Data’s had a quick ascension to become the most critical asset for modern business. Some might say too quick. Today’s data-driven businesses can struggle to integrate existing data reserves with new, incoming data, all while keeping proprietary and valuable data secure.

At Data Citizens ’21, data intelligence company Collibra NV gathered its community to discuss best practices for data management, with experts demonstrating how they are tackling the problems stemming from data complexity within their organizations.

In case you missed the event, theCUBE — SiliconANGLE Media’s livestreaming studio –is highlighting five key insights emerging from Data Citizens ’21. (* Disclosure below.)

1) Everyone needs to become a data citizen

When companies become data-driven, data flow takes on the same importance as cash flow, which means employees need a new attitude toward how they handle data. Cybercriminals are becoming ever more sophisticated, and humans are almost always the weak link.

Educating employees on proper data management is essential to mitigate the risks associated with sharing data, according to Stijn (Stan) Christiaens, co-founder and chief data citizen of Collibra. The company has adopted the term data citizen to define employees responsible for using data in their job, which encompasses most of the workforce in a data-driven company.

“The more data we get, the more complexity, the more challenge, the more there is a need to prioritize, align with business strategy and ensure that you are embedding into the culture and the DNA of the corporation,” stated Jacklyn Osborne, managing director and risk and financial technology executive at Bank of America Corp. This year’s winner of Collibra’s “Data Citizen of the Year” award, Osbourne shared her data management tips during the event.

Data citizens take responsibility for data and are data-literate, meaning they share a common understanding of correct data practices.

“Data literacy builds a foundation for understanding around data that means you will not only improve data protection but also be better equipped to build trust in your data and innovate based on insights from your data,” Christiaens stated.

2) Changing technology and culture simultaneously

Undergoing digital transformation is the first step toward becoming a data-driven company. The technological and cultural requirements this entails are built-in for cloud native companies that manage data as a company asset from day one. It’s a different story for companies with established operations and decades of data stored away in siloed systems.

“You still have to care and feed legacy while you’re building the new highway,” said Aravind Jagannathan, chief data officer and vice president at The Federal Home Loan Mortgage Corp. (known as Freddie Mac) in a panel discussion.

But technology isn’t the only concern for CDOs managing a company’s transformation. Changing set-in-stone processes and ripping down departmental divides are also part of the process. Alongside adopting the Collibra platform to manage and govern its data resources through technology, Freddie Mac committed to building a data framework from the “people and processes” perspective.

“There has to be a tight link and collaboration between the data engineers, the data scientists and analysts, and the business stakeholders themselves,” said Michele Goetz, vice president and principal analyst of Forrester Research Inc., who presented a session on “How obsessive collaboration fuels scalable DataOps.”

Data teams aren’t just sitting in the basement or another part of the organization and digitally disconnected anymore, according to Goetz. “You’re finding that they’re having to work much more closely and side by side with their colleagues and stakeholders,” she said.

3) Assuming data is accurate is a dangerous mistake

Making mistakes is easy when digital transformation is done under pressure. There is too much complexity for the process to ever go completely smoothly. But when data-driven companies skip critical data governance steps, the result could negate the point of becoming data-driven in the first place.

“I would say that every Fortune 100 or even Fortune 1000 probably considers themselves a data-driven business at this point, which means they’re going to make decisions quickly based on data,” said Kirk Haslbeck (pictured), vice president of engineering at Collibra. “And if we pull that thread a little bit, what’s the cost of making decisions on incorrect data?”

It’s impossible to port across legacy data that has been input by humans and not encounter errors, according to Haslbeck. The problem occurs when the AI analyzing that data takes it as true. This means any decisions based on it are based on false information.

“That adds a risk,” he said. “It becomes an embarrassing moment if your data is incorrect.”

4) Artificial intelligence and automation are essential

Edge computing is bringing a new level of complexity to the already diverse and distributed landscape created by cloud computing. Incoming data is at unprecedented levels, and companies need to be able to analyze and gain insights from it in real time, or as close as possible. This is, of course, impossible for humans to handle.

“You create this massive complexity to managing the data, governing the data, orchestrating the data because it’s not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third-party information,” said Forrester Research’s Goetz.

Advanced technology, such as artificial intelligence, is the only way to handle the massive inflow of data, according to many of the data professionals speaking at the event. Collibra itself has embedded business intelligence capability into its cloud native data governance platform. Now called the Data Intelligence Cloud, the platform catalogs and tracks data as it is created, maintaining compliance with data regulations whilst providing authorized users access to up-to-date insights.

5) Low-code and no-code tools are needed

A benefit of the rise in AI is the ability to democratize access to data by offering low-code or no-code tools that allow employees to ask a question and receive an almost instant response based on real-time data. Without these tools, the complexity of diverse and dispersed data would restrict access to highly trained data scientists, putting a roadblock in the way of those who need data insights fast.

“It’s not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third-party information,” Goetz stated. “So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it’s hyper contextualized to the types of value propositions that our intelligence systems are going to serve?”

The answer comes in the form of a central library, built by data teams but accessed by non-data scientists.

“This is where a lot of the auto [machine learning] begins, because those who are less data science-oriented but can build an insight pipeline can grab all the different components — from the pipelines to the transformations, to capture mechanisms — to bolt into the model itself and allow that to be delivered to the application,” Goetz added.

Tools such as Collibra’s platform provide this level of autonomy to non-technical employees across a company.

“Imagine being able to generate those instructions from everything that we have in our metadata repository to say, ‘This is exactly the data I need you to go get,’ and perform what we call a distributed query against those data sets and bringing it back to them,” Jim Cushman (pictured), chief product offer of Collibra, said in a discussion with theCUBE host Dave Vellante. “No code written.”

Watch SiliconANGLE’s and theCUBE’s coverage of the Data Citizens ’21 event on theCUBE’s dedicated channel. (* Disclosure: TheCUBE is a paid media partner for Data Citizens ‘21. Neither Collibra NV, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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