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Why Data Science Needs DataOps

Isn’t DataOps just DevOps?

DataOps is a result of the advances in DevOps, but applying DevOps principles to data won’t get you DataOps. The DevOps cycle is an infinite loop between the planning and creating stages of software development and considers the unique abilities of software developers and engineers.

  • DevOps: The iteration cycle for products must tie to the data insights to produce real business impacts.
  • Agile: Companies that respond in real-time to customer needs will be the ones to survive the digital transformation.
  • Lean manufacturing: The data pipeline resembles a physical factory. Raw data must flow into one end of the pipeline and produce systematized insights at the endpoint. This flow drives business operations.

What Does DataOps Look Like?

The DataOps pipeline uses two intersecting pieces to produce continuous insight. The first pipeline handles the cleaning and management of raw data, producing valuable insight into the question’s businesses need answered before proceeding with any new initiative. This is the value pipeline.

The Benefits of DataOps

One of the most prominent adjustments data scientists make when moving into the business world is the structure of producing data insights. In school or research, using cutting edge, complex models was the way to go, but business is only concerned with the impact.

Obstacles to DataOps

There are a few roadblocks to implementing DataOps where your business stands now.

  • Lack of Visibility: More data leads to clearer insights, but if you have no idea where your data is, how it’s stored, and how it’s been used in the past, you’re in a bind. Find out about your data and put systems in place for its governance.
  • Unrealistic expectations for pipelines: A pipeline is an automation tool, but it can still get complicated. Data scientists must have an operationalization understanding to set up pipelines that work. Project creep can derail a pipeline with unnecessary steps and activities that don’t align with a business’s goals.
  • Inadequate monitoring: Addressing the root cause of issues and standardizing success measurements can make or break a pipeline. DataOps relies on effective monitoring with clear and attainable goals.

DataOps: The Path to Digital Transformation

In the early days of development, businesses experienced some growing pains between the development/test iterations and production rollout. Now, companies are experiencing the same growing pains with data science.



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