In line with the recent trend of commercial tech companies embracing the open source ethos (or at least components of it), these hybrid cloud services can be deployed across multiple cloud providers and are based on open source technologies, open ecosystems that include company and third-party data, and open architectures that allows data to easily flow amongst the different services.

IBM's new cloud services include:

    IBM Compose Enterprise: A managed platform designed to help development teams build modern web-scale apps faster by enabling them to deploy business-ready open source databases in minutes on their own dedicated cloud servers.
    IBM Graph: The first fully managed graph database service built on Apache® TinkerPop that provides developers a complete stack for extending business-ready apps with real-time recommendations, fraud detection, IoT and network analysis uses.
    IBM Predictive Analytics: A service that allows developers to easily self-build machine learning models from a broad library into applications to help deliver predictions for specific product use cases, without the help of a data scientist.
    IBM Analytics Exchange: An open data exchange that includes a catalog of more than 150 publicly available datasets that can be used for analysis or integrated into applications.

IBM’s open approach means that any member of a data team can add or remove services at any time, to best suit immediate and long-term needs of their business. Developers can tap IBM Cloud Data Services to openly and freely move data in, across and out of the services.

Derek Schoettle, General Manager, Analytics Platform and Cloud Data Services considers IBM's cloud data services to be a 'one-stop-shop' of  data and analytics services, cloud development platform Bluemix; and Analytics Exchange, a repository of more than 150 public and free data sources, everything from census data to government economics statistics.

He claims the key beneficiaries of cloud data services is the citizen analyst who can enjoy a 'drag and drop experience' without enlisting the help of the IT department. Clearly IBM is intent on embracing business solutions that bring data analysis to the workplace.

Predictably, IBM has kept its finger in the AI pie with the release of Predictive Analytics, a complementary service to Apache Spark, that makes it easier for app developers to build their own machine learning models based on product use cases. The developer can define the use case, provide the data set to base the model on, and the Predictive Analytics service will evaluate a set of algorithms to determine which one will provide the most accurate predictions. This makes AI exploration quite accessible to the budding scientist.

While IBM has a slew of competitors to deal with, they are clearly in this for the long haul, and it will be interesting to see how their new services are received by consumers.