Achieving Data Compliance: What You Need to Know

Data compliance is not a static issue.

In fact, it’s been shifting for years – and fast.

As the Senior Vice President of Product Management here at Precisely, I talk to a lot of businesses who have been trying to keep up with these ever-changing regulatory requirements. Becoming more informed and prepared to tackle these changes head-on is a top concern.

To discuss data compliance in more detail, I joined Ian Murphy for an Enterprise Times podcast. Read on for a few of the takeaways from our chat, and listen to the full conversation linked below for even more.

Context is key

Over the last five to 10 years, one thing has become increasingly clear: data compliance, data quality, and data governance go hand in hand.

In fact, a recent survey we conducted with LeBow College of Business through Drexel University, found that for Chief Data Officers across organizations, 53% of the drivers toward data quality and governance, were because of data compliance.

And high-quality data requires integrity.

At Precisely, we’re firm believers that you can have all the data in the world at your disposal, but if it lacks integrity, you’re not going to be able to fully harness its possibilities.

Data integrity boils down to three components: accuracy, consistency, and the essential element of context – it’s what fuels more powerful business decisions and sets you apart from the competition.

And this demand for integrity is increasingly urgent as the mandate for data-driven decisions and machine learning (ML) projects is only accelerating. AI and ML are drivers in improving data context, but we do need to make sure that they’re working within the controls of our data compliance.

My top tip for getting there is this: focus on your data preparation and the input sources that are going to feed those algorithms – make sure to clean up the data where needed, to ensure that the information is high quality.

Then, when you’re assessing the results of those analytics on the back end of the process, you know what the lineage of that data was.

From a data compliance perspective, you want those controls to make sure that you’re able to prove – especially from an audit standpoint – that you have those checks in place and can map to the results of what controls were applied as you’re implementing the AI and ML.

Roadblocks to data accessibility must be removed

Data compliance has a negative connotation for many end users. They see it as something that stops them from accessing data, and in turn, prevents the finding of something transformative for the organization that would satisfy senior management.

The truth of the matter is that this should be a non-issue. In an ideal world, and regardless of where you work in the organization, you wouldn’t need to worry about the compliance side of the data – you’d simply be able to know that it’s secure and has that green light to access.

The reality, though, is that with so much data and the many ways to access it, we want to make sure that individuals across the company do have that sensitivity to the systems that they’re using and how they go about obtaining the data.

Ultimately, it takes a combination of process, education, and systems within the organization to really enable true data compliance, which gives users trust in the information that they’re accessing.

Data compliance has gone remote

While the shift to a work-from-home world hasn’t been without its own distinct challenges for data compliance and security, I’ve come to see it as a positive.

That’s because this unique time in our history has forced us to accelerate our initiatives around improving data security – especially with so many different types of source systems and where they’re located.

We’ve all become inherently more sensitive to the information we’re accessing – I know that I’m certainly more aware when I’m logging into my different sources by VPN from home. What we’ve been able to do from an end-user standpoint, as well as the IT organizations and security teams, is to implement programs that help increase the security around our datasets.

Another pivot has been on the collaboration front. We still have “face time” in the form of video calls, but it’s certainly different from the days of in-office collaboration.

Take, for example, the increase in the sharing of data – something that wasn’t previously commonplace in traditional office environments.

While this has the potential to raise concerns around data compliance, access rights, security, and privacy, I still believe that when the proper tools and security checks and balances are in place, this is a movement that has enabled positive change.

Swapping desktop piles of paper and spreadsheets for shared tools within our corporate network, has enabled more control and enforcement of security policies. Previously you would show someone a report at your desk, and that’s not as secure as having everyone log into the same tool and collaborate on that data. For that same dataset, we now have the auditability and traceability in terms of the access.

Moving forward with confidence

Overall, when working with data, I want to make sure we’re doing so in a safe way.

This boosted awareness around the importance of data compliance, security, data governance, and even data quality management, has been a great shift to see.

Ultimately, we want the businesses we work with to feel empowered to be more proactive about these issues, so that they can get to a place where they have the utmost trust in their data. That trust leads to more confident decision-making, and opens the organization up to even bigger, better possibilities.

To hear more from my discussion with Ian, including the importance of continuous data assessments, and applying data compliance to your data quality and enrichment strategies, listen to the full podcast.

And, for additional insights from our survey of more than 800 data and analytics professionals, download the full report: Data Professionals Speak: Trends In Data Governance and Data Quality.