DataOps practices bring people closer to data, delivering a serious payoff in data management when resources are stretched thin. This article by Andrew Stevenson, chief technology officer, and co-founder, Lenses.io, outlines six basic steps to help you move forward on the journey.
Over just a few short months, the healthcare crisis has transformed workforces worldwide. For data professionals, getting the most out of limited resources is becoming more important than ever. Technology can help, but it needs to be supported by the right strategy and culture. To realize the most value from your data, you need to be sure it can flow smoothly to the people who need it mostâ€”and have the expertise to build insights from it.Â
Open-source technologies can deliver the latest and greatest capabilities for your data platform, but they can also be complex and require extensive skills to manage and support. It’s difficult to realize their potential if you don’t have enough people with the expertise to apply them.Â
To really get the most out of your data, you need not only advanced technologies but an approach to understanding and working with data that is centered around your business imperatives. Designed to deliver value faster leveraging technology, DataOps is collaborative and highly automated.Â
6 Essentials for a DataOps Strategy
DataOps is not a solution you can buy, but a strategy and set of tools built around the collaboration between your technical people and your business stakeholders. If you haven’t yet put a DataOps initiative in place, six basic steps can help put you on the path.
1)Â Build a data meshOpens a new window foundation
Traditional, monolithic data lakesOpens a new window are centralized, monolithic, and slow to meet today’s needs at scale. Establishing a modern, distributed data mesh architecture helps you put a self-service approach to data in place. Designed with discoverability in mind, it lets you give individual domain teams access to the data they need in a way that is easy to consumeâ€”and share insights about the data faster.
2) Choose the right tools for the job
Plenty of tools and technologies are available to discover and analyze data, but you’ll want to choose proven technology that’s best for your specific platform and use case. Tools go in and out of fashion, but whether you select a commercially developed solution or an open-source technology, the important thing is to stay focused on the best way to achieve your business outcome.
3) Elevate your visibility
Even the best data solution will be of little value if your users can’t get to it and start using it. If you want to put data in reach for your business users, you’ll need to give them visibility into the system in an intuitive way. Your users shouldn’t have to spend time grappling with complicated interfaces and challenging management tools. They should be free to concentrate on the business objective they are trying to achieve.Â
4) Make data understood and accessible
One of the strengths of DataOps Opens a new window is that it puts data technologies in reach for more people across your organization, instead of a limited set of highly technical users. Work with tools that build around a common set of skills that everyone in your organization can domain, like SQLOpens a new window .Â Â
5) Automate to accelerate
If technical employees are unavailable, automation and managed services are powerful ways to do more with less. For example, GitOps workflows can provide the agility and repeatability you need to move your data project from concept to production fast.
6) Keep data ethics in mind
Opening up more data to more users creates powerful efficiencies but also the risk of misuse. That’s why it’s important to maintain governance and compliance as a pillar of your strategy. To uphold data ethics, be sure you know who is accessing data and why, and set up the right controls to ensure your policies are effective.
Data Technology Is Simply a Means to an End
In a data-driven company, data is the true protagonist, while technology is its enabler. Technology provides powerful capabilities, but at the end of the day, it’s simply a way to visualize data, gain insights into it, and build value from it.Â
For example, an energy analytics provider supports its traders with Apache Kafka streaming data. These professionals work with data feeds in real-time coming from freight ships and other supply chain elements, then resell analytics to clients who use the insights to make investment decisions. The better their visibility into these data flows, the faster its analysts can respond to change and take direct action.Â Â
In short, to unlock the full value of dataOpens a new window , you need to bring it closer to the business needs it is intended to support. A data mesh architecture, built on the right strategy and supported by the right tools, can put you on a fast track toward becoming a more data-driven organization.Â