Table of contents for IBM Insight 2015
Next up at IBM Insights is the Advanced Analytics keynote with a focus on how to get insight out of all these new data sources and infrastructure. The session is focused on Spark, on hybrid cloud-on premise solutions and on trusted data. Companies are moving up maturity curve they say, moving from cost reduction to a modern BI infrastructure and thence to self-service and new business models (not a maturity curve I would have used as self-service is orthogonal to maturity not a step on the path).
A cognitive business is based they say on analytics. For a cognitive business every business process (every business decision) is enhanced by analytics. Analytics is used to solve problems and make better business decisions across the whole organization while continuous improvement and learning drive ongoing change. This takes a range of analytic tools from those aimed at business users, powerful data science and advanced analytics capabilities, strong data integration etc etc. Specifically a platform that is:
- Hybrid – cloud or on premise access to data of various types no matter where it is stored
- Trusted – data and insight that is believable and usable
- Open – based on and leveraging open source analytic capabilities
Starting with data scientists, one of the two key roles that the analytic stack must support. The IBM Predictive Analytics stack has recently been extended with Spark-based algorithms. This means that R, Python and Spark routines can be included in modeling. The coding of these algorithms can be encapsulated so that not everyone has to code. With more data, more options for analyzing the data, data scientists have had to become coders and data engineers too. Examples of needs for external data that must be integrated with internal data, open algorithms, streaming data and more abound.
Analysts too need support from the stack which is where Watson Analytics comes in (I just blogged about Watson Analytics). In addition, they say, analysts need support fort external data like weather and twitter data as well as unstructured content managed in box.net for instance. Enhance first, they say, and then analyze the data without preconceived notions and therefore without bias.
In parallel with the investment in Watson Analytics for discovery and analysis, the user interface for the core Cognos product has also been improved. IBM Cognos Analytics is the evolution of the Cognos BI stack with a new user interface, a simpler one with more work space and less menus and one that is mobile-friendly. In particular the search is designed to be very context aware to make it easy to find content. While all the content developed with Cognos BI is brought forward, the new environment allows more interactivity so that users can add filters or otherwise interact with data in a report build by someone else.
It also allows end users to bring in different data sources, easily blend them and then report across the now integrated data. The product will suggest possible approaches to integrate the data based on intent expressed by the user. Having dynamically built a data model in this way the user can now self-serve reporting or visualizations against the data. The environment allows a relatively non-technical user to do more while still allowing a more sophisticated user to gradually configure and extend them.
It’s clear from the panel that follows this overview that Cognos customers really like the new UI, the mobile features, the ability for less skilled users (and mobile users) to develop their own dashboards and reports, and more rapid access to more data.
Three announcements then – support for Spark in SPSS Predictive Analytics, Watson Analytics enhancements especially around data access and Cognos Analytics as a new generation of Cognos BI with a focus on mobile and self-service.