In early 2018, I had a conversation with some of my leadership team about what we could build to address our client’s most pressing needs. We knew data was growing at an exponential rate, and analytics were critical. Our decision was to build a data and AI platform, one single platform that clients could use to drive enterprise data and AI strategy in a landscape of rapidly evolving technology centering more and more on data and ml. We validated our strategy with clients, and began work on what we codenamed Project Zen, which met the world a few months later as IBM Cloud Private for Data and is now called IBM Cloud Pak for Data.
Since its arrival, our platform has continued to develop in its capabilities. Launching with a core of integrated services based off our Db2, IBM Governance Catalog and IBM Watson Studio product lines, a series of semi-annual updates has brought in most of the key capabilities of the IBM Data & AI portfolio, including IBM Streams, IBM Watson Knowledge Catalog, IBM DataStage, IBM SPSS, IBM Cognos Analytics, IBM Planning Analytics and the core IBM Watson AI services including Watson Assistant and Watson Discovery. These existing services – and many others not named – are augmented by new IBM capabilities designed exclusively for the platform, including IBM Data Virtualization, IBM Watson OpenScale and IBM Watson AIOps (coming soon), as well as a growing ecosystem of third party extensions including Intel, MongoDB, Senzing and Portworx amongst many others.
These services have helped our customers expand their AI Ladder from being able to simply collect, organize and analyze data to becoming a singular AI platform capable of handling the entire analytics journey. This could not have happened without perhaps the biggest game-changer in this product’s history, the shift from running our container management from IBM Cloud Private to Red Hat OpenShift Container Platform (hence the accompanying name change). OpenShift has allowed our customers to consume our services on the cloud of their choice and hardware of their choice as easy to manage containers on a single user experience. Launches of “Quick Starts’ on AWS and Azure have shown our commitment to a hybrid multi-cloud approach. OpenShift also allowed us to create IBM Cloud Pak for Data System, an OpenShift in a box hyper-converged system that removes the need to bring one’s own hardware to the platform and substantially cut down deployment time.
“Data scientists are both more productive with Cloud Pak for Data and can deploy models to market faster. Additionally, due to Cloud Pak for Data’s integrated platform, companies avoided costs associated with legacy analytics tools or otherwise building a comparable solution internally.” – New Technology: The Projected Total Economic Impact Of IBM Cloud Pak For Data
These advances over the last two years have brought us to where we are today, with V3.0 announced at Think and set to be released in the coming weeks. The major upgrades, including an improved user experience, new services and tighter integration into the Red Hat ecosystem, can be viewed here. The net is we continue to do our best to build a customer-focused, integrated platform that allows any user to consume the services they need to build a proper data and AI foundation.
“Companies are struggling with managing the quickly increasing amounts of data in their organizations and setting up a cohesive governance system and strategy. Cloud Pak for Data’s Collect and Organize solutions help address that challenge.” – New Technology: The Projected Total Economic Impact of IBM Cloud Pak For Data
Consider the current approach to data – multiple user groups consuming software only they know how to use, with no ability to communicate across business function, across clouds, across data silos. With Cloud Pak for Data V3.0, this is a problem of the past. Having every needed service inside the same user experience, your entire team can perform cross-functional data & AI projects without ever having to leave the platform. As our platform has all the services needed to support a quality end to end experience, deploys where and on what cloud you need it to, and maintains the highest levels of data security and governance, we feel we are the leading platform for AI development on the market.
After two years of developing Cloud Pak for Data into the premier offer of IBM Data & AI, our goal is to continue the momentum to ensure we are able to remain the AI platform of choice. For the rest of 2020, that comes down to three strategic priorities: Edge analytics, an option for a managed service and ecosystem expansion. The first of these, Edge analytics, has the potential to change how businesses operate their core data landscape. Part of the overall IBM emphasis on edge computing, Cloud Pak for Data will allow our customers to drive real-time insights at the source of data and increase security by removing unnecessary data movement with seamless extension to the network edge. You can read more about this here.
In addition to moving clients to the edge, we want to give them the ability to work in Cloud Pak for Data as a managed service on the IBM Cloud. Taking advantage of the security and power of the IBM Cloud for data and AI workloads, clients will be able to provision and use Cloud Pak for Data with but a click in a fully managed environment.
Finally, while we have brought most of the Data & AI portfolio to Cloud Pak for Data already, bringing more services to our customers is always the goal for our team. Each release will continue to see new features available from within IBM, open source and third-party vendors, as well as numerous industry accelerators and other aides to the data scientists working on our platform.
Please join me in celebrating the two-year anniversary of IBM Cloud Pak for Data. Try it out for yourself today, or if you want to learn more about the benefits of Cloud Pak for Data join our webinar with our guests from Forrester on the projected total economic impact of the platform from the February 2020 commissioned study conducted by Forrester Consulting.