Lifting the lid on IBM's 'Project Neo'

IBM recently gave analysts an in-depth look at the products and underlying strategy of its extensive business analytics (BA) portfolio and a product that caught our eye is IBM’s recently announced visual data discovery tool, codenamed “Project Neo.”

The solution is the result of IBM Labs coalescing some of the company’s latest in-memory database, analytics, data visualisation, and design initiatives.

Business analytics is front and centre for IBM. Investments of nearly $US20 billion into R&D and M&A activity have left IBM with an impressive (and extensive) list of BA solutions. However, IBM has been somewhat underrepresented in the increasingly competitive market of visual data discovery solutions. Vendors such as Tableau, Tibco Spotfire, and QlikTech have led the way with intuitive solutions that have attracted a drove of customers who are looking for intuitive and visual ways to interact with their business data.

But IBM is now upping its ante in the visual data discovery market by making Project Neo available as a beta program in early 2014. The stated goal for the solution is similar to that of its data discovery competitors: to simplify the analysis and understanding of data for the nontechnical business user. But IBM’s approach is different – it aims to do so by allowing business users to ask questions of raw tabular data sets in plain English.

The UI is simple and contains only a single Google-like free-text search bar where a user can ask questions such as “Why are my sales down in Asia?” or “Will higher education create a better employee?” Behind the scenes, Project Neo automates the process of data modeling, advanced and predictive data analysis, and the creation of data visualisations. The end result is interactive visualisations and natural language explanations of what was discovered.

The technologies Project Neo leverages are from five other IBM initiatives:

• IBM SPSS Analytic Catalyst – automates data preparation and analytics procedures to produce results in Project Neo in the form of interactive visualisations and plain text that explains the results in layman’s terms.
• IBM DB2 BLU – provides in-memory and columnar processing of data and analytic workloads “behind the scenes” in Project Neo.
• IBM InfoSphere Data Explorer – federates, navigates, and indexes a variety of data sources and applications for a unified search experience in Project Neo.
• IBM Rapidly Adaptive Visualisation Engine (RAVE), Smart Metadata, and Answer Coach technology – automates and optimises the selection and creation of interactive and advanced data visualisations that are presented to the end user in Project Neo.
• IBM Design initiative – creates better user experiences (UX) across IBM’s entire portfolio, which for Project Neo means a sleek, simple, and intuitive UI.

Black-box approach has it challenges

Project Neo offers a black-box search-oriented approach to visual data discovery, i.e. it automates most steps necessary for analytic insight: data modelling, data analysis, and data visualisation. In comparison, competing solutions such as Tableau, SAP Lumira, and QlikView still require end users to understand underlying table structures, pick proper dimensions and measures, choose the right type of calculation and analytics, and possess a certain level of skill to choose and interpret the data visualisations.

Project Neo has great potential to help users get quick, intuitive, and self-service insights into data. What Ovum finds specifically useful is that it offers nontechnical users a guided-analysis approach with incremental modelling where the user can adjust their questions as the analysis progresses.

However, Project Neo’s black-box approach might not be for everyone – and more advanced users might not feel that IBM’s algorithms and models are the best fit for their data. For Project Neo to be a successful BI tool, IBM needs to ensure that the underlying data preparation and analysis are transparent and “open” so the models can be tweaked and audited by more skilled workers. Further, from an enterprise-wide BI strategy standpoint, it is imperative that the results can be fed back into IBM’s other BI solutions, such as SPSS and Cognos, for further analysis, tweaking, and sharing when needed.

Where does Project Neo fit in?

Project Neo is not IBM’s first attempt to make a mark in the visual data discovery market. Early in 2012, IBM launched Cognos Insight as a competitive answer to a wave of self-service data visualisation products gaining significant traction in the market, such as Tableau and QlikTech. However, after the initial release, there has been little noise around the product, and the goals of Project Neo seem to overlap significantly with the goals of Cognos Insight. IBM has not clarified the roadmap for Cognos Insight after introducing Project Neo, but it begs the question if there is a future for the product.

IBM Watson and Project Neo also share many similarities, in particular the free-text search interface. They are, however, built on different technologies and frameworks. The three biggest differences seem to be scalability, price, and Watson’s machine-learning capabilities.

Going forward, the challenge for IBM is one it has had to tackle over and over again, namely that of consolidation. As noted in Ovum’s recent Opinion piece “IBM underscores Big Data breadth and depth at IOD,” IBM has a tendency to talk about, and sell, its solutions in silos. And it is key that IBM clarifies what analytics needs Project Neo can fill, when to use IBM Watson, who should use Cognos Insight, and how they all can coexist (if at all).

Without this, IBM risks confusing customers and partners looking to IBM for a visual data discovery solution.

Fredrik Tunvall is an analyst at Ovum in its software information management division.