Your data is everywhere, and that's part of the problem. Siloed data becomes an even bigger problem when your data and development teams also work in silos, causing a slow response to any incident. This lack of collaboration affects other areas of your business, too, from bug fixing to goal setting, making overall data use and operations inefficient.
With an IBM DataOps platform, you can eliminate the distinction between data- and development-focused teams through collaborative work to develop an overview of the data acquisition journey. As a result, incident responses will accelerate, bugs will be fixed faster and a cohesive team will be able to set and update performance goals in real-time. Your data will be agile, accurate and efficient from end to end.
Scale AI workloads, for all your data, anywhere
Access agile software to curate, govern, manage and provision data—connected and optimized at every stage of the data lifecycle—across the entire supply chain.
Apply controls for automated, customizable data quality, masking, tokenization and more so data is protected and compliance-verified at every step of its journey.
Offer stakeholders self-service access. This quality will make data easily discovered, selected, and provisioned to any destination while reducing IT dependence, accelerating analytic outcomes and lowering data costs.
A modern data catalog designed to help data scientists, data governance professionals and business analysts activate data for AI, business operations and analytics
A highly scalable data integration tool for designing, developing and running jobs that move and transform data; can be deployed on IBM Cloud Pak for Data, IBM Cloud® or on premises
A flexible multicloud data platform that integrates all your data, whether on premises or on any cloud, while helping keep it more secure at its source
A cloud-native, data observability platform that helps data teams detect their data incidents earlier and resolve them faster so they can deliver more reliable data
Catalog, protect and govern all data types, trace data lineage and manage data lakes.
Integrate, replicate and virtualize data in real time to meet data access and delivery needs accross multiple clouds.
Get a single, trusted, 360-degree view of data and enable users to know their data.
Cleanse and manage data while making it available accross your entire organization.
Transform large amounts of raw data into quality, consumable information.
Provide real-time change data capture and synchronization to make data available fast.
Automate metadata and policy management, provide consistent definitions and enable self-service management of high-quality enterprise data.
Modernizing its information architecture allowed Flagstar Bank to provide faster and more accurate data to its customers through their customized solutions.
While applying customer data privacy practices as part of data governance, Vanguard also became a digital transformation leader in its industry.
With a master data management platform, Sonoma County could connect four disparate data pools of 91,000 clients to serve their community better.
Explore how IBM DataOps builds a scalable and agile data-driven culture through automation, data quality and governance through this interactive guide.
Read how the IBM DataOps methodology and practice can help you deliver a business-ready data pipeline.
Learn what defines a leader, how a leader embraces DataOps and how DataOps benefits AI and other business initiatives.
Learn about DataOps dimensions, the team and the process. Then, learn how to organize data as part of the implementation of DataOps using IBM Cloud Pak® for Data; set up IBM Cloud Pak for Data on Red Hat® OpenShift®; set up governance artifacts for the data; and more.
Read this blog article about DataOps, a different perspective of its definition and how it works.