Quick and informed decision-making is a critical ability for businesses to stay ahead of competitors. Making data-driven decisions efficiently demands more and better organized information. Unfortunately, many data teams are overwhelmed with maintenance tasks and troubleshooting data quality issues. When data teams are unable to keep up with requests, business leaders, frustrated by delays, create their own localized data solutions. This kind of localized analytics and data management, a.k.a shadow IT or shadow data, creates maintenance overhead and discrepancies with established solutions, slowing your data teams down even more and reducing confidence in your data initiatives. Any solution to this self-perpetuating cycle of applying Band-Aid solutions will require rethinking your approach to data management. DataOps provides just such a solution, with a holistic approach to improving data management incrementally in meaningful, impactful ways.
“DataOps” describes a set of tools and processes that improve the speed, reliability and agility of developing data and analytics solutions while improving overall data quality. Inspired by the success of DevOps in improving the software development life cycle (SDLC), the goal of these practices is to:
- Keep your data current and accurate which builds trust with your users
- Reduce time spent troubleshooting data pipelines and existing analytics solutions
- Reduce the operational effort required to manage and support the underlying platform
- Allow your data and operations teams more time to focus on high-end activities such as developing and releasing new analytics innovations
Implemented properly, a DataOps initiative impacts analytics consumers and decision-makers by improving satisfaction with your data team’s responsiveness while increasing confidence in your company’s data solutions.
DataOps practices include:
- Codifying data ingestion, transformation, validation, and orchestration to provide faster and more stable data pipelines
- Achieving high-quality data with continuous, automated error detection
- Applying knowledge of data structure and statistical methodologies to proactively identify potential problems, data quality issues, or important business events
- Supporting continuous integration / continuous delivery (CI/CD), now the norm for developers, for repeatable efficient delivery of new data engineering and analytical reports
- Monitoring response times and compute resources to proactively adjust to demand
- Defining the underlying cloud infrastructure as code (this is more a related DevOps practice, but important for scaling and resilience)
More than just a set of technical recommendations, DataOps also promotes a philosophy of close collaboration between data teams, operations teams and analytics consumers, engaging them earlier in the process and following up more frequently. Regular reviews of business rules with data consumers to ensure validation rules and business logic are up to date. Data and operations teams collaborate on the development of automated data validation and pipeline releases which increases the detection of production data issues and feature delivery times. These solution enhancements aim to reduce friction between teams by reconciling the priorities of both parties. This shared approach encourages embracing change, which is as important to developing trust as accurate, timely data. For more information on the philosophy behind this movement, see The DataOps Manifesto – 18 DataOps Principles.
Regardless of your organization’s sophistication around managing data, DataOps provides a framework for strategic improvement. The breadth of options available, however, can be daunting and it is not easy to decide what to prioritize. A valuable resource for understanding the options available and determining what will bring the most value is provided in a whitepaper by Evan Pearce, Data Solutions Manager for Indellient. Evan has years of experience working with clients of varied sizes and from different industries and has seen the value of DataOps in practice. The whitepaper lays out the justification for DataOps practices in greater depth but more importantly helps establish the first steps towards defining your journey whatever your existing data practices are today.
Streamlining operations around maintaining data platforms to reduce repetitive, low-value activities; catching performance and data quality issues before they are noticed; greater collaboration between data consumers and the data team. These three overarching goals are the guiding principles behind any DataOps initiative and any corporate data strategy.
Wherever you are in your data journey, harnessing the power of DataOps begins with a well-defined approach. To learn more about implementing DataOps into your workflow, download “A Practical Guide to DataOps.”