The Age of DataOps

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Over the past couple of years, anyone with an interest in IT is likely to have heard of “DataOps”. Is this another office buzzword, or does it have meaning? For many companies and organizations, data processing wasn’t viable until the late 1990s. The growth of the internet made hard drives cheaper. Computers and networking equipment came down in price. Processors became more powerful. Finally, data processing was accessible to all.

Addressing Problems

Companies can analyze data today like never before, but it’s also possible to process data poorly and even be damaged by it. DataOps aims to extract as much useful information as possible from data in a secure, efficient way. But how?

  • DataOps encourages collaboration between different data teams in a company or organization (e.g. analysts, engineers, integrators, scientists). Less obstruction between said teams increases speed so that data-driven projects are more likely to stay on schedule.
  • DataOps reduces data friction, allowing data teams and departments to access the data they need while still complying with regulations (e.g. GDPR). Proper data management assesses risk and protects more sensitive information while allowing other data to flow freely.
  • Data security is better using a DataOps system since distinct parties such as analysts and integrators are in constant touch with each other. This makes the loss of data at vulnerable endpoints less likely.

DataOps vs DevOps

If you’ve heard of DataOps, you might also know about DevOps. What’s the difference between the two? They have certain similarities beyond the semantic, and they complement each other. Both rely on close collaboration between different teams to enhance speed and efficiency.

DevOps harnesses the work of software engineers, tech operations and quality-assurance staff in one collaborative, automated effort to deliver high-quality software at speed.

DataOps is similar, in that it encourages collaboration between data engineers, integrators and analysts. With greater depth and precision, its goal is to inform company decisions and strategy and increase profit.

Switching to DataOps

DataOps outlines a new way of working without imposing any strict standard to adhere to. A DataOps platform will include the following essential elements, among others:

  • Data democratization to allow easy access to data without bottlenecks.
  • Automation is used wherever the manual alternative consumes more time (e.g. quality assurance testing).
  • Purging of old-fashioned silo ways of working in favor of new collaborative workflows. New tools must allow work and ideas to be easily sharable with colleagues. Cloud storage is a popular way of implementing this.
  • Stacking of existing platforms and tools to save time and avoid as many new learning curves as possible.

Becoming a data-driven company results in a better data workflow, quicker processing, improved analytics and more intelligent, more informed decision making.

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