Without an advanced analytics program, organizations will not be able to compete with those that can predict and influence outcomes through advanced analytics. Data science allows you to discover, test, unlock new insights, and gain a deeper understanding of a problem or question. It transforms a jumble of numbers into insights that can improve businesses, combat diseases, or even recommend the next movie you'll love. That sounds sexy. Doesn't it?

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Why does data governance not have as many fans as other disciplines?

Traditionally, data governance has often been seen as a burdensome regulation, leading to resistance from employees. To counter this, some initiatives have chosen more approachable terms, such as "data portal" instead of "data governance tool." However, overcoming this resistance is crucial, especially for anybody who is working with the data – as a data professionals who also benefit from reliable and secure data. It’s important to remember that good data governance is not just about compliance, but about protecting everyone's best interests.

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Arguments why data governance should be "sexy" for scientists

Let's do a twist on this would-be paradigm by providing some really good arguments.

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Why should data scientists care about data governance?

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More time for enjoyable tasks

Who has time to waste? Doing your job faster and with more confidence can only benefit your life. Good data governance saves time, not adds it. And it does so significantly. It's difficult to quantify the exact amount of time saved through data governance because of several factors like your company size, specific tasks, or data volume. But let’s take a look at some numbers. Data scientists spend 60% of their time on data cleaning and organization. Collecting data sets takes up 19% of their time. This means that with searching for data , data scientists dedicate around 80% of their time to preparing and managing data for analysis.  Data governance helps ensure cleaner, more organized data, reducing this wrangling time. Spend more time on more enjoyable activities. Have data for your projects without long searching and asking others.

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Automation of boring documentation

We'll stay on the subject of saving your time because it won't be reduced just by cutting down the time it takes to find suitable data for analysis. It will also reduce your work after you have created your models. Sharing your groundbreaking model shouldn't require writing a novel on the data it used. Data governance promotes data lineage, which essentially means automatically documenting the origin and transformations your data has undergone. This not only saves you the time and effort of writing lengthy documentation but also fosters trust and transparency in your findings. Stakeholders can see exactly where the data came from and how it was used, leaving no room for confusion (that means less speaking with people who do not understand your work if you know what we mean).

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Focus on the "Why" not the "Where"

To be honest, the true essence of data science lies in extracting knowledge, not managing data pipelines. Data governance takes care of the tedious details, allowing you to focus on the bigger picture – the "why" behind your analysis.  You can simply explain what your model predicts and the algorithm used without getting stuck in the details of data origin stories.

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Get an "Exemption from meetings" card

A benefit we've touched on here, and that most data professionals will appreciate with all the respect and sympathy we have for our colleagues, is a reduction in the number of conversations with business people. It can significantly reduce endless clarification meetings. A robust data governance framework provides a single source of truth for data definitions and usage guidelines. This empowers you to confidently point colleagues to the data governance tool for their questions, freeing up your valuable time for analysis.

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Key takeaway message

In essence, data governance sheds its "unsexy" reputation by offering data scientists significant time savings and increased efficiency. By guaranteeing clean, organized, and documented data, data scientists can dedicate less time to data wrangling and more time to the analytical tasks they find truly rewarding. This translates to faster project completion, increased trust in findings through data lineage, and less time spent clarifying details with colleagues – all thanks to the power of data governance.

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