I’ve been a data practitioner responsible for the delivery of data management strategies in financial services, online retail, and just about everything in between. In all of these roles, I’ve come across patterns that enable organizations to build faster business insights and innovation with data.
These patterns encompass a way to deliver value to the business with data; I refer to them collectively as the “data operating model.” It facilitates the alignment of people, processes, and technology toward a common vision and objective. Organizational outcomes such as being data-driven, data democratization, automation, self-service, developer velocity, and delivering faster insights and increased revenue are all can result from the efficiency that a data operating model engenders.
These outcomes are attractive, but for practitioners like you, execution is where the rubber hits the road. In this article, I’ll explore the three execution patterns I’ve come across that have engendered success with data: cloud-native technologies, real-time data, and open source software.
If, as Gartner puts it, an operating model brings the broader business model to life, then execution patterns are an important part of breathing life into an operating model. Patterns maintain consistency when executing on the operating model. Mike Tyson is often quoted as saying, “Everyone has a plan until they get punched in the mouth.”
Similarly, an operating model can be challenged when there are changes in leadership, architects, technical leaders, developers, product managers or new additions to a technology stack. But established execution patterns help the operating model, strategy, and vision stay on track. They’re also an excellent aid to bringing on new team members quickly.
The first execution pattern is cloud-native. Cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives by 2025 — up from less than 40% in 2021. Why are enterprises shifting to the cloud? They’re trying to leverage the benefits of the private, hybrid, or public cloud. Lower total cost of ownership, scalable unit economics, multi-region reliability, digital transformation, faster delivery of applications, and machine learning models—these are all business benefits of cloud-native adoption.
Communicating the business value of cloud-native adoption is an important part of this pattern. Cloud-native is much more than cloud, Kubernetes, services, CI/CD, and automation. In the context of applications and data, creating and maintaining a cloud-native strategy provides portability, resilience, fault tolerance, scalability, and flexibility. A cloud-native pattern helps manage the costs and resources of the technology stack for the business in a consistent way.
Speed helps drive innovation. The faster applications can be deployed, data can be integrated and refined, different algorithms and data sets can be tested for new models, the faster business can make new decisions. A cloud-native pattern helps reduce barriers to innovation, supports frictionless change, and enables innovation with data to happen faster.
The ability to assess data in real-time is set to be one of the biggest data analysis trends for 2022. According to Gartner, more than 50% of new business systems will use real-time data to improve decision-making by 2022. Making decisions faster in real-time with trusted data leads to a competitive advantage.
Real-time data flows through a data ecosystem. The “easier” the right data can flow to the right people at the right time, the healthier the ecosystem is for generating business outcomes.
