The friction that leads to fruition: How to make data become mission-critical data

220126 Agne 7
Agne Nainyte
Jan 24, 2022 · 4 min lezen Engels
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As companies more and more move to the cloud, collecting, storing and managing data has finally become inexpensive. Even better, with both the data and the relevant tooling now easily accessible in the cloud, the threshold for getting started is lower than ever – it takes just one credit card swipe. Data-driven decision-making is the only way forward for organizations that want to survive disruption and fully embrace the benefits of a digital transformation. Using data enables us to tackle business problems on the basis of facts and figures rather than just going with a gut feeling or being led by intuition. In turn, it advises us on how and when to seize opportunities that let us achieve greater business value.

While most organizations recognize the importance of data and already work with it, data projects all too often become overwrought, drawn-out, expensive, and ultimately useless for making good business decisions. In other words, the data doesn’t become mission-critical. This is why when non-negotiable virtues, such as business continuity, license to operate, and maintaining a market share are at stake, organizations operating in the world’s most crucial-for-society industries rely on us. We help them keep their essential activities running steadily and securely. It’s what we call our 100% uptime guarantee. By enabling organizations to make their data mission-critical, we ensure their data creates real value. Here’s how.

Getting to the question behind the IT problem
Sometimes when we’re working on a data project, we have very feisty debates. This often stems from the fact that our customer teams are composed of different people with different skills and different expertise areas. Working alongside the customer, some of us are data scientists, while others are cloud or data engineers. And some of us are business process specialists. Nevertheless, as a single team of experts, we always strive to fully analyze the problem in the first place. Those of us who are hands-on engineers are especially eager to roll up our sleeves, start developing proofs of concept, giving customers models to see and behold. At the end of the day, we all share the same drive to tackle the challenge that underpins a project: the real question behind the IT problem.

Our drive, a mindset for delivering 100% impact, is strong, and the conviction of each teammate is so pronounced that we regularly engage in heated conversations. During them, everyone is equally invited to share their standpoint, no matter their years of experience or title. Naturally, friction arises. But we welcome it. We believe that this combination of the right people in the right setting – experts in the lead, as we call it – lets us find the best, most fitting solution for each business problem.

Above all, to develop the most successful solution for any data project, we make sure that we all identify and agree on the business problem. This requires a 360-degree view. Getting that vantage point comes automatically because we have already involved all the relevant experts right from the start of a project. Practically speaking, this implies that we don’t have many project handovers across roles or departments. The team that launches the project is the very same team that concludes it. And, to reiterate, we’re hardly shy to speak up and argue our points.

Greater business ambitions
To illustrate, we can pull back the curtain on a case we’re currently working on. One of our customers is a large European payment provider that now has its data and analytics engine running within its own servers in its own building.

This company, like others in the financial sector, handles enormous amounts of transaction data that is highly sensitive. Mishandling or poorly governing that data can lead to disruptions in crucial-for-society operations as well as other potentially costly and reputation-damaging complications.

Aware that maintaining on-premise datacenters would prove even costlier in the future, our customer indicated a readiness to move its big data warehouse to the cloud. Making that decision, the company was focused on the near future. Its concerns were to what extent the cloud would perform as needed and whether some existing toolsets could be used instead of cloud-native versions. But our data scientists and engineers both probed. With the customer, we conducted an in-depth exploration into how a migration could yield additional business value rather than cost savings alone. We asked: what additional functionality could be realized during this migration? And how could we speed up both fulfilling regulatory requirements and the time-to-market of new products? Plus, how did the company want its business – and its bottom lines – to look in three or five years? And a decade from now?

Together with the customer, our data scientists created a business case showing how a migration of the data platform could improve quality of the company’s regulatory reports. They proposed ways to add functionalities to the IT landscape and solve existing problems to improve daily operations. Equipped with such analyses, our engineers could go into action mode. They began building the initial proofs of concept with the customer to show how the first data pipelines, reports, and dashboards can run in the cloud and what advantages there would be to modernization, such as through giving up legacy tooling.

Where why and what meet
In this case, as in many others with our customers, the desire from the perspective of business process experts to rightly understand the why and the desire from an engineering perspective to execute the right what support each other. That support is generated in the process of finding the best, most fitting solution for each business problem. While sometimes our data scientists want to spend a lot of time analyzing the problem and our engineers are revving to get started on technical development, our solution is always a collective effort that combines everyone’s expertise.

On our customer teams, the feisty debates thus serve as assurance. We know that the friction caused between insisting we deliver 100% IT uptime and insisting we deliver 100% business impact leads to fruition. It keeps mission-critical activities running stably and securely no matter how far along organizations are on their digital transformation. This, in turn, lets organizations innovate, expand, and experiment. In that way we help customers make their mission-critical data value-creating data.

By Frank Buters