients and collaborators. It’s also essential to be aware of new legal developments that relate to your work.

Laws like General Data Protection Regular (GDPR) and California Consumer Privacy Act (CCPA) provide data guidance, improving users’ rights over their private information by establishing consent and control regulations and requiring businesses to have a comprehensive data privacy policy.

Since businesses increasingly rely on data analytics to drive business decision-making and optimization, it’s crucial to generate a data collection privacy policy that’s comprehensive and transparent.

Questions to consider:

  • What protections do we have in place for sensitive data and how can they be improved?

  • Does our privacy policy reflect current regulations?

  • How else should we be using data governance to protect our business?

Parting thoughts

Remember that a structural framework for making decisions about data can benefit organizations of any size or industry. But it must be designed with your needs and goals in mind.

Successful acquisition and interpretation of data will ultimately benefit everyone in your company. So, make sure everyone’s involved at the appropriate level and your system for governing data aligns with your business’ long-term vision.

Think big about what you can accomplish through data governance, but don’t neglect the day-to-day details.

Get familiar with the latest tools at your disposal and how they can help you achieve your objectives.

And remember to maintain your data governance policies with integrity and safety in mind.

Making positive commitments like these and sticking to them will allow you to feel confident that you’re using data resources to their fullest potential.

That’s it for now.

To your success!

Keen to learn more? Watch the webinar featuring a conversation with the author of Data Governance for Dummies, Dr. Jonathan Reichental.

More data governance resources

Learn more about Alation’s Active Data Governance solutions, or visit our data governance resource center for articles, use cases, and on-demand webinars.

Author Bio

Shane Barker is a digital marketing consultant who specializes in influencer marketing, content marketing, and SEO. He is also the co-founder and CEO of Content Solutions, a digital marketing agency.

  • Defining Data Governance
  • 5 common data governance mistakes
  • Parting thoughts
  • More data governance resources
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