Automating data governance: 5 immediate benefits of modern data mapping | 7wData

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Data privacy legislation like GDPR and CCPA has caused a certain amount of upheaval among businesses.

When GDPR went into effect, many organizations weren’t in compliance, and some still weren’t sure where to start. With the advent of CCPA in California, and ongoing talks at the FTC about national consumer data privacy rules for the U.S., the issue of data governance will only grow more visible and more critical.

Unfortunately, many businesses still aren’t ready. Many have no documentation on how data moves through their Organization, how it’s modified, or where it’s stored. Some attempt documentation using spreadsheets that lack version control and are at the mercy of human error.

But the implications of data governance go well beyond compliance. Here are five reasons why it’s time to professionalize your documentation and map your data using automation.

Data privacy laws are arriving whether businesses like it or not. GDPR famously caught businesses by surprise, despite a two-year heads up that it would be taking effect. Less than a month before GDPR was set to take effect in May 2018, Garnter predicted that more than half of companies affected by the law would fail to reach compliance by the end of the year. More than 18 months later, at the end of 2019, new data suggested 58% of GDPR-relevant companies still couldn’t address data requests in the designated time frame.

To achieve compliance with GDPR and CCPA, you essentially need to know how data comes into your company, where it goes, and how it’s transformed along the way. Organizations struggle to do so because they haven’t properly mapped data’s path through their environment.

Spreadsheets are no solution. To prove compliance, you need accurate, current, and centralized documentation mapping your data. Automated tools speed the process and deliver foolproof compliance.

Data scientists bring a lot of value to an Organization, but given their specialized and in-demand skills, the average base salary for a data scientist ranges from $113,000 to $123,000. More experienced data scientists command even more.

Unfortunately, at many organizations, data scientists spend 30-40% of their time doing data preparation and grooming, figuring out where data elements came from, and other basic tasks that could be automated.

When data scientists spend so much time on basic tasks, the organization isn’t just losing the time and cost it takes to do that work, it’s losing the opportunities that could be uncovered if the data scientists spent more of their time on data modeling, actionable insights, and predictive analysis.

Many companies are looking to transition their data from in-house data centers to more cost-efficient cloud databases, where they only pay for the compute power they use. It’s an opportunity to realize cost savings and modernize their environment.

But it can be a strenuous process if you don’t know what’s flowing into on-premises hardware, because you won’t be able to point the same pathways at the cloud. Documenting those pathways manually can be precarious and time consuming at best.