In today’s Digital Era, data driven decision making is playing an increasingly pivotal role in running modern businesses. Companies are making significant investments in modernizing themselves or relying on help from external consultants to deal with the large and complex data landscape. Despite relentless efforts from data teams both internal and external, Forrester’s research study found that most of the enterprise data either goes unused and/or the executives lack trust in their own data.
The ever-growing volume and complexity of actionable data being collected warrants the need for better and more streamlined processes. Going forward, organizations will have to alter the way they work to overcome bottlenecks and preemptively address challenges of scale, in order to achieve data driven and analytical solutions to meet their business needs. By introducing DataOps, data and analytics teams can achieve what software development and deployment teams have attained with DevOps.
What is DataOps?
DataOps is a collection of data management processes, practices and technologies which are all focused on improving collaboration between teams, integration and automation of data-flows and end-to-end observability of the entire data pipeline which in turn drives greater reliability, performance, cost optimization and an overall improved quality and turn-around times.
DataOps is the need of the hour, given the following challenges:
- Complex, multi-tool and heterogeneous environments which make it hard for data professionals to manage and use
- Obsolete manual processes which don’t achieve the scale, quality or minimal cycle time required. Also, existing practices and processes don’t always translate well to newer technologies
- Rising expectations of stakeholders on Operationalizing at scale, quicker turnaround, faster integration of new capabilities and flexibility
- Increasing roles and ineffective communication/collaboration between teams halts innovation and reduces the speed & quality of delivery
- Difficulty in keeping up with change, given the rapidly changing customer preferences and market requirements. Executives find it hard to determine the right approach to handle the change without relying on up-to-date and relevant insights
- Rapidly increasing data sources causing data silos with no connection with other pipelines, where data discovery itself becomes a challenge
- Development and deployment processes are complicated in the Data lifecycle due to the following factors:
- Two intersecting data pipelines (value and innovation pipeline)
- Duality of orchestration and testing
- Complexity of Sandbox and test data management
How to Introduce and Implement DataOps:
Enabling collaborations across roles and hierarchies:
To stay competitive and keep the innovation free flowing, it is important to harmonize back-and-forth communication between the centralized (local) and decentralized (distributed) teams involved in data analytics. Also, to keep up with the market pace and quality standards it is important that development and operations teams work in an integrated manner.
Adding strategic roles to handle the engagement between various teams, creating a medium to interact, both in the real and digital worlds and encouraging teams to use/update metadata management tools on a regular basis – this can be a starting point.
Apply Agile methodologies, DevOps techniques and Lean manufacturing tools to Data Analytics
Agile development ensures that teams publish in short increments while they continuously reassess the priorities based on changing customer requirements. DevOps optimizes code verification, builds and delivery by automating integration, test & deployment. And lean Manufacturing tools ensure that the KPIs and other vital metrics remain within acceptable ranges by orchestrating & monitoring data pipelines.
For starters, software development teams applying DevOps techniques can be observed closely to lay out a plan of approach for data analytics projects. DataOps principles can be applied to small internal projects and POCs to demonstrate value. Automation of orchestration & testing along with the upgrade of supporting tools can be a great first step in this direction
Demonstrate value and prove the credibility of DataOps techniques to data teams
Due to an aversion to change, traditional approaches of defining and executing data-oriented projects might be hard to replace. For effective introduction of DataOps and smoother transition from old techniques, it is crucial to demonstrate the value it delivers to all those involved.
Identify struggling data analytics projects and motivate key stakeholders to apply DataOps practices to improve quality and speed of delivery and use that as a fly-wheel to inculcate broader change in the organization.
To Summarize
Given the nature of the rapidly changing data landscape, it is important, now more than ever, for organizations to stay relevant and deliver quality data-driven outcomes in a streamlined, prompt and relevant manner. Now is the right time to introduce DataOps! But adoption and implementation of the same can be quite a challenge to get right the first time and it would be
worthwhile to seek the help of domain experts and consultants to hit the ground running on your data journey.
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