Organize – creating a trusted analytics foundation

Given that data sits at the heart of AI, organizations will need to focus on the quality and governance of their data, ensuring it's accurate, consistent, and trusted. However, many organizations struggle to streamline their operating model when it comes to developing data pipelines and flows.

Some of the most common data challenges include the following:

• Lack of data quality, governance, and lineage
• Trustworthiness of structured and unstructured data
• Searchability and discovery of relevant data
• Siloed data across the organization
• Slower time-to-insight for issues that should be real time-based
• Compliance, privacy, and regulatory pressures
• Providing self-service access to data

To address these many data challenges, organizations are transforming their approach to data: they are undergoing application modernization and refining their data strategies to stay compliant while still fueling innovation.

Delivering trusted data throughout your organization requires the adoption of new methodologies and automation technologies to drive operational excellence in your data operations. This is known as DataOps. This is also referred to as "enterprise data fabric" by many and plays a critical role in ensuring that enterprises are gaining value from their data.

DataOps corresponds to the Organize rung of IBM's AI ladder; it helps answer questions such as the following:

• What data does your enterprise have, and who owns it?
• Where is that data located?
• What systems are using the data in question and for what purposes?
• Does the data meet all regulatory and compliance requirements?

DataOps also introduces agile development processes into data analytics so that data citizens and business users can work together more efficiently and effectively, resulting in a collaborative data management practice. And by using the power of automation, DataOps helps solve the issues associated with inefficiencies in data management, such as accessing, onboarding, preparing, integrating, and making data available.

DataOps is defined as the orchestration of people, processes, and technology to deliver trusted, high-quality data to whoever needs it.

People empowering your data citizens

A modern enterprise consists of many different "data citizens" – from the chief data officer; to data scientists, analysts, architects, and engineers; to the individual line of business users who need insights from their data. The Organize rung is about creating and sustaining a data-driven culture that enables collaboration across an organization to drive agility and scale.

Each organization has unique requirements where stakeholders in IT, data science, and the business lines need to add value to drive a successful business. Also, because governance is one of the driving forces needed to support DataOps, organizations can leverage existing data governance committees and lessons from tenured data governance programs to help establish this culture and commitment.

The benefits of DataOps mean that businesses function more efficiently once they implement the right technology and develop self-service data capabilities that make high-quality, trusted data available to the right people and processes as quickly as possible. The following diagram shows what a DataOps workflow might look like: architects, engineers, and analysts collaborate on infrastructure and raw data profiling; analysts, engineers, and scientists collaborate on building analytics models (whether those models use AI); and architects work with business users to operationalize those models, govern the data, and deliver insights to the points where they're needed.

Individuals within each role are designated as data stewards for a particular subset of data. The point data citizens of the DataOps methodology is that each of these different roles can rely on seeing data that is accurate, comprehensive, secure, and governed:

Figure 1.4 – DataOps workflow by roles

IBM has a rich portfolio of offerings (now available as services within Cloud Pak for Data) that address all the different requirements of DataOps, including data governance, automated data discovery, centralized data catalogs, ETL, governed data virtualization, data privacy/masking, master data management, and reference data management.