Ready to deliver on your data strategy?
With your data strategy in place, it's time to implement a data architecture that helps drive operational efficiency, reduce risk and increase revenue growth for your organization. The secret is creating an environment where employees can find and explore data themselves, empowering them to surface insights that shape innovative, data-driven decisions.
So how do you put your data strategy into practice across a complex hybrid multicloud data environment?
Implementing your vision means getting the right technology architecture in place, one that allows you to see where all your data sits so that you can make the most of it. Creating a way to access data wherever it resides enables you to manage the demands of compliance, security and governance risks, and regulatory requirements, while identifying insights that will drive the business forward.
“While individual point solutions have been able to address some of these concerns in the past it is rapidly becoming clear that a more robust solution is needed – one that can address a business’s most pressing data and AI need while providing an easy path to solve additional challenges,” says Jay Limburn, who's a VP and Distinguished Engineer for IBM's Data and AI product portfolio. “That solution is based upon an integrated data fabric architecture.”
If you really want to create this culture of people working with data, consuming data, making decisions based on data, it starts with having easy access to data.
Chief AI Architect
Why data fabric?
A data fabric is an architectural approach to simplifying data access in an organization. It smooths the way for the end-to-end integration of various data pipelines and cloud environments through intelligent and automated systems.
Until recently, most organizations stored data aligned to specific lines of business separately. HR had their data in one place, customer service in another and accounting in another. Data lived in separate environments despite whatever natural overlaps might occur.
A data fabric brings multiple data sources together, augmenting rather than replacing your existing technology and giving you a centralized reach across all points on your data landscape. With end-to-end data management capabilities, a data fabric ensures your various kinds of data can be successfully combined, accessed and governed, so that business users can put data to work.
This holistic approach to data and AI elevates the use of data as an enterprise asset. With a data fabric architecture you can discover the data you have, process it in disparate locations and collect, store, analyze and operationalize AI at scale.
“A data fabric connects the consumers of the data to the sources of the data. But what is unique about that is that that connection layer is always governed and always secure and follows privacy and compliance rules,” says Priya Krishnan, IBM’s product leader for Data and AI.
Data fabric or data mesh?
We’ve talked about data fabric. But what about data mesh, another approach that also streamlines enterprise-wide use of data? A data mesh is a decentralized data architecture that organizes data by specific business domain — marketing, sales, supply chain and others — by providing more ownership to the producers of a given data set.
Data fabric and data mesh are both emerging data management concepts that address the complexities of working with big data and its related data platforms in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complementary. Each is designed to solve the problem of bringing data together and analyzing it in a unified semantic system. Many components of a data mesh are also in a data fabric, making data fabric the more flexible option to start with.
Explore the architecture
An important first step in building a data fabric is identifying a specific use case that delivers value for the organization. You can build in increments to drive quick wins with agile MVPs, starting with use cases like multicloud data integration, data governance and privacy, MLOps and trustworthy AI, or holistic customer 360 views. This will help you demonstrate ongoing success while executing on your longer-term data strategy.
Design your data strategy
Six steps to building a data-driven organization, from ideation to execution.