Building a Future-Ready, Data-Driven Business with DataOps

Today, the world runs on data. From our GPS to our cell phones, to apps, to ATMs, to the sensors on planes, trains and every form of transportation and utility, data is everywhere. IDC predicts that by 2025, there will be 55.7 billion connected devices worldwide, with 75% connected to an Internet of Things (IoT) platform — and those IoT devices will generate 73.1 zettabytes of date, up from 18.3 zettabytes in 2019.

With so much data for mining, companies have quickly recognized that there’s big business in collecting it, gathering insights from it and monetizing it. Data conversations are shaping the strategies and the future of every organization.

“The Autonomous Digital Enterprise: A Strategic Approach to Measuring and Improving Digital Competitiveness,” a BMC-sponsored research paper by 451 Research, found that the top priority for enterprise organizations now and in two years is to become a data-driven business. This is one of the tenets of the Autonomous Digital Enterprise, our future-state business framework that’s rooted in implementing automation across all business functions. DataOps is helping drive that evolution.

I define DataOps as the application of agile engineering and DevOps best practices to the field of data management to rapidly turn new insights into fully operationalized production deliverables that unlock the business value of data.

Business Challenges 

By implementing DataOps, organizations can overcome many of the challenges that have caused previous attempts to harness and monetize data through data and analytics initiatives to fail, such as:

• Exponential data growth, in number and type of data.
• Disconnected traditional and emerging technology-driven processes.
• No collaboration to support cultural shifts and operational readiness.
• The capital investment required (e.g., mid-size companies invest $50-70M over three years).
• A skills and talent shortage for internal domain knowledge and deep technical knowledge.
• Inability to scale for the speed, flexibility, timeliness and customization that stakeholders expect.
• Unclear success measurements across the organization.

Successful DataOps

DataOps can solve for these issues and help organizations capitalize on their data and evolve their business through:

• Extensive collaboration across the data management ecosystem and the personas spanning business and IT.
• Pervasive automation that encompasses orchestration, provisioning, configuration and self-service.
• Recognition of the process standardizations and refinements that would be more effective with artificial intelligence (AI).
• Right-fit technology: converting traditional analytics methods to an AI-based method, but only if it would provide an order of magnitude percentage improvement over the current approach.
• Pragmatically approaching data governance, security and metadata management, so it’s not too burdensome or too lax.

Next Steps

As with any new technology capability, it’s advisable to establish a multihorizon initiative with a set of deliberate steps:

1. Start small with the introduction of DataOps in one or two analytics use cases.
2. Learn from these, refine, and then scale DataOps across additional business functions.
3. Keep applying specific best practices as business-as-usual once you get to a steady-state.

Conclusion

DataOps, including automation technologies and extensive cross-business collaboration, can help enforce the delivery discipline required for successful data and analytics transformations, as organizations strive to become a data-driven business and an Autonomous Digital Enterprise.

Data isn’t going anywhere. Businesses that want to be forward-looking and future-ready must be able to harness it.