Why Data Intensity Matters in Today’s World

Using DataOps to achieve data intensity and bring data closer to people with knowledge of the business lets those people make decisions about how best to use it.

Andrew Stevenson

May 13, 2020

4 Min Read
Why Data Intensity Matters in Today’s World
(Image: Pixabay)

If you’re in business today, it’s obvious that data holds tremendous potential. The trick is determining how we can best put data to use and apply it to real-world business imperatives. The path toward data intensity can take us there. Many of us have already heard about “tech intensity,” where companies take the tools and technologies provided by vendors and put them to work to solve their own complex business problems. In some ways, every company is becoming a technology company, where they’re using the latest technologies like machine learning, AI, and other innovations to create their own intellectual property.  

Data intensity takes everything a step further, by building on the tech intensity that companies have acquired within their organizations and applying a real-time DataOps approach to the business. It's all about bringing people closer to data, application development, and focus on business outcomes. When you cut out the complicated IT and technical processes that stand between data and people that have the domain knowledge to really apply it to create applications, you build data intensity. Instead of worrying about how to deploy a technology or configure a new toolset, you can focus on making the data the engine that drives your outcomes.

Five ways to move toward data intensity

Data intensity won’t happen overnight. It’s a journey that brings together the right technology, best practices, and infrastructure foundation. The first step is to start with proven available technologies. Open Source offerings may tempt us with the latest technical bells and whistles, but they aren’t always the solution that aligns best with our business objectives. One reason that IT projects fail so often is that people choose the wrong technology. As you evaluate the tooling you will use with your data, consider whether you need some of the scale and complexity that comes with these technologies. Not every company is a Facebook or a Google.  Choose the technology that lines up best to your own use case and your platform, not merely the flavor of the month. Don’t be afraid to purchase the technology and tools you need, rather than build it yourself. 

Maximizing data literacy is another key step toward data intensity. It starts with establishing a common way to talk about data, using a baseline set of knowledge, such as SQL. Understanding the data is more important than understanding the technology behind it.   

Even the best solution won’t do you any good if you can’t bring it into production. To do that, you need an automated pipeline in your data platform. Make sure you are following best practices of the DevOps community. For example, a GitOps workflow can help your processes fit into the standard lifecycle of the release team, providing the agility and repeatability to move into production fast.

Even as you move forward faster, it’s essential that governance and transparency don't get left behind. As you work to build data intensity, you’ll want to ensure that data ethics are sustained. Think about who can access the data, and the risks involved, with an eye toward how your data is stored, transmitted, and processed.

Future proofing is the next requirement, so you're not locked into a particular platform or technology. Focus on your data and building applications around it with an infrastructure agnostic manner. Your data should be the constant—there’s no reason to have to start from scratch every time a new technology comes along. 

Data mesh is the fundamental architecture

Your DataOps strategy can’t exist on its own. You will need a robust architecture to support it. That’s where a data mesh comes in. Data mesh takes you beyond yesterday’s monolithic approaches to data. For example, data lakes are massive, but they lack the accessibility and discoverability required to connect the right data with the right people, at the right time.

A data mesh is a data system that aligns with your specific access patterns and lets lines-of-business users model and expose their data in the correct context. It’s a more collaborative approach that grants access and shares data insights faster.

Achieving data intensity positions organizations to bring data closer to those with knowledge of the business so that they can make decisions about how best to use it. When you break down barriers between people, tools, data, and applications, you set up a springboard for faster, better business outcomes—informed by data, but enabled by technology.

About the Author(s)

Andrew Stevenson

Andrew Stevenson is the Chief Technology Officer and co-founder ofLenses.io, where he leads the company'sworld-class engineering team and technical strategy.With more than 20 years of experience with real time data, Andrew started as a C++ developer before leading and architecting big data projects in the banking, retail and energy sectors including Clearstream, Eneco, Barclays, ING and IMC. He is an experienced Fast Data Solution architect and highly-respected Open Source contributor with extensive data warehousing knowledge. His areas of expertise include DataOps, Apache Kafka, GitOps and Kubernetes and the delivery of data driven applications and big data stacks.

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