9 common data governance mistakes and how to avoid them

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When organizations look to upgrade and improve their data infrastructure, one of the most important elements has nothing to do with hardware or software. Instead, it is data governance that will likely determine the success of any effort.

Simply put, a strong data governance program has clear rules and guidelines for how data should be created or collected, stored, protected, accessed, used, and shared. It is as much about human activity as it is about technological processes.

Towards this end, Nicholas Askham advises organizations on how best to integrate data governance into their practices, in order to better understand and manage this data. Askham goes by the eponymous name “The Data Governance Coach” on its website and in its consulting practice.

For nearly two decades, Askham has helped organizations reduce costs and inefficiencies and stay competitive by better understanding data governance principles.

“People typically come to me because their data is in a mess and they need help sorting it out, or because they realize they’re investing money in new initiatives that fail due to poor quality data,” Askham told VentureBeat. “In addition to offering coaching and consulting to help my clients better manage their data, I facilitate my popular training courses. I think it’s important to give people the skills to make sure data is used to solve problems and make more informed decisions. »

The human side of data management

The most overlooked element in most data governance programs is the human element, Askham believes.

“Cultural change is critical to the success of a data governance initiative. In fact, I would go so far as to say that in the early stages it’s more about people than data,” Askham said. “It is vital that you achieve this change of mentality. All business users in your organization should understand that data is an asset and manage it as such. Designing and implementing a data governance framework without addressing the human side will spell disaster.

That said, the questions Askham says he asks most often relate to the “ownership” of the data.

“I get the most questions about data ownership topics: what data owners should do, who should be data owners, how to engage them, etc.,” Askham said.

To aid in these efforts, Askham has published a guide detailing “The 9 Biggest Mistakes Companies Make When Implementing Data Governance.” In brief, the errors described in his report include the following:

Mistake 1: Initiative is IT-led

“The key to data governance success is getting stakeholders to take ownership of their data and take the lead in data governance initiatives,” Askham wrote in his report, published last year. “When I do a data governance health check for companies that are having issues, it’s quite common for IT to lead the data governance initiative.”

To avoid this problem, the organization must recognize the need to take ownership of its data and take ownership of the data governance initiative, according to the report.

Mistake 2: Not understanding the maturity of the organization

“The bottom line is that until your organization is able to think about data the right way, a large-scale data governance initiative is likely to fail because the necessary communication and education will confuse or fall into disarray. deaf ears,” Askham wrote.

There are two steps to avoid this problem, according to Askham’s report. The first is to assess your current level of data governance maturity. The second is to be clear about what you hope to achieve with data governance.

“This will ensure that everyone involved in your initiative clearly understands what the initiative is trying to achieve and how it will positively impact their part of the organization, which will no doubt spark interest in communications,” said writes Askham.

Mistake 3: Data governance as a project

“Once you get buy-in from shareholders, then you’re faced with the even greater challenge of changing attitudes, behaviors and even culture towards data management. It’s going to take something a little more sophisticated than conventional project management,” Askham wrote in his report.

The secret to avoiding this mistake is to implement the initiative as a program of change with different workflows, Askham details in the document.

“Your change plan should outline the transition from the current situation to business-as-usual data governance. You should also apply best practices in terms of organizational change management and allocate a realistic timeframe,” he said. -she writes.

Error 4: Misalignment with strategy

“Unless stakeholders see how data governance will help them achieve their strategic goals, it’s unlikely to be relevant when it comes to gaining buy-in and, ultimately, to use their influence to drive culture change,” according to the report.

It helps to have a clear business outcome in mind and to be able to communicate it clearly to the rest of the company, Askham details in the document.

“They will want to know how the initiative will help them achieve their departmental goals and how much effort is needed,” she wrote.

Mistake 5: Not understanding the data landscape

“You need to have a high-level understanding of how you own and manage data within your organization,” Askham wrote. “It doesn’t need to be too detailed, and provided you start with a broad understanding, you can add detail as it makes sense.”

The key to avoiding this mistake is to define your data landscape before you start. You should also undertake some sort of impact analysis before making any changes, Askham wrote.

Error 6: Framework integration failed

Unless you effectively embed a data governance framework into the organization, any potential benefits will be short-lived, Askham wrote.

“If the data governance framework is not an integral part of your business, your business will slowly revert to old behaviors,” the report says.

“Make sure your roles and responsibilities are properly defined and that you have found suitable people for each of these roles,” Askham wrote.

You will most likely need central support to ensure your data governance framework. “It can be attributed to one person you can call data governance, or maybe even an entire team,” she wrote.

Mistake 7: Trying the big bang approach

“By big bang approach, I mean attempting a major initiative to implement everything related to your data governance framework,” Askham detailed in the document. “The big bang approach quickly turns data governance into a major project that undoubtedly requires a lot of time and resources.”

To avoid this mistake, “take a step back and take a methodical approach when determining why you are doing data governance and what you want it to achieve for the organization. You can then attempt to implement your initiative in manageable chunks,” she wrote.

Mistake 8: Checkbox approach to compliance

“If the pressure to implement data governance is coming from a regulator, then it is very tempting for organizations to seek to meet the absolute minimum required to satisfy the regulator,” Askham wrote.

To avoid this mistake, “From the start, consider taking advantage of the regulatory requirement as a driver, but don’t limit the scope of the initiative to the bare minimum. Think about how you can meet regulations and gain business benefits as well,” Askham wrote in his report.

Mistake 9: Thinking a tool is the answer

There are several tools on the market today that can help with data governance efforts, but Askham cautions against the idea that such tools are the answer to implementing a good data governance strategy. .

“For you to get the most out of a tool, you need to have a clear understanding of what you will be using the tool for,” Askham wrote. “Start by drafting your data governance framework, and as part of this exercise, determine if your organization is mature enough in terms of understanding data governance. In fact, it may be too early to start considering tools.

Finally, Askham’s report indicates that there are certain elements of a data governance initiative that data infrastructure professionals should give top priority to when managing, maintaining, or expanding their systems.

“The business needs to meet its data needs,” she said. “The days of IT making decisions about data are over because no one in the business would. Data governance is about entrusting this responsibility to business stakeholders and giving them the skills to articulate their data needs. IT should no longer have to “guess” what the business might want to do with its data.

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