High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However,  stakeholders across the board are responsible for data delivery, quickly evolving requirements, and processes. Their preference towards technology is deflating traditional methods of responding to inconsistent data and consequently disappointing users. Some common roadblocks include:

• Teams spend more time identifying data pipeline and code inconsistency issues due to older code or incorrect connection and metadata information, infrastructure or operations-related challenges, or resolving technical dependencies across stakeholders compared to the time spent focusing on data delivery
• Manual processes lead to long response times, frequent errors, inconsistent data, and poor repeatability needed to support multiple teams continuously
• Siloed processes stemming from on-demand economies are leading to unusable data or unpredictable results

This is where the DataOps practice and methodology come into play. While many have defined what DataOps means, only a handful have tried to provide a deeper inside look at the holistic toolchain requirements. The tooling to directly and indirectly support DataOps needs can be broken down to five steps, leveraging existing analytics tools along with toolchain components meant to address source control management, process management, and efficient communication among groups to deliver a reliable data pipeline.

1. Use source control management: A data pipeline is nothing but source code responsible for converting raw content into useful information. We can automate the data pipeline end-to-end, producing a source code which can be consumed in reproducible fashion. A revision control tool (like GitHub) helps to store and manage all of the changes to code and configuration to minimize inconsistent deployment.
2. Automate DataOps process and workflow: For DataOps methodology to be successful, automation is the key and requires a data pipeline designed with run-time flexibility. Key requirements to achieve this are automated data curation services, metadata management, data governance, master data management, and self-service interaction.
3. Add data and logic tests: To be certain that the data pipeline is functioning properly, testing of inputs, outputs, and business logic must be applied. At each stage, the data pipeline is tested for accuracy or potential deviation along with errors or warnings before they are released to have consistent data quality.
4. Work without fear with consistent deployment: Data analytics professionals dread the prospect of deploying changes that break the current data pipeline. This can be addressed with two key workflows, which later integrate in production. First, the value pipeline creates continuous value for organizations. Second, the innovation pipeline takes the form of new analytics undergoing development which are later added to the production pipeline.
5. Implement communication and process management: Efficient and automated notifications are critical within a DataOps practice. When changes are made to any source code; or when a data pipeline is triggered, failed, completed or deployed, the right stakeholders can be notified immediately. Tools to enable cross-stakeholder communications are also part of the toolchain (think Slack or Trello).

The key takeaway from this article is this: a holistic approach to the DataOps toolchain is critical for success. Organizations that focus on one element at the expense of others are unlikely to realize the benefits from implementing DataOps practices.

Learn about the IBM DataOps Program

The shift to adopt DataOps is real. According to a recent survey, 73 percent of companies plan to Invest in DataOps. IBM is here to help you on your path to a DataOps practice with a prescriptive methodology, leading technology, and the IBM DataOps Center of Excellence, where experts work with you to customize an approach based on your business goals and identify the right pilot projects to drive value for your executive team.

Accelerate your DataOps learning and dive deeper into the methodology and toolchain by reading the whitepaper Implementing DataOps to deliver a business-ready data pipeline.