How your organization manages data says a lot about the way you do business. Data is worth its weight in gold, and poor management of this precious asset just shows that you have much to work on in this area of your business.
What is Data Governance?
First of all, what is data governance, and what does it involve? Essentially, data governance is a framework of rules and processes set out to ensure data is handled correctly. There are four pillars of data governance. These include: data quality, data stewardship, data protection and compliance, and data management.
Data quality refers to the accuracy and completeness of the data. High-quality, reliable data can be used to make fully informed business decisions and limit the risk of errors. Auditing data is important to make sure that its quality is maintained.
Stewardship basically means who has ownership of the data. An individual is usually given the responsibility of overseeing the data. They are tasked with enforcing data compliance across the organization and ensuring that it is properly used and managed.
Data protection is a term that has been used frequently over the last decade or so. Businesses are expected to abide by regulations designed to prevent security breaches. Failure to do so results in legal implications. That’s why data protection is such a hot topic.
Finally, the data has to be managed effectively. Not only does proper data management ensure that the data is readily available when needs arise, but it also means that laws are not broken and that the data is stored safely.
Why Does Data Governance Matter?
Just like any resource, data requires management in order to get the best out of it. Effective data governance gives organizations access to insightful and reliable data exactly when they need it. This can open doors to new business opportunities and drive smarter decision-making.
These protocols are also put in place to protect both the data sources and the business that has obtained it. Without data governance, data can be at risk of interception through cyberattacks or of violating data protection laws. Both incidents can ruin a business’s reputation.
For businesses that are aware they fall short in this area, we recommend checking out a list of data governance consultants and addressing this before it has a serious impact. But, no need to worry, we have the formula to fix your data governance.
Consequences of Poor Data Management
Organizations that disregard data governance often suffer from the consequences. From incorrectly analyzed data leading to poor decisions to delays in accessing data, these are common problems resulting from poor data management.
Messy data
Disorganized filing systems and storage of data can lead to duplicates and incomplete information. What’s more, if data cannot be located when it is requested by a department, this can slow down teams and workflows. In turn, messy data management results in lower efficiency.
Misleading analytics
Forming an important business strategy on the back of incorrect or poor-quality data can have disastrous consequences. Not only does this result in a project’s failure, but it also reflects badly on the company in the eyes of partners and clients,
Missed opportunities
Not utilizing data to its full potential could mean your business is missing out on profitable opportunities. By consistently tracking and analysing data, you are more likely to spot trends in customer buying behavior or areas to invest in.
What Makes a Strong Data Governance Policy?
Now that you know the powerful impact that correct data governance has on a business, the next step is to strengthen your policy. Build your framework around these four key elements, and you’ll soon see the benefits strong data governance can bring.
Upholding data quality standards
Set standards and stick by them, so that your data is of the highest quality. Data should be accurate, complete, consistent, and up-to-date. Using high-quality data to base your business decisions on reduces the risk of making critical errors that could cost you contracts, clients, and revenue.
Defined data ownership
Assign the role of data steward to a specific employee or employees. This way, access is controlled, and corruptability is limited. By giving set employees the responsibility of overseeing the data, they become accountable for it. Also, it’s not in danger of being a shared and forgotten-about task that someone else will do at some point. Make data management a priority.
Access and security protocol
As with data ownership, access to data shouldn’t be something every employee has. Access can be role-specific or granted when needed. If fewer employees have access to data, it becomes easier to monitor its use and compliance. This reduces the risk of security breaches or improper data use, reinforcing customer or client trust.
Data lifecycle management
Data has a use-by date. It shouldn’t be available for an indefinite period. A strong data management policy should outline the details of data collection and storage. This includes: how it will be collected, where it will be stored, and for how long.
As a final thought, your business goes to the effort of collecting data. The least it can do is to manage the data correctly. Why waste employee energy when, through poor data governance, the data is lost, corrupted, or results in a lawsuit? Proper policies ensure that your conscientiously collected data works for you, and not against you.
