“We want to give users the ability to have a simple experience. That means all data users, whether in the build, govern or discover stages of data harnessing”

“Data-driven” has become a buzz word in all areas of industry. Many people know what data is, they know how to extract it and handle it, but very few of them can extract business value with a repeatable and govern process over time. Enterprise companies rely on data to drive decision-making and to comply with standards of regulation, but the complexity of the IT environment, the fragmentation in several data siloes and the speed of evolution of the data technologies market can dampen the benefits of data could drive.

Agile Lab, a leader in data-engineering initiatives, knows how to help companies to change their way of working on the data governance life-cycle and revolutionize their understanding of data, providing sustainable data architectures and automated platforms driven by data governance best practices. “We want to give users the ability to have a simple but effective experience. That means all kind of data users, whether in the build, govern or discover stages of data harnessing,” says Alberto Firpo, Co-Founder and CEO of Agile Lab. Each stage affords a different journey. The build stage seeks to enhance the development experience facilitating the developers with different levels of skills. The govern stage means implementing standards across the data life-cycle to solve data governance concerns and comply with regulations. And lastly, the discover stage revolves around the data consumption in general, examining the ways in which data can be accessed rapidly to drive a decision-making process.

All the three stages, Firpo is quick to point out, represent a whole end-to-end experience. Agile Lab can bridge the gaps between these journeys so that an integrated approach is woven into a company data strategy. “We can go broad taking care of the entire life-cycle, involving the whole community of data practitioners, data producers, platform team and data consumers or instead we can choose to be much more narrow and be concentrated on a specific capability and problem to solve,” Firpo explains.

Both approaches are included in the “technology agnostic” solution that Agile Lab affords clients trying to harness the power of their data. Further, these approaches become woven into Witboost, Agile Lab’s modular platform that allows to build Self-Service capabilities on top whatever data technologies a Customer has chosen, helping the data practitioners to do the “right thing in the easiest way” complying with data governance best practices, saving time and money. “It's an agnostic approach that is helping a lot of companies to evolve over time in the fast-changing data tech market,” Firpo says.

Innovation and Beyond
Witboost responds and solves some of the challenges that companies face when trying to leverage their data and make sure that its usage complies with data governance standards. “Data producers and engineers practically need to inject the [data governance] standard in the development lifecycle because the producer needs to do everything in the safest and quickest way possible. So, this standard injection is something that stays in a platform that allows a platform team to input governance policies and those policies become code, they become computational. We're able to track what is happening before the data are deployed or once the data are in production. And so, in this way we can be automated in building and enforcing governance. That way, data consumers are then able to get into the data marketplace knowing they can trust the data they find,” Firpo says.

These factors of ensuring safety, building it into systems, and then being able to work through the data marketplace to drive results, speaks to some of the challenges companies face now in the data environment as a whole.

Complex organizations with more than one business unit have large communities of data users, all for different purposes. The data landscape is very fragmented, meaning it “becomes increasingly difficult for an enterprise company to have centralized data projects or have the totality of data lifecycles in one single technology. Fragmentation is obviously serving some specific needs; that’s why companies utilize different technologies, but integrating them tactically can become very expensive,” Firpo says. These different technologies that can collect different data aren’t unified, from a data governance standpoint: this means it’s becoming hard to enforce data governance best practices on many data siloes that don’t necessarily speak to one another or concern one another.

Simplifying Data Sets
What Agile Lab, with Witboost, promises to do is unify all data siloes, helping create an environment that data engineers and producers can work through to comply with standards and which can also lead to opportunity, creating integration that gives scope to all the different data sets coming from all information sources. “Witboost is an orchestrator. It stays on top of the complexity and the technology choices that the companies, our customers, have made overtime in terms of infrastructure, and the platforms that require different governance strategies. Those choices must be preserved and then we want to stay on top with a layer that encompasses everything. So, we are able to control everything and to orchestrate the expectations that are needed from each one of those siloes. Witboost’s scope is to glue everything together without entering into the specific technology life-cycle (ETL/ELT, data transformation, data quality, etc) so that the instruments used by the data practitioner can remain the same.”

In terms of governance, Firpo says that the number one priority of Witboost is to act as a guardrail system for data practitioners trying to “embed” data governance requirements. “Incorporating templates into all structures ensures that the standards are being met by design, whether it’s paying attention to infrastructure constraints or privacy or whatever else. Witboost guides users through that, day by day, with a very little effort.”

Firpo stresses the importance of a day-by-day approach as requirements and data environments change constantly. “The consumer must receive transparency and completeness in terms of the information that is relevant for him/her. So in this way the consumer can trust that basic information and then he or she can drill down and also jump to other existing tools like Data Catalogs. This extends to very technical processes while guaranteeing the integrity of those sets of information. In this way, the data marketplace can then be accessed faster because the consumer is able to trust the data using Witboost,” Firpo explains.

Firpo also explains how Witboost’s capabilities align with company strategies by giving a recent use-case example. In one instance, a very large banking company in Europe needed to build a data mesh platform that was going to provide the right guardrails for developers, or in other words, their internal team, as well as for other outsourced system integrators. “There was plenty of technology and data coming from siloed applications, including things like Palantir that they used for marketing and sales, GCP used for new applications in the cloud and Teradata warehouses used on more of the finance side. The challenge was to be in such a complicated scenario where there are a lot of stakeholders in place while still needing to push transformation towards data mesh by decentralizing the ownership of producing the data. They needed a faster way that included everything. So, we created a first fully fledged Self-Service platform complying the Data Mesh principles after four months, going through the whole set of components and connecting all of them to one another. We created a full data catalog that helped the company build and market. We also enforced reusability of the data in general, avoiding data duplication. This helps to safeguard in those kinds of situations and we’re providing a better standard in general to be able to work through the data sets,” Firpo explains.

This level of automation as it pertains to implementing policy and integrating different data siloes together is what gives Agile Lab an edge over competitors. The ability to be agnostic also means that Agile Lab isn’t forcing companies to be centralized or decentralized regarding the organizational model; Agile Lab works with the company towards the best integration possible, no matter what that looks like. It also means that companies using Agile Lab solutions can be future proof, helping to move from one technology to another or to change gently the organizational pattern in a more transformative way.

Agile Lab hopes to help companies become even more future proof in their data environments, helping to build ecosystems that bridge all data technologies while giving companies the ability to create insights into data that can drive strategy and organizational results.