The opportunity to work with many clients on their data fabric journey continues to drive and inspire us to achieve even greater heights with our solutions. We believe the findings of the recent Forrester Data Fabric Wave for 2022 is a clear indication that the efforts we’ve taken to “[ramp up our] fabric offering aggressively” for our clients is delivering on its initial promise: A data fabric that dynamically and intelligently orchestrates governed data across a distributed landscape to provide a common data foundation for data consumers. 

The ability to compose and re-use data services with IBM’s data fabric on IBM Cloud Pak for Data allows you to tackle a variety of use cases such as data integration, data governance, AI governance, and data science and MLOps. In this way, the IBM data fabric delivers end-to-end automation to maximize productivity and timetovalue such as automated policy and business controls enforcement – all while leaving data where it resides. Let’s take a look at IBM’s take on some of the specific strengths recognized in Forrester’s Wave below. 

“IBM is a good fit for organizations with large, complex, distributed data stores across hybrid-and-multi-cloud environments, including legacy systems.” 

 Key attributes of IBM’s approach to data fabric 

Good for large, complex environments – As noted in the quote above from the report, IBM’s data fabric can handle the workload of large organizations even when the data in question is dispersed across hybrid or multi-cloud environments. Making complex environments operate more smoothly without excluding legacy systems is key to addressing the challenges faced by most organizations today, as noted in the report “IBM is strong in several data fabric capabilities, including data modeling, data catalog, data governance, data pipeline, data discovery and classification, event and transaction processing, and deployment options.”. 

Data governance – Some of the most exciting governance capabilities of the IBM Data fabric include automatically applying metadata to new datasets using machine learning as well as auto-generated data quality assessments and scoring and AI-based dataset recommendations. Providing the semantic
meaning to technical data assets is the foundation of any governance program and key to enabling self-service; IBM’s data fabric accelerates business understanding through machine learning applied classification, profiling and quality assessments. Moreover, dynamic masking alongside automatic policy enforcement and recognition of sensitive data allows businesses to make sure data is only in the hands of those with a need to know without losing value from key datasets. Finally, the automated and central enforcement of policy and business controls sets apart the data governance and privacy capability of IBM’s data fabric architecture. 

Data pipeline and Trustworthy AI – Getting enough data of sufficient quality to the right users or apps for analysis and AI processing will always be easier said than done. However, we’re working to narrow that gap as much as possible. A plethora of data integration styles such as ETL and ELT, data replication, change data capture and data virtualization help access all data seamlessly. Across the IBM data fabric components such as advanced data engineering work to automate access and sharing, while accelerating data delivery with active metadata. From there the focus becomes a trust in model and deployment with automated tools for data cleaning and preparation so users can dive right into the integrated tools for building, deploying, scaling and training models. Finally, the monitoring and management of models comes into sharp focus with automation of monitoring and retraining models to help avoid model degradation, drift and bias. 

Transaction processing – The storied reputation of IBM Db2 carries forward into the transaction processing capabilities of the IBM Data Fabric. All of the same great capabilities businesses have come to rely on remain and have been upgraded with a data fabric approach. The data integration styles mentioned above are, of course, part of these improvements, but even more is available including automatic workload balancing and elastic scaling to help ensure you’re ready for high volumes of data. Zero-downtime on data migration and upgrades also helps make sure your transaction processing never misses a beat. 

Deployment options – IBM’s approach to the data fabric is founded on giving clients the freedom to choose how they architect their solution. Nowhere is this more important than in our deployment options. Our clients have a broad range of deployment options from fully managed by IBM to clients fully managing the deployment themselves on premises. Additionally, can choose from several options for cloud vendor including AWS and Azure. And while our experts would be happy to advise on which capabilities should be chosen as part of the IBM Data Fabric to suit your existing architecture the ultimate decision lies with you on which of our highly-composable data fabric components are selected now and which you might want to activate at a future time – allowing you to avoid paying extra for a “one-size-fits-all” approach. 

We look forward to showing you more in the upcoming months. Until then, please check out The Forrester Wave™: Enterprise Data Fabric, Q2 2022 for the full details on what led us to be named a Leader. And take a moment to peruse The Data Differentiator our newly-released, continually-evolving guide shaped by CDOs, data leaders and data experts across IBM and beyond. 


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