How DataOps Can Transform Your Data Team's Productivity

Discover how DataOps can bridge the gap in your data team, boosting efficiency and collaboration!

2 min read
engineering-management

Your data consumers, internal and external stakeholders, never get the data they need, when they need it. You thought migrating from a legacy data warehouse to Snowflake was enough! You are wrong.

While it is true that Snowflake propelled your team forward in delivering cheap and performant analytics, it is not enough. What is holding you back are the methodologies that need to be migrated with your evolved data stack. Data silo between database administrators/engineers and data consumers (data analysts and data scientists) in an anchor dragging your team.

High-functioning data teams are leapfrogging their productivity and impact with DataOps.

Clear examples of not realizing the full potential of your team and tools:

  • Your Data scientists waste a lot of time waiting for the data.

  • Your Data engineers spend long hours debugging and deploying pipelines.

  • Data errors are a common cause of your costly business mistakes.

The reason why your data team (DE, DS, DA) feels under-resourced and overwhelmed with requests is primarily lack of automation and collaboration.

These are the few challenges that burn your data team the most

  • The data team is not able to cope with the business needs of insights and data assets.

  • Data lives in silos and it takes a long complex process to get it in the hands of the data scientists and analysts.

  • Errors in data cause costly downtime in data pipelines and slow down the development of new assets.

  • Manual processes in data your teams strain their productivity and lead to high turnover.

Adopt, DataOps: The set of guiding principles to deliver faster and higher quality data and insights to the right stakeholders.

DataOps is DevOps applied to the data lifecycle "Raw Data 👉 Biz Insight".

This framework encourages the following:

  • Analytics is code and configuration. They should be versioned and reviewed like software.

  • Quality is paramount, use tests on data pipelines and monitor continuously using proper tooling.

  • Reduced cost of experimentation by using Infrastructure as code. Let the data team create disposable environments.

  • Orchestrating end-to-end is key.

  • Breaking down barriers between D. Eng, D. Scientist, D. Analyst, and others.

Implementing these practices will minimize the time and effort to turn a customer's need into an analytic idea, create it in development, release it as a repeatable production process, and finally refactor and reuse that product.

If you would like to know more on tactile steps to achieve data ops, please DM me or like the post.