9 common data governance mistakes and how to avoid them
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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’s 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 gathered, stored, protected, accessed, used and shared. It’s as much about human activity as it is about technology processes.
Toward that end, Nicola Askham has been advising organizations on how to best incorporate data governance into their practices, in order to better understand and manage that data. Askham goes by the self-titled position of “The Data Governance Coach” for advisory practice.
For nearly two decades, Askham has been helping organizations reduce costs and inefficiencies and remain competitive by better understanding data governance principles.
“Typically, people turn to me because their data is a mess and they need help unraveling it, or because they realize that they are pouring cash into new initiatives that are failing because of poor quality data,” Askham told VentureBeat. “As well as providing coaching and consulting to help my clients better manage their data, I run my popular training courses. I feel it is important to give people the skills to make sure that data is used to solve problems and make better 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 a successful data governance initiative. In fact, I would go as far to say that in the early phases it is more about the people than the data,” Askham said. “It is vital that you achieve that mindset change. All business users in your organization need to understand that data is an asset and manage it as such. Designing and implementing a data governance framework without addressing the people side will result in a disaster.”
That said, the questions that Askham said she is most often asked relate to data ‘ownership.’
“I get the most questions on topics related to data ownership: what data owners should do, who should be data owners, how to engage them, etc.,” Askham said.
To help in those efforts, Askham published a guide detailing “The 9 biggest mistakes companies make when implementing data governance.” In brief, those mistakes as outlined in her 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 her report, which was published last year. “When I perform a data governance health check for companies that are running into trouble, it is fairly common for IT to be leading the data governance initiative.”
To avoid this problem, the organization needs to recognize the necessity to take ownership of its data and take charge of the data governance initiative, according the report.
Mistake 2: Not understanding the maturity of the organization
“The bottom line is that until your organization is capable of thinking about data in the right way, a full-scale data governance initiative is likely to fail because the necessary communication and education will either confuse or fall on deaf ears,” Askham wrote.
There are two steps to avoid this problem, according Askham’s report. The first is to assess your current level of maturity in terms of data governance. The second is to be clear about what you hope to achieve with data governance.
“This will ensure that everybody 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 undoubtedly spark interest in the communications,” Askham wrote.
Mistake 3: Data governance as a project
“Once you get shareholder buy-in, you are then faced with the even bigger challenge of changing attitudes, behaviors, and even the culture towards data management. This is going to take something a bit more sophisticated than conventional project management,” Askham wrote in her report.
The secret to avoiding this mistake is to implement the initiative as a change program with different work streams, Askham detailed in the document.
“Your change program should outline the transition from the current situation to data governance being business as usual. You should also apply best practice in terms of organizational change management and allocate a realistic time frame,” she wrote.
Mistake 4: Misalignment with strategy
“Unless stakeholders see how data governance will help them achieve their strategic objectives, it is unlikely to be of any relevance when it comes to getting buy-in and ultimately using their influence to drive culture change,” according to the report.
It helps to have a clear business outcome in mind and be able to communicate it clearly to the rest of the business, Askham detailed in the document.
“They will want to know how the initiative will help them reach their departmental objectives and how much effort is required,” she wrote.
Mistake 5: Not understanding the data landscape
“You need to have a high-level understanding of how you hold and manage data within your organization,” Askham wrote. “It does not need to be overly detailed, and provided you begin with a broad understanding, you can add detail as and when it makes sense to do so.”
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.
Mistake 6: Failure to embed framework
Unless you effectively integrate a data governance framework into the organization, any potential benefits will be short-lived, Askham wrote.
“If the data governance framework doesn’t become integral to your business, the business will slowly revert back to old behaviors,” the report states.
“Ensure your roles and responsibilities are properly defined and you have found suitable people for each of these roles,” Askham wrote.
You will most likely need central support to ensure that your data governance framework. “This may be allocated to one individual who you may refer to as data governance manager, or perhaps even a whole team,” she wrote.
Mistake 7: Attempting the big bang approach
“By the big bang approach, I mean attempting one major initiative to implement everything to do with 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 follow a methodical approach when working out 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: Tick-box approach for compliance
“If the pressure to implement data governance comes from a regulator, then it is very tempting for organizations to look at satisfying the absolute minimum required to keep the regulator happy,” Askham wrote.
To avoid this mistake, “from the outset, look at leveraging the regulatory requirement as your driver but don’t limit the scope of the initiative to just doing the bare minimum. Think about how you can satisfy the regulation and get some business benefit too,” Askham wrote in her 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 thinking that such tools are the answer to implementing a good data governance strategy.
“For you to get the most from a tool, you should have a clear understanding of what you are going to be using the tool for,” Askham wrote. “Draft your data governance framework first and as a part of that exercise, consider whether your organization is mature enough in terms of understanding data governance. It may, in fact, be too early to start considering tools.”
Finally, Askham’s report documents that there are certain elements of a data governance initiative that data infrastructure pros should place top priority on as they manage, maintain or grow their systems.
“The business needs to provide their requirements for data,” she said. “Gone are the days when IT made decisions about data because no-one in the business would. Data governance is all about giving that responsibility to business stakeholders, and giving them the skills to articulate their data requirements. IT should no longer have to ‘guess’ what the business might want done with their data.”
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