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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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What Is DataOps? When Agile Meets Data Analytics

2 July 2021

The amount of data enterprise businesses are producing is growing by 40 to 60% a year, and many companies are facing challenges managing, analyzing, and interpreting all that data so they can enable solutions, support their data-focused teams, and glean valuable business insights.

With requirements changing every day and the need for data access continuing to grow, many organizations have to find ways to improve their data management processes.

That’s where DataOps (data operations) methodologies come in.

 | Bernard Marr

What Is DataOps?

DataOps is an agile operations methodology that improves a company’s use of data through better tools, automation, and collaboration.

The primary purpose of DataOps is to align data management tools and processes with data goals, and improve communication and integration between data managers and the end-users who consume data.

In a nutshell, DataOps borrows the principles of DevOps – an approach to more agile and collaborative software development to speed up build lifecycles – and applies those principles and processes to data analytics.

DataOps encourages improvement and innovation by introducing the concepts of agile development into the world of data analytics, so data teams and the users who work with data can collaborate effectively to create a smooth, hassle-free data pipeline.

Deploying DataOps improves the speed and accuracy of data analytics across a company – including data quality, access to data, automation, integration, and development and deployment of data products and applications.

DataOps practitioners, who can come from a number of different departments, engage with teams of data scientists, analysts, engineers, and developers to create cross-functional collaboration and increase the pace of their data-driven deployments and initiatives.

The Biggest Benefits of DataOps

  • DataOps methodologies lead to better, cleaner data, which leads to improved analysis and business insights.
  • Improving collaboration between different parts of the technical team – including engineers and data scientists – helps companies access and leverage data better, in far less time.
  • Organizations that take an agile approach to data science are four times more likely to hit their financial goals.
  • DataOps makes data more manageable and accessible for its biggest users. Many times, these users are data scientists or analysts who are not as technically savvy as engineers, and they need tools and processes for getting the data they need as efficiently as possible so they can focus on their primary jobs.
  • Creation and management of central repositories for application data and data models allow for more detailed layers of analytics for organizations.
  • Companies that deploy DataOps can reduce the cost of data management.

DataOps Helps Leaders Deliver Data Insights Faster and Better

Effective deployment of DataOps gives teams a more agile, collaborative, efficient approach to managing and automating the flow of data, so stakeholders can get the data they need, when they need it, in a way that works for them.

As companies ease into democratizing their data and giving users more self-service ways to get data without needing to involve IT or engineering teams, I predict that more and more companies will deploy DataOps methodologies to keep things running smoothly and optimize their data analytics processes as much as possible.

Where to go from here

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Data Strategy Book | Bernard Marr

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