DataOps: Data at Speed
Learn about DataOps and how it can help your organisation move faster and achieve better business outcomes.
“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”
—Eric Schmidt, CE0 at Google (2010)
We all knew data was the new oil; some even compared it to water, which is essential for survival. As organisations try to unearth value from data by accessing and merging new sources, as well as shed light on the “dark data” they never knew existed in their own backyard, speed is the key to innovation.
Over the last decade, agile software development has led to the rise of the DevOps (development + operations) foundation in software engineering. DataOps (data + operations) is the next phase of evolution that nicely combines the key elements of DevOps in a data engineering lifecycle. DataOps helps accelerate implementation of the data pipeline, reduce errors, and improve collaboration, resulting in greater efficiency and reliability in data-centric activities.
So, what exactly is DataOps?
DataOps is a set of practices, processes, and technologies used to streamline and automate the data pipeline, from ingestion to consumption. It involves collaboration between all technical, operational, and business stakeholders to ensure that data is available, accurate, and reliable, and can be used to make informed business decisions. DataOps aims to bridge the gap between development and operations teams by using DevOps principles to manage data and analytics pipelines. This approach involves continuous integration, delivery, and deployment of data, along with continuous testing and monitoring, to ensure that data is accessible and of high quality.
What does it mean to implement DataOps?
The DataOps Manifesto is an industry-accepted open forum that lists the principles required for a successful implementation, focusing on the tri-factor combination of people-process-technology.
Some key takeaways from the principles are as follows:
Customer centricity is always key for any product development, and ensuring it early and on a regular basis helps us to not only course correct but also show value at a very early stage, building up the team’s confidence.
Automate at every opportunity — this not only minimises the chances of error due to manual intervention but also speeds up the development and testing cycle times.
Continuous integration and delivery — deploy all new and existing changes from the bottom up to minimise the impact of ad hoc code/data changes directly in production and ensure quality. Proper version control is essential to keeping an audit trail of events, allowing parallel development work and rollback if necessary.
Security and compliance—DataOps ensures that data security and compliance requirements are integrated into the data pipeline. This includes monitoring data access, enforcing data privacy rules, and ensuring compliance with regulations such as GDPR and HIPAA.
Overall, DataOps principles aim to create a culture of collaboration, automation, and continuous improvement that results in better data quality, faster time to insights, and improved business outcomes.
How does it benefit me?
Improved data quality: By automating and streamlining the data pipeline, DataOps ensures that data is accurate, consistent, and reliable. This reduces the risk of errors and inconsistencies, leading to better quality insights.
Faster time to insights: DataOps enables faster delivery of data and analytics by automating processes and reducing bottlenecks. This helps businesses make timely decisions, respond quickly to changing market conditions, and gain a competitive advantage.
Increased collaboration: DataOps encourages cross-functional collaboration, enabling data engineers, data scientists, and other stakeholders to work together seamlessly. This improves communication and reduces silos, leading to better outcomes.
Greater agility: DataOps provides flexibility and agility to the data pipeline, allowing businesses to quickly adapt to changing requirements or data sources. This helps businesses stay ahead of the competition by making data-driven decisions in real time.
Cost savings: By automating processes and reducing manual labor, DataOps reduces costs associated with data processing and analysis. This enables businesses to allocate resources to other areas that require attention.
Enhanced security and compliance: DataOps ensures that data security and compliance requirements are integrated into the data pipeline. This reduces the risk of data breaches and regulatory noncompliance, leading to greater trust among customers and stakeholders.
Overall, DataOps offers several benefits that enable businesses to make better decisions, gain a competitive advantage, and achieve better business outcomes.
Determining DataOps maturity
Much in line with the Capability Maturity Model, the maturity of DataOps can be categorized into four levels:
- Ad hoc: At this level, DataOps practices are ad hoc and inconsistent, with no standardized processes or tools in place. Data is often siloed, and there is little collaboration between teams. There is a lack of automation and testing, leading to poor data quality and slow delivery times.
- Emerging: At this level, organizations start to recognize the benefits of DataOps and begin to implement standardized processes and tools. Collaboration between teams improves, and data is more easily accessible. There is an increase in automation and testing, leading to better data quality and faster delivery times.
- Established: At this level, DataOps practices are well established, with standardized processes and tools in place. Collaboration between teams is strong, and there is a focus on continuous improvement. Automation and testing are prevalent, leading to high-quality data and fast delivery times.
- Optimized: At this level, DataOps practices are developed, with a focus on continuous innovation and efficiency. Processes are streamlined, and automation is pervasive, enabling teams to quickly respond to changing requirements. Collaboration between teams is seamless, and data quality is high, leading to fast delivery times and accurate insights.
Achieving the optimized level of DataOps maturity requires a significant investment in resources and a commitment to continuous improvement. However, organizations that reach this level can reap the benefits of faster time to insights, better data quality, and improved business outcomes.
What does DataOps implementation look like in practice?
The diagram below depicts the logical framework of a DataOps implementation. Choosing the right technology is essential, but what is more essential is to foster the data culture within the organisation — this will drive the implementation to success.
The image above shows how DataOps can supplement traditional data warehouse development.
Sounds interesting, how do I start?
Implementing DataOps is a gradual process, but here’s an implementation checklist you can use to ease into it:
1. Add logic and data test at every step to automate and measure.
2. Script all steps and enable version control.
3. Branch to improve productivity of the team via parallel working.
4. Isolate environments to minimise interference.
5. Reuse artifacts before writing a new script.
6. Make use of metadata to drive the scripts, thereby limiting chances of script updating.
7. Ensure developers can work without fear — constructive challenges should be encouraged.
Do I need a roadmap?
Yes, it always helps when you know where you stand as compared to peers and where you intend to go — after all, the journey is as important as the destination. Technologies open up opportunities, but it’s the right mix of people and process that make DataOps happen, and when it does, the possibilities are endless. Having the right strategy and approach is essential to ride the innovation wave — harnessing the hidden value from your data at speed is just a start!
“Too often we forget that genius, too, depends upon the data within its reach, that even Archimedes could not have devised Edison’s inventions.’’
—Ernest Dimnet, priest, writer, and lecturer
At Slalom, we can help you assess your current landscape and, given our rich experience in a wide variety of industries, help benchmark against your peers. We share insights on best practices and potential pitfalls, and offer a list of assets to supercharge your journey towards DataOps implementation.
Slalom is a global consulting firm that helps people and organizations dream bigger, move faster, and build better tomorrows for all. Learn more and reach out today.
