Data mesh takes you beyond yesterday’s monolithic approaches to data. You will need a robust architecture to support it. Your DataOps strategy can’t exist on its own. Your data should be the constant-there’s no reason to have to start from scratch every time a new technology comes along.ĭata mesh is the fundamental architecture Focus on your data and building applications around it with an infrastructure agnostic manner. Think about who can access the data, and the risks involved, with an eye toward how your data is stored, transmitted, and processed.įuture proofing is the next requirement, so you're not locked into a particular platform or technology. As you work to build data intensity, you’ll want to ensure that data ethics are sustained. 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.Įven as you move forward faster, it’s essential that governance and transparency don't get left behind. Make sure you are following best practices of the DevOps community. To do that, you need an automated pipeline in your data platform. Understanding the data is more important than understanding the technology behind it.Įven the best solution won’t do you any good if you can’t bring it into production. It starts with establishing a common way to talk about data, using a baseline set of knowledge, such as SQL. Maximizing data literacy is another key step toward data intensity. Don’t be afraid to purchase the technology and tools you need, rather than build it yourself. Choose the technology that lines up best to your own use case and your platform, not merely the flavor of the month. Not every company is a Facebook or a Google. 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. One reason that IT projects fail so often is that people choose the wrong technology. 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. The first step is to start with proven available technologies. It’s a journey that brings together the right technology, best practices, and infrastructure foundation. 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.ĭata intensity won’t happen overnight. 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. It's all about bringing people closer to data, application development, and focus on business outcomes. 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.ĭata 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. 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. The path toward data intensity can take us there. The trick is determining how we can best put data to use and apply it to real-world business imperatives. If you’re in business today, it’s obvious that data holds tremendous potential.
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