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When an organization is looking to upgrade and improve its data infrastructure, one of the most important elements has nothing to do with hardware or software . Instead, data governance may determine the success of any endeavor.
In short, a strong data governance program has clear rules and guidelines for how data is created or collected, stored, secured, accessed, used and shared. It is as much about human activity as it is about technological processes.
To this end, Nicola Askham has been advising organizations on how to best incorporate data governance into their practices to better understand and manage this data. Askham calls herself a “Data Governance Coach” on her website and consulting practice.
For nearly two decades, Askham has been helping organizations reduce costs and inefficiencies and stay competitive through a better understanding of data governance principles.
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“Usually people turn to me because their data is a mess and they need help unraveling it, or because they realize they’re throwing cash into failing due to poor data quality new plans,” Askham told VentureBeat. “In addition to providing coaching and consulting to help my clients better manage their data, I also run training courses that I am very popular with. I think equipping people with the skills to ensure data is used to solve problems and make smarter decisions decision is very important.”
Human aspects of data management
The most overlooked element, according to Askham, is that the data governance program is the human element.
“Cultural change is critical to a successful data governance program. In fact, I would say that in the early stages, it’s more about people than data,” Askham said. “Achieving this mindset shift is critical. All business users in your organization need to understand that data is an asset and manage it. Designing and implementing a data governance framework without addressing the people side of it will lead to disaster.”
That said, Askham says the question she is most often asked has to do with data ‘ownership’.’
“I receive Get the most questions on topics related to data ownership: what data owners should do, who should be data owners, how to get them involved, etc.,” Askham
to help these Efforts, Askham released a guide detailing “the 9 biggest mistakes companies make when implementing data governance.” In short, the errors outlined in her report include the following:
Error 1: The initiative is led by IT
“The key to success in data governance is to make Stakeholders take ownership of their data and take the lead in data governance initiatives,” Askham wrote in her report published last year. “It is fairly common for IT to lead data governance initiatives when I conduct data governance health checks on companies in trouble.”
To avoid this problem, the report said, the organization The need to own data ownership and be responsible for the data governance program needs to be recognized.
Mistake 2: Not knowing the maturity of the organization
“The bottom line is that until your organization is able to think about data the right way, a comprehensive data governance program is likely to fail due to the necessary communication and education It’s either confusing or falling on deaf ears,” Askham wrote.
According to Askham’s report, there are two steps to avoid this problem. The first is to assess your current maturity in data governance. The second is to be clear about what you want to achieve with data governance.
“This will ensure that everyone involved in your program has a clear understanding of what the program is trying to achieve and how it will positively impact their part in the organization, which Undoubtedly sparking interest in communication,” Askham wrote.
Myth 3: Data Governance as a Project
“Once you have shareholder buy-in, you face the greater challenge of changing attitudes, behaviors and even culture to data management. This will be a bit more complex than traditional project management,” Askham wrote in her report. For the different workflows, Askham has detailed instructions in the documentation.
“Your change plan should outline the transition from the current situation to business-as-usual data governance. You should also apply best practices in organizational change management and assign a realistic time frame,” she wrote.
Error four: inconsistent with strategy
“Unless stakeholders understand how data governance will help them achieve their strategic goals, the report says that in gaining support and ultimately leveraging their influence This is unlikely to be of any relevance in terms of driving cultural change.
Keeping business results in mind and being able to communicate them clearly to the rest of the business, Askham details in the document Stated this.
“They will want to know how the initiative will help them achieve their departmental goals and how much effort will be required,” she wrote.
Error five: Not understanding the data landscape
“You need to have a high-level understanding of how data is stored and managed within your organization,” Askham wrote. “It doesn’t need to be overly detailed, as long as you start with a broad understanding, you can make sense of it when adding details. “
The way to avoid this error is to define your data environment before you start. You should also do some kind of impact analysis before making any changes, writes Askham.
Error six: Failed to embed the frame
Unless you effectively integrate a data governance framework into your organization, any potential benefits will be short-lived, Askham writes.
“If a data governance framework does not become an integral part of your business, the business will slowly revert to old behavior,” the report states.
“Ensure your roles and responsibilities are properly defined and that you have found the right person for each role,” Askham writes.
You will most likely need central support to ensure your data governance framework. “This may be assigned to an individual you may call a data governance manager, or even an entire team,” she writes. “
Mistake 7: Trying the Big Bang Method
“By a big bang approach, I mean trying a big move to implement everything related to your data governance framework,” Askham detailed in the document.” The big-bang approach quickly turned data governance into a major project that undoubtedly required a lot of time and resources. “
To avoid this mistake,” take a step back and follow a methodical approach in figuring out why you need data governance and what you want it to achieve for your organization. Then you can try to implement your plan in a manageable way,” she wrote.
Error 8: Compliance method for tick boxes
“If the implementation of data governance Pressure comes from regulators, so it is very tempting for organizations to focus on meeting the absolute minimum requirements needed to keep regulators happy,” Askham wrote.
In order to avoid The mistake, “From the outset, consider using regulatory requirements as your driving force but don’t limit the scope of the initiative to the bare minimum. Think about how you can meet regulatory requirements and gain some commercial benefit,” Askham wrote in her report.
Mistake 9: Thinking that tools are the answer
There are several tools on the market today that can help Data governance works, but Askham cautions against thinking these tools are the answer to implementing a good data governance strategy.
“In order for you to get the most out of your tools, you should have a clear understanding of your The purpose of the tool is going to be used,” Askham writes. “Draft your data governance framework first and, as part of this exercise, consider whether your organization is mature enough to understand data governance. In fact, it may be too early to start thinking about tools.
Finally, Askham’s report documents certain elements of a data governance program that data infrastructure professionals should prioritize
“Businesses need to meet their data needs,” she said. “Gone are the days when IT departments made decisions about data because no one in the business would. Data governance is all about giving responsibility to business stakeholders and empowering them with the skills to express data needs. IT no longer needs to “guess” what businesses might want to do with their data. “
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