Processing huge amounts of information is a routine of data scientists in the contemporary world with the onrush of data. With such a responsibility the need to ensure that data is used properly but at the same time is used securely and ethically. This is where data governance comes into play Data governance can be well understood by the ability to use data for business advantage and innovation as well as the capacity to translate it into value for the organization. But it boils down to managing data that is safe and adheres to as many regulations as possible.

But let’s say you asked yourself, ‘Okay, how on earth should I apply data governance to the data science group I am in charge of?’ This blog will cover some tips to ensure that data governance stays at the forefront and also for your team to remain productive at the same time.

1. Start with Clear Goals and Guidelines

Every system or policy is expected to be grounded on an observed goal that defines the end purpose of a particular system or policy. Ask yourself: What is your goal for data governance? It could be enhanced protection of data or use of data, improved analysis of data, or adherence to rules such as GDPR. Remember that before you start trying to accomplish something, your goal must be very clear to you.

Once you have your goals, set your guidelines. These will serve as the guideline for how data is gathered, processed, retrieved, and used within your business. Thus, all members of the team need to know these rules so that they can adhere to them rigorously.

2. Select a Data Steward or Governance Leader

Even in designing your data governance plan, you require the services of a person who will implement it. This is where a data steward or governance leader comes in; if it does not exist in your organization then you need to create one. He/She also reports directly to the main in-house data governance resource. They wake up knowing that there were processes put in place, policies being implemented, data being protected, and problems being solved.

This is why having a specific person in charge is beneficial. It means data governance is not an add-on or an extra that may or may not get attention but is part of your team’s routine.

3. Make Sure Data Accurate and Reliability

Of course, it is far easier said than done to have massive amounts of data than to parse out and amass quality data. The problem with bad data is that it leads to bad decisions, or nobody wants that. The data needs to be cleaned, validated and checked correctly at least once for errors occasionally, and it becomes necessary to have your team do it effectively.

This could mean establishing some form of automated reminders or putting together people who will check the document depending on what you require. Always strive to be accurate in all your data science projects and you end up saving a lot of trouble.

4. Put an Emphasis on Access Right

Another important concept of data governance is the concept of data access, or control features, whereby only the right people get to look at the right data at the right time. This means that the principle of various access controls should be implemented as clearly as possible. This means setting permissions based on positions or rank with your team or coworkers. Only some employees require all the data in their working process, Sometimes it’s better to lock some data even for the administrator, so the idea is connected with the concept of restricted and delayed access to information.

For example, a low-level data analyst may only require to access certain data sets while a high-level data scientist may have rights to access all data sets. When defining who can access what, you reduce the likelihood of members of a particular team exposing organizational data to threats or even using it inappropriately.

5. The policies have to be reviewed and updated systematically

Even when data has been governed, it is not a process that is ever completed. It is a solution that requires enhancements from time to time as you build up teams, and find yourself exposed to better technologies and legal requirements change. You need to ensure that the organization’s data governance policies are updated regularly.

This could be quarterly or biannually, depending on the team’s size and the level of data processing. This way, your team remains legal, your information is safe, and the policies are up to date.

6. Develop an Accountability Environment

Data stewardship and governance are not owned by the data steward or the governance leadership team. It’s a team-wide commitment. What you need to do is make everyone on the team own up to how they manage data or how they work with data information.

It should be done by composing formal training sessions or by ensuring all the people recognize the purpose of those rules. The deeper the people in the team are committed to protecting data, the better it will be to practice good governance.

7. Employ technologies to automate the process.

Last but not least, mutual utilization of the technology available. Fortunately, there are many tools you can obtain today that will assist in the automation of your data governance strategy. Of course, this greatly simplifies the task; starting from data validation software and ending with access control management, much of the work can be automated.

However, there is always the problem of identifying the correct tools for your team. Do not go for complex specifications, which may negatively impact the work of your team members. Select those tools that can help improve governance and make things easier for your team.

Conclusion

Data governance does not always have to be complex; with defined objectives, the formation of a competent team, and proper software to support the process, the task is much easier to accomplish. If your team follows these best practices, not only will you protect your data, but your team will be well-prepared for the future.

It is important to note that good data governance is not just a compliance issue, but an issue of consistency, credibility, and trust within the organization, and with the consumer.

In case you are interested in updating your knowledge in data science then you ought to consider joining a data science course in Chandigarh. Happy data governing!

How To Set Up Strong Data Governance: Good Practices For Data Science Groups