DataOps is trending, but what is it?

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Understanding Data Ops and Opportunities in Tech

Welcome to a discussion on the intrigue of data operations, otherwise known as Data Ops. We are taking a tour through this excitingly underrepresented area in tech, laying magnifying glasses on everything from what it entails to its opportunities.

About…

First, let’s take a quick glimpse into my life. I began my journey into the tech world at a young age, dabbling in coding and eventually earning degrees in computer science and digital intelligence analytics. Today, I am lucky enough to work as a technical manager within the Business Intelligence and Analytics spectrum at Watch Finder and Co, part of the luxury Richmond Group. As a woman, I recognise the underrepresentation in the tech industry and am passionate about advocating for more inclusion.

Why is Data Ops Trending?

Data Ops is not a new concept. It is, however, one of the fastest-growing areas of tech. It encompasses an array of skills, and it offers a world of opportunities for data engineers, data analysts, and data scientists. It's important for us to encourage more women into this sector.

Gender Gap and Salary

The gender gap in tech positions is, unfortunately, still significant. And while there has been some progress in female representation in roles like data scientists, there is always room for improvement. This is coupled with the promising salaries in this area, which further bolster the attractiveness of this field.

Unpacking Data Ops

Data Ops, an abbreviation for data operations, is not to be confused with DevOps. DevOps focusses primarily on the software engineering spectrum whereas Data Ops incorporates principles of DevOps alongside agile, lean, and quality aspects. It aims to bridge the gap between data collection teams and the subsequent analysis and application of these findings.

The Conception and Evolution of Data Ops

Despite being a trending topic, Data Ops has been around for some time. A blog post in 2014 on IBM’s hub spoke about why it was essential for big data success. Fast forward to today, and data continues to be the corporate world's "black gold", a vital catalyst for success and growth.

Understanding the Best Practices of Data Ops

Data Ops encourages collaborative environments and gives emphasis on guidelines, metrics, and the development of defined roles. It promotes automation and a quick delivery level, all while ensuring efficiency and quality.

The Various Roles within Data Ops

Data Ops comprises of four key roles each with its unique set of responsibilities. These include:

  • DevOps engineer: Handles infrastructure, familiarity with cloud environments, facilitates automation.
  • Data engineer: Designs data structures, manipulates SQL, and understands cloud infrastructure.
  • Data scientist: Builds machine learning models, masters advanced mathematics, and codes in languages like Python.
  • Data analyst: Handles data visualization, develops dashboards and reports, maintains advanced SQL skills, and understands statistics and machine learning basics.

It's important to note that one can easily transition from one role to another, facilitating growth and development within the field.

The Data Ops Infrastructure

A Data Ops ecosystem might comprise data sources like databases and APIs, ingestion tools like AWS or Google, analysis tools, data presentation tools, repositories like GitHub for version management, and also data governance. The kind of infrastructure you choose to set up, and the tools therein, are determined by your use case.

The Future of Data Ops

Data Ops continues to grow in secularity in 2021, and it is projected to continue its stride well beyond. Companies are investing heavily in their data infrastructure, recognizing the value data holds for their success and growth.

For those who want to venture into this field, brushing up on SQL, learning a coding language like Python, using open source tools such as DBT analytics, understanding databases and modelling, and brushing up on statistics are just a few steps you could take.

So, there you have it - a crash course on Data Ops, everything it entails and the opportunities it offers. Thank you for joining this session, feel free to reach out if you have more questions about Data Ops and how to venture into this lucrative yet underrepresented field.


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