Data governance
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In May 2023, more than 23,000 Tesla documents hit the streets. These documents contained personal data of more than 75,000 people, risking Tesla a fine of €3.3 billion. Moreover, these documents revealed Tesla’s trade secrets. How this could happen? It turned out that two former Tesla employees still had access to the environment where Tesla had stored these documents. They had ‘stolen’ the documents and shared them with the media.
This is just one of many examples of how poor (or no) data governance led to major negative consequences for a company. For instance, a $327 million NASA spacecraft went up in flames when it arrived at Mars. This was because the sensors on the craft measured in metric units, while the monitoring station on Earth displayed everything in imperial units.
Both examples could have been avoided with good data governance policies. So what data governance means, how to avoid these costly blunders and how we can help you do that? We explain that to you below.
What is data governance?
Data governance is an umbrella term for all agreements, processes and responsibilities surrounding the management of data within an organisation. Just as you need rules and agreements to ensure your business processes run as smoothly as possible, you also need them for your data. A good data governance policy will ensure that your data is and remains reliable, secure, well-organised and usable.
In practice, data governance means creating a policy that describes the technologies you want to use, the processes you want to set up for your data, who the stakeholders are and what responsibilities and access they have. Applying a tight policy with these elements will transform your cluttered data landscape into a value-creating asset. This will prevent decisions based on wrong data, errors in reporting or violation of privacy rules.
How is data governance implemented?
Getting from that disorganised data landscape to a well-oiled data machine requires five steps:
Exploration
Before creating a policy, it is important to understand what kind of data you are dealing with. This revolves around what kind of data is available within your company, where it is stored, in what form it is stored and what you ultimately want to use it for. It should also be clear what you want to achieve with your policy, for instance better data quality or privacy compliance.
- Classification
Once your available data has been mapped, it should be divided into different categories. These categories are based on the value and sensitivity of the data, looking at the goals you have set. For example, customer data is very sensitive in relation to privacy regulations, but your reports that you propagate may need less stringent protection.
- Policy
Create a policy in which you describe who has access to the data (by category). How should it be used and how long should it be kept? Also describe what quality standards you want to maintain and what security is needed. Ultimately, the policy should outline a clear framework for how you handle your data in line with regulations, quality standards and your strategic goals.
- Rules
To monitor your policy and implement it in practice, rules need to be drawn up. These rules cover how backups should be made, how different data categories should be encrypted and who can edit or view files. There should also be guidelines for monitoring data quality, privacy, definitions, etc.
- Implementation
Several tools are available to make data governance part of your daily work. Depending on your policy, you can select the tools you need. For instance, there are different ways to improve your data quality, secure your data and manage access. Furthermore, it is important to label the data to make the source, format and use clear. The combination of the tools and labelled data ensures good findability, organisation and security.
To introduce data governance, it is crucial that employees with different roles, from managers to technicians, are included in defining and implementing the policy. This ensures good support and awareness within the organisation and helps keep the topic alive after implementation. Without these elements, it will remain a paper policy.
What are the biggest challenges in data governance?
Creating support is also a key challenge within data governance. Therefore, data governance should be linked to the impact on employees’ work. At NASA, for example, a good data quality monitoring system had helped technicians by providing alerts on conversion from metric to imperial units.
Furthermore, ownership may be lacking in the organisation. As a result, no one feels responsible for data quality or availability, leading to errors creeping into systems and no decisions on an approach. With clear roles such as data owners and data stewards, this problem can be remedied. Data owners help establish policies and procedures. Data stewards deal with implementing and monitoring the policies.
Finally, a lack of expertise and capacity can hold your company back from taking the data organisation to a mature level. Especially in somewhat smaller companies where data is not yet part of the core business, these competences may be lacking. In such cases, we at Blenddata can help turn your company into a well-oiled data machine.
What can data governance do for your company?
Besides turning your data into a valuable asset, a good data governance policy brings the following:
- Improved decision-making
Your decisions will be based on reliable, consistent and up-to-date data. The discussion about “which numbers are right” is a thing of the past.
- Optimised operation
Instead of spending time searching for data, resolving errors, or duplicating work, you can focus on making use of your data.
- Legal compliance
By thinking carefully about what you use data for and setting your policies accordingly, you can better comply with privacy rules such as the General Data Protection Regulation (GDPR), as well as your audit obligations or industry rules.
- Higher customer satisfaction
Because your data is in order, your customers get correct information and you can answer questions faster. Furthermore, you avoid errors in your reporting or invoicing.
- Future-proof data organisation
With an organised data organisation, you can better integrate new systems or data streams into your current environment.
- Reduced risks
Since the data governance policy is also about access management and data retention, you run less risk of a data breach like the one at Tesla.
We help you process your data
With a good data governance policy, you kill more than two birds with one stone. You prevent data chaos and transform your company into a well-oiled data machine in which reliability, security, organisation and usability of your data are central. Former employees will never have access like they did at Tesla, and calculation errors will be noticed immediately by your data quality monitoring system.
We at Blenddata help you translate your data into working solutions for your business. We create a platform on which your processes run smoother and your organisation performs better. Data governance is a key pillar that keeps these platforms up. Our automated data governance policy is implemented from day 1, using the latest techniques. If you want to make optimal use of your data without compromising on quality and security, feel free to get in touch.
