Authored by: Jim Azar, Sr. Vice President, CTO at Orasi Software
We all know the value of Big Data by now. And attached to that big tide are concurrent waves of small data, real-time data, and warm data that have made enterprises think of data as the new competitive advantage. But harnessing the power of data is like putting up a big dam and a pipeline together.
A lot can go wrong on this spectrum; however, a lot can be made better as well. That is why DataOps has emerged as the perfect answer to exploit the power of information – from raw data to perfectly baked insights.
DataOps essentially galvanizes agile development methodologies, statistical tools, and DevOps to create a truly data-focused enterprise. It puts DevOps teams on the same page as the data engineer and data scientist. It is an approach that uses the approaches of automation and agile models across the entire data spectrum – from one part of the entry to the exit – from input to the final form of reports/models/dashboards and decisions – everything is made fluid and smooth.
DataOps brings together the data and operations side of data analytics. This leads to several gains.
- Overall data quality, efficiency, and uptime become elevated.
- The entire span of design, development, and maintenance of applications that are based on data and data analytics become streamlined.
- It leads to better decisions, faster and better products, and fully accomplished business goals.
- It solves the big problem of data fragmentation because now the data is not scattered, siloed, and unstructured, but in a smooth pipeline and runs like a data factory.
- Overall, data management for multiple data sources is automated.
- It can activate data for business value across several data infrastructure levels.
- It encourages and enables cross-functional teams across areas like operations, software engineering, architecture and planning, product management, data analysis, data development, and data engineering.
- It enhances data-centric collaboration between developers and operations professionals, and thus empowers business users with high-quality and swift data.
DevOps and DataOps
While the lineage is the same, and DataOps does leverage DevOps methodology’s core premise – they are different concepts. DevOps is a software development methodology that hinges on automation, continuous integration, and continuous delivery. DataOps adds data specialists like analysts, developers, and engineers to promote collaboration and ongoing data flows.
DevOps is for software developers and coding/application-related areas. DataOps is for data scientists and analysts who are building and deploying models and visualizations. The level of complexity between DevOps and DataOps varies. Often, it is higher for DevOps.
If DevOps shrinks the time to deployment, time to market, defects, and testing for applications – DataOps does just that for the data cycle time at an end-to-end level–from ideas to final charts, graphs, and models.
DataOps–Practices worth emulating
Here are some salient best practices that can elevate the impact of DataOps in an organization:
- Agile methodology
- Automation of workflows and provisioning
- Incremental builds
- Stress on production-quality data
- Self-service data management
- Fast data delivery and apt control on security and storage loopholes
- Operational support
- Creation of glossaries and documents
- Adaptive and agile management of red flags
DataOps–Value that flows to the right spot
With a solid DataOps strategy in place, businesses can finally squeeze concrete value from data. This automated and process-oriented methodology empowers analytic and data teams to augment the quality of analytics. They can also cut down the cycle time of data analytics.
Using agile and collaborative shifts as core levers inside the enterprise, DataOps can allow enterprises to achieve better and faster customer service avenues. They also make sure that this value does not cost too much time or money spent.
A simple, straight, intelligent, and well-oiled pipe manifests when an enterprise puts DataOps in place.