Six DataOps essentials to deliver business-ready data

Nearly every business is under competitive, disruptive, and regulatory pressures. As companies face digital transformation and modernization to meet their customers' expectations, leveraging data and AI at the speed of business can be the biggest differentiator.

However, according to MIT Sloan, 81 percent of organizations don’t understand their data because it’s locked in silos. Much of the data remains inaccessible, untrusted or unanalyzed, providing little-to-no value. Cloud and mobile adoption has accelerated the pace of data creation and increased the number of locations where data can reside. Yet many businesses don’t know what data they have, where it resides, what systems are using it and for what purpose – and if it meets regulatory and compliance requirements.

With all of these challenges, how can companies gain agility through data?

Companies need trusted, business-ready data at the speed and scale of the market to help achieve business objectives. Goals such as “I want to increase sales revenue by 5% next quarter;” “I want to decrease costs by 5 percent over the next 3 quarters;” “I want to increase cross-sell and upsell opportunities by 10 percent” are mentioned around boardrooms. Yet data bottlenecks continue to be a challenge and prevent these goals from being achieved.

The common data bottleneck
Many data-driven organizations are spending 80 percent of their time on data preparation and find it a major bottleneck. The reality is their data is not business-ready. When data is not business-ready, they spend more cycles, time, and resources on data preparation instead of spending time on AI modeling, analytics reporting and iterating for new insights.

What are the attributes of business-ready data? If you know, trust, and use your data, it means it is business-ready. In other words, your data is:
- Easy to understand, find and use
- Trusted, governed and lineage-tracked
- Able to provide a 360-degree view of data and self-service capabilities

With a business-ready foundation data preparation takes less time and it improves data agility and responsiveness to new market demands and business models.

How can your organization get to business-ready data to deliver analytics and AI at scale and speed? It starts with building a curated, trusted, automated and collaborative data pipeline between your data providers and data consumers. DataOps can help accelerate your journey to business-ready data.

Six essentials of DataOps to get business-ready data
To achieve business-ready data, you could leverage data operations, or a DataOps methodology. DataOps is the orchestration of people, process, and technology to deliver a curated, automated, and trusted data pipeline to data citizens. It’s similar to DevOps but focuses on enabling collaboration among data providers and data consumers.

It automates many of the operations on data, and works to remove bottlenecks in the data pipeline. The goal is a self-service data culture that drives agility, speed, and new initiatives at scale.

Here are six essential components of DataOps that can help drive a trusted business-ready data pipeline:
• Automated data curation services with auto-discovery and classification, sensitive data detection, quality analysis, and auto-assignment of business terms
• Automated core governance and master data management services with automated data lineage creation, policy management and enforcement
• Automated open metadata management that becomes the knowledge catalog for the enterprise for any type of assets
• Hybrid cloud data integration, movement and virtualization including multicloud optimization and replication
• Self-service capabilities for search, data preparation, workflow and collaboration
• Applying AI and machine learning to drive automation and innovation. This element makes all the five above possible.

Next steps on your DataOps journey
DataOps delivers a prescriptive methodology and framework to help you start your journey toward business-ready data. Deciding on where to start from can be a challenge for many organizations. Here are some strategies:
• Align data strategy with your business strategy. Select a project that drives and contributes to business outcomes, enabling the scope of the project defines the target data to start with.
• Catalog your data assets. You have to know what data you have, where you have it—and what rules and policies apply to it. Centralize data knowledge –irrespective of data locations or data types – with ease-of-use providing instant findability and access.
• Build an open governance framework. An open and extensible platform builds the foundation wherever your data resides, across hybrid and multicloud environments.
• Ensure data quality. Empower data consumers with complete set of structured and unstructured data that is trusted, integrated and governed.
• Create a 360-degree view. Gain master views of your customers, products and any entities to drive agility and faster business decisions.

DataOps helps you deliver curated, trusted, self-service business-ready data –accelerating digital transformation, AI and data operations at scale.

Aliye Ozcan is Portfolio Marketing Leader at IBM.