By George Trujillo, Senior Data Strategist, DataStax
I’ve been a data practitioner responsible for implementing data management strategies in financial services, online retail, and just about everything in between. In all of these roles, I’ve encountered patterns that allow organizations to create business insights faster and innovate with data.
These models encompass a means of delivering value to the business with data; I call them collectively the “data mining model”. It facilitates the alignment of people, processes and technology towards a common vision and purpose. Organizational outcomes such as data stewardship, data democratization, automation, self-service, developer speed, and delivering faster insights and increased revenue can all result from efficiency that a data mining model generates.
These results are appealing, but for practitioners like you, the execution is where the rubber hits the road. In this article, I’ll explore the three execution models I’ve encountered that have driven success with data: cloud-native technologies, real-time data, and open-source software.
Execution models in an operating model
If, as Gartner says, an operating model brings the larger business model to life, then execution models are an important part of bringing an operating model to life. Models maintain consistency when running on the operating model. Mike Tyson is often quoted as saying, “Everyone has a plan until they’re punched in the mouth.”
Similarly, an operating model can be challenged when there are changes in management, architects, technical leads, developers, product managers, or new additions to a technology stack. But established execution patterns help keep the operating model, strategy, and vision on track. They are also a great help in quickly recruiting new team members.
1) The cloud-native model
The first execution model is cloud-native. Cloud-native platforms will underpin more than 95% of new digital initiatives by 2025, up from less than 40% in 2021. Why are businesses turning to the cloud? They are trying to leverage the benefits of private, hybrid or public cloud. Lower total cost of ownership, scalable unit economics, multi-regional reliability, digital transformation, faster application delivery and machine learning models are all business benefits of adopting cloud native.
Communicating the business value of adopting cloud native is an important part of this model. Cloud native is 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, resiliency, fault tolerance, scalability, and flexibility. A cloud-native model helps manage technology stack costs and resources 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 datasets can be tested for new patterns, the faster the business can make new decisions. A cloud-native model helps reduce barriers to innovation, supports frictionless change, and enables innovation with data to happen faster.
2) The real-time data model
The ability to evaluate real-time data is expected to be one of the biggest data analytics trends for 2022. According to Gartner, more than 50% of new business systems will use real-time data to improve insight. decisions by 2022. Faster, real-time decisions with reliable data lead to competitive advantage.
Real-time data flows through a data ecosystem. The more the right data can be “easily” delivered to the right people at the right time, the healthier the ecosystem is to drive business results. Data streams are generated from applications, streaming and messaging technologies, and databases. As the business seeks different ways to examine the data or additional data sources to gain business insights, the speed of the business is determined by how easily it can be correlated, integrated, and refine these data streams in new ways.
A real-time data model guides architects, data engineers, and developers in managing change. Reducing barriers to data access and complexity makes it easier to innovate with data. Complexity is the sworn enemy of data quality, trust, and business speed. Sticking to recognized and proven models helps minimize changes that will create roadblocks and complexity (data swamp) in future cycles.
It is increasingly common for industries to require the integration and refinement of different data sets downstream for real-time processing. I can’t remember the last time a business executive asked for more batch data. What I hear instead is, “We need to make data decisions faster and in real time.”
For digital applications, streaming and messaging technologies, and the databases that support them, data must be able to flow easily through the ecosystem. Establishing a model for this type of real-time data flow within an organization helps all members of the data ecosystem understand and support the direction of real-time data in which the organization must evolve to achieve business objectives.
3) The open source software model
Finally, there is open source software. The OSS is at the origin of many technological innovations for companies. On the one hand, OSS allows teams to experiment or create proofs of concept with fully-featured, essentially free software. This shifts the decision-making process about which technologies to use away from endless debate and toward success (or learning failure). It also reduces the risks of being locked into a particular vendor (read more about OSS and innovation here).
The use of open source has become an important part of application and data management strategies. In the CDO community, a recurring theme among data leaders is the importance of data literacy. Open source is more than innovation, scalable unit economics, and ease of use. Open source is also culture; it impacts a group’s way of thinking, values and beliefs. When cloud-native developers and real-time data engineers consider data innovation for digital transformation, they naturally turn to open source. It’s a model that helps fuel innovation in a data culture.
As a data scientist, I constantly see companies following the execution patterns of cloud-native adoption, focusing more on real-time data, and leveraging open source. So what’s the key to putting them all together?
It is about creating congruence with an operational model. It creates harmony and synergy with a vision that aligns cloud-native adoption, a real-time data management strategy, and leveraging open source. These three delivery models must work together and complement each other. Unfortunately, too often, cloud strategy, data strategy, and open source decisions are all driven by different business units with separate goals that aren’t aligned. All of these execution models need to be part of a unifying vision and data mining model.
In a future post, I’ll share a data journey with execution models that create business results with a real-time data mining model, and show you a real-time data platform to help data consumers to innovate.
Learn more about DataStax.
About George Trujillo:
George is a senior data strategist at DataStax. Previously, he built high performing teams for data value initiatives at organizations such as Charles Schwab, Overstock and VMware. George works with CDOs and data managers on the continuous evolution of real-time data strategies for their enterprise data ecosystem.