If you are responsible for an organisation’s data strategy, you face two huge challenges. First, you need to manage exponentially growing volumes of data. Second, you need to support complex and rapidly changing demands for data insights.
Until recently, almost all business data projects were managed using a ‘waterfall’ approach, a linear process where each project phase (e.g., scoping, development, testing, delivery) starts once the previous one completes. However, the larger the project, the more complex each phase becomes. Delivering value takes a long time. It can also be complex and time-consuming to adapt to any changes in requirements.
As a result, the field of data and analytics is moving towards a more collaborative data management practice. It is designed to improve the flow of data across the whole organisation, and it’s called ‘DataOps’.
How does DataOps compare to DevOps?
As two similar-sounding terms, DataOps and DevOps can be confused with one another. However, the two fields are very different.
- DevOps is a set of practices and tools designed to increase the speed and quality of software development, by enabling development and IT teams to better work together. But while DevOps does include some elements of better team collaboration, there’s a heavy focus on the use of technology, tools, and frameworks, including automation and testing software, to speed up software delivery.
- DataOps has far wider goals, bringing together everything you need to create an end-to-end data lifecycle strategy. DataOps uses tools and methodologies to improve delivery, including Agile and DevOps itself, but optimising project delivery is only part of the equation. DataOps is designed to improve the whole flow of data, so organisations can get greater value from data at every point of need. There’s also a focus on how to drive innovation within data teams. “
The point of DataOps is to change how people collaborate around data and how it is used in the organisation,” says Ted Friedman, Distinguished VP Analyst, Gartner.
The tools you'll need for DataOps
A DataOps strategy commonly brings together three existing project methodologies and their toolsets: Agile development, DevOps, and lean manufacturing.
Agile development usually divides a project’s tasks into a number of smaller sets that can be developed and delivered in shorter cycles, called ‘sprints’. Sprints usually last for a week, with the team agreeing at the beginning of the week which tasks are included, and success reviewed at the end of the week. A retrospective look at the week’s work enables the team to improve its approach for the following week, for greater success.
Working in sprints enables new analytics to be launched in rapid succession, and enables teams to regularly reassess priorities. This enables the organisation to adapt to any changes in requirements during the project.
DevOps was designed to bring together software development and operations roles in a single team. The closer collaboration aims to reduce silos and produce better-quality software products.
Many data and analytics projects require the development of software or reports, so projects can learn valuable lessons from the DevOps movement. Bringing people together throughout the project also enables issues to be spotted and addressed far earlier in the project lifecycle.
3. Lean manufacturing
‘Lean’ is a methodology that focuses on streamlining processes to speed up delivery and reduce waste. This is obviously important when you manufacture a physical product. By altering the definition of ‘waste’ to become ‘anything that doesn’t add value to the customer’, and by considering a data analytics pipeline similar to a production line, it becomes possible to apply lean principles to a data strategy.
In particular, DataOps uses statistical process control (SPC) to consistently monitor the data pipeline. If anything unexpected occurs, automated notifications enable the team to quickly check and resolve the issue, keeping the pipeline or project on track.
See the benefits of DataOps
Data volumes are expected to continue growing for years to come, so it’s essential to have a data strategy that is robust, yet flexible and scalable. With DataOps, you can bring together your whole data ecosystem, enabling you to streamline existing processes and identify where new technologies can deliver improvements to your business.
Ask us for more information about how DataOps could help your organisation better manage its long-term data strategy. Get in touch at email@example.com.