Cloud, Mobile, and Transactional Analytics

Cloud, Mobile, and Transactional Analytics

Organizations need enhanced insight into customer relationships, payments and transactions, revenues, costs, risks, etc. – considered across geographical boundaries, product lines, and all aspects of business.

Social, mobile, analytics and cloud technologies are laying the foundations for a new enterprise IT architecture. They're working in conjunction together: inexpensive standardized hardware components, service-based software, Big Data processing, broadband networks, social media, with intelligent mobile business and consumer devices.

Special Effects

Through what's known as the Google Effect, information and physical location have been fully virtualized. The LinkedIn Effect lets us map our professional networks, and quickly locate trusted expertise. For enterprises, the Amazon Effect has brought us the virtual retail interface for customers.

IP-based communication platforms (video, voice or e-mail) make speaking to someone on the other side of the world the same as shouting across the street. Well, almost. This is the Skype Effect.

For businesses, masses of data that were previously too costly to transmit are now affordable to consume.

There's a lot of information. And something has to be done with it.


Advances in analytics and business intelligence are helping organizations to determine what they're doing, why they are doing it, and what they should be doing to survive.

Unstructured data analytics tools can incorporate information from business records, online discussion forums, social networks and call scripts to determine customer reactions or market opportunities. There are algorithms which measure their own accuracy and feed that information back into models to create self-improving predictive analysis.

Some tools (like IBM’s Watson platform) even use natural language analytics to understand questions, context and semantics, then analyze terabytes of data to identify and rank likely answers.

IBM has partnered with Veristorm to offer zDoop, a commercial Hadoop solution for Linux on System z. By keeping the data on System z mainframe, and with support for direct import from z/OS into Hive, zDoop lets you perform operations to prepare data for analytics, without incurring MIPS charges.


Cloud services are giving greater access to Big Data analytics, optimization and decision management tools. The cloud also makes it easier to share learning and replicate success across organizations. Collaboration, rapid application development (RAD) and cloud-based community marketplaces are changing the pattern of investment in analytics.

Cloud-based analytics are founded on scalable service-based infrastructure, elastic on-demand computing power, standardized adapters for accessing data sources, reusable solution components, and RAD tools. Virtually any type of existing model can be imported into operational decision-making processes and workflows.

Tools include domain-specific application frameworks with libraries of predictive customer characteristics, extensible data models, process templates and rapidly adaptable user interfaces.

IBM's Enterprise Cloud System blends leading software, storage and server technologies into a flexible and integrated solution. Cloud services can be delivered with the ability to rapidly deploy a fully automated OpenStack based cloud orchestration and monitoring environment. This occurs via a Linux environment with mainframe qualities of service.

The new System z utility pricing for managed service providers (MSPs) offers a ‘pay as you grow’ pricing model. This can reduce the total cost of acquiring System z technologies by as much as 65 percent. IBM's scheme gives organizations the ability to start small, and scale up to 6,000 virtual machines in a single footprint. With the Enterprise Cloud System and new utility base, clients can provide superior cloud services for as low as half the cost of public cloud or x86 private cloud alternatives.


At the IBM Mainframe50 event in April 2014, IBM announced a number of software products which make it easier to build and integrate new mobile apps with back-end resources. Their goal is to bring modern mobile-enabled application workloads onto the mainframe.

IBM WebSphere Liberty z/OS Connect provides a simple, secure and scalable way to discover and call application assets or infrastructure on z/OS from Web/Cloud/Mobile applications (via RESTful services).

IBM Customer Information Control System (CICS) Transaction Server for z/OS and CICS Tools for z/OS support multiple application versions running on a single platform instance. Several updates and options are designed to attract mobile-friendly applications onto the mainframe for back-end processing. CICS applications written in traditional mainframe languages can interact directly with mobile clients.

IBM Mobile Quality Assurance (IBM MQA) facilitates the creation of apps to capture tester and live-user experiences, including context-aware crash logs and in-app bug reports, in-app user feedback, and streamlined quality metrics.

Best Practices

1. Collect and collate structured and unstructured data to understand attitudes, correlations, preferences and behavior.

2. Use comprehensive, easy-to-use data sets and predictive analytics tools optimized for business users, analysts and statistical programmers.

3. Use data mining and modeling tools to create predictive customer and operational models.

4. Deploy results-oriented decisions, by building analytics into your operations.

5. Integrate those analytics that predict outcomes, and automate processes to deliver insight.