What Is System Thinking for Data (And Why Is It So Important)?
In my role, I travel all over the globe meeting with people who are trying to turn their data to strategic advantage. They know that we’re facing a generational disruption in the market, and they want to thrive in the age of Data 3.0, where data truly powers digital transformation. But harnessing data effectively is challenging. Fortunately, there’s an answer. We call this System Thinking for data – and it represents an end-to-end, enterprise-wide vision for how data, applications, processes, and people can all work in concert to drive innovation and change. I’ve spoken about this concept at Informatica World. But here’s a deeper dive into my thoughts around System Thinking and why it’s so essential for data leaders today.
A Strategic Approach to Today’s Data Challenges
Data-driven organizations are dealing with data at a scale and complexity not seen before. All of this data is distributed across multi-cloud and hybrid environments while being generated at very high speeds.
If data is the fuel that drives business outcomes, then companies need reliable, always available data pipelines that can access, integrate, cleanse, master, protect, and deliver all types of data for any use case. There are also many more consumers of data and new types of users across the enterprise. However, you can’t just keep throwing people at the problem as there is simply too much data and too many steps involved – so automation and collaboration tools are imperative. Add to all this the myriad of new and evolving technologies to process data at scale and the ever-increasing number of regulations organizations must deal with to ensure governance, compliance, and data privacy – nothing damages the reputation of an organization more than the cavalier treatment of their customer’s data. So, the challenges organizations face have gone up exponentially and several degrees of complexity.
System Thinking addresses all these considerations and provides a framework for you to achieve your data-driven business objectives, now and in the future. By following its principles, you’ll be in a position to adapt to whatever new technologies, ecosystems, regulations, or other requirements come your way. Here are the building blocks to put it into practice.
5 Pillars of System Thinking
There are 5 pillars that support System Thinking and enable data-driven companies to improve business outcomes. They are:
- Platform – Your platform is the foundation to support a System Thinking approach. It has to be at enterprise scale in terms of performance, availability, reliability, and utilization across all data management patterns.
- DataOps – In order to execute System Thinking, organizations must operationalize their data platform by extending the concepts of DevOps to the world of data. DevOps is built on three major principles: Continuous Integration, Continuous Delivery, and Continuous Deployment.
- AI – With exponential data growth, AI is a necessity for increasing efficiency. Offload time-consuming and tedious tasks to AI, so you can free up time for human collaboration and higher-value work.
- Metadata management – Metadata management across the enterprise enables AI to be effective when applied to data management. It is the foundation of the data platform so that all components work together seamlessly, data professionals work collaboratively and efficiently, and AI augments human knowledge in context and with relevancy.
- Data governance and privacy – Data governance and privacy must be built into the data platform as design principles. They cannot be afterthoughts. Governance and privacy enable data democratization, because without trust in the data or data security, data is not useful or accessible.
And, of course, people play a central role. There are many stakeholders, such as LOB business analysts, data scientists, data engineers, data stewards, data governance councils, InfoSec analysts, administrators, and more. System Thinking is crucial to enabling all these people to work together effectively.
We’ll explore the key principles and best practices for each of these pillars in more detail in the next 5 blog posts in this series on System Thinking.