Common Red Flags in the DataOps Practice - and what you can do to avoid them

By Sneha Ramesh


A recent blog by OpenView highlights the five most common DataOps mistakes of today. The article is highly relevant for everyone in the data space. We have summarised this blog for the benefit of the audience. The goal of DataOps is to deliver usable data solutions faster. It bypasses the traditional Software Development Lifecycle model by taking a more iterative and automated approach to development. Instead of the upfront design and requirements phases, small and valuable features are identified and integrated into the software. Tools such as Snowflake and DBT, at GitLab, automate development, testing and deployment to the production site (within hours).


The common mistakes to be avoided while embracing a DataOps style of work are listed and explained below:


1. Overlooking the importance of a cultural mindset

  • If your organization does not already have a DataOps mentality, it can be challenging to shift the culture effectively. The focus needs to be on making smaller, iterative changes rather than massive changes.

  • People on the team need guidance on how to get started.

2. Buying a data warehouse just because you want one

  • Many companies can manage without a data warehouse because many of the tools have inbuilt analytics and reporting capabilities.

  • An organization needs to have maturity level before it invests in a data warehouse.

3. Failing to align with business needs

  • Without being deeply engaged with the business, the DataOps team will not be able to understand and derive insights.

4. Choosing the wrong technology

  • Choosing a technology that does not solve business needs can be because the DataOps team has either misinterpreted or does not understand the business needs.

5. Looking inward instead of outward

  • Most of the successful data teams think of themselves as a consulting expert in data for the rest of the business. This perspective helps them focus more on looking outward instead of inward focusing on the business needs.

Future advancements in this field are expected to continue to broaden the scope of what organizations can do in the DataOps field, even without many specialists. Other expected improvements include automation tools that can automatically generate insights (look for historical trends, drill into deeper layers) that would otherwise not be discovered. Organizations should focus on building a strong DataOps foundation, while avoiding the most common mistakes listed above.


A sixth common mistake around DataOps, not addressed in the article by OpenView, is that people ignore or are unaware of DataSecOps and its underlying principles.


The confluence of multiple technologies at work today is seeing the emergence of a new market segment called DataSecOps which directly addresses this issue. DataSecOps is a discipline which empowers Software Engineers, Data Scientists, Governance Risk and Control, Cyber Security & Operations teams to work together in a single application for safer and easier access, analysis, delivery, and governance of data.


The very crux of this domain is that the entire business and the associated processes must be involved in combating security issues. The aggregation of Privacy Enhancing Techniques (PETs) lies at the very core of DataSecOps.


The primary focus with respect to data protection is to create a culture of prevention and to enhance security, agility and speed without sacrificing the benefits of APIs. This is called “Shift Left”, or to find and prevent defects early in the software delivery process. The idea is to improve quality by moving tasks to the left as early in the lifecycle as possible. Shift Left testing means testing earlier in the software development process.

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For more of our insights on the latest data innovations and developments, read our range of blogs here, or download our eBook, Introduction to DataSecOps, for a fully comprehensive overview of DataSecOps.


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