Today I’m going to take a first look at Watson Analytics, the newest BI offering from IBM. To say Watson Analytics is critical to IBM is an understatement. While IBM assures investors of their profitable future as a cloud analytics and services company, Gartner’s 2015 BI Magic Quadrant shows continued deterioration for IBM in the face of stiff competition from Tableau, Qlik and others.
The market clamors for easily deployed, departmental, user friendly BI tools and IBM’s existing champion Cognos is a slow moving behemoth tailored to IT managed enterprise deployments. Their first crack at meeting the new demands of analytics users, Cognos Insight, saw very limited adoption. So IBM went back to the drawing board and Watson Analytics was born.
A New Business Model for IBM Analytics?
Let’s get the most important question out of the way – how much does it cost? Watson Analytics is currently a cloud only app which can be accessed for free with .xlsx or .csv data sources of up to 500,00 rows and 50 columns with 500MB of total storage. 1,000,000 rows and 256 columns with 2GB total storage will run you $30.00 a month. This smartly removes the two biggest roadblocks to acquisition faced by Cognos, SPSS or TM1 – the IT Department and a few million dollars.
IBM has taken all the hard won software know-how of their existing analytics stack and applied it to Watson Analytics. You can see the fingerprints of Cognos and SPSS all over. SPSS handles the statistical magic happening under the hood while the Cognos RAVE visualization engine appears to drive data presentation. It’s wrapped in a mostly intuitive and very modern UI capable of competing with Tableau & Friends in the battle for eyeballs.
Who Is Your Watson? And What Does He Do??
There are currently three main features to Watson Analytics, Explore, Predict and Assemble. To help illustrate how they function I created some visualizations and predictions using a data set of 2014 NFL offensive stats. I will give each of these features the full treatment in future posts. Below is a quick overview and screenshot of the output.
Explore is kind of the “query tool” of Watson Analytics. Here you create visualizations and do guided data discovery. Swapping between visualizations is intuitive and there is a surprising amount of control hidden away in a few hard to find menus. Let’s take a look at the basic layout.
- The small blue summary charts at the top of the screen are interesting relationships identified automatically when Watson Analytics first analyzes the data set. Clicking on them will take you to a fully realized visualization of lower level detail.
- The visualization itself occupies the center of the screen and is fully interactive. For example, clicking on the “Calvin Johnson” tree map segment opens a menu that allows you to change, filter or exclude that element.
- To the right of the visualization is the key and a slide filter. These were generated automatically and provide an additional way to edit or filter the data elements on the visualization.
- The bottom of the screen lists all data elements and provides access to more advanced capabilities, including custom calculations and the ability to define dimensions for slice-and-dice analysis.
Predict is probably the part of Watson Analytics most aggressively marketed by IBM. Predict does exactly you think – applies statistical models to make a predictive analysis of the data set. It’s highly automated and you do not need to be a data science to understand the output, which perhaps a double edged sword because if you aren’t a data scientist there’s a big risk of misinterpreting the data.
- The top of the screen provides various details about the prediction, including a score of the data quality, top field associations, and additional statistically significant relationships.
- In the center we see the primary predictive relationships in the data – in the case, that receiving targets and yards are the primary predictors of total receptions. More interesting perhaps is the much weaker relationship between touchdowns and total receptions.
- You can go into much greater detail about the statistics behind all of these elements with just a click. I’ll explore this in detail in an upcoming post.
Assemble is the dashboarding tool in Watson Analtyics. This is where you combine the visualizations from Explore and insights of Predict to tell the graphical story of the data. It provides an interactive environment and, I think, looks quite good compared to Cognos. But in its current form your options for sharing and collaboration are pretty sparse.
- The dashboard is highly interactive without any configuration, which should make Cognos developers accustomed to Active Reports party in the streets.
- New tabs and a variety of visualizations, images and external content can be added easily at the top of the screen.
- Total development time for a dashboard is reduced to minutes vs days or weeks in Cognos. I built this in about four minutes (and you can tell!)
And Now For The Secret Sauce
Looks cool so far but is that it? Well, no. Embedded in everything is a search box connected to a natural language processor that generates visualizations on the fly rather than crawling for pre-existing content…! To me this is the most exciting feature of the whole thing because so much of my job is hand crafting bespoke, locally sourced reports and visualizations which, popular as they may be in certain parts of Brooklyn, is a job better done by a machine.
The suggested visualizations you see above in response to my query are not something a human being was paid to create, and that seems to me to be the secret sauce of Watson Analytics. More than the predictive modeling and the cloud and the other buzzwords, this is the big improvement on IBM’s existing analytics platforms. Type in a question and get three days worth of the BI team’s best visualizations. Don’t like the results? Just ask another question.
Watson Analytics, Cognos and The Future
I am still thinking through the implications of IBM’s new direction for both the Cognos platform and Cognos developers. IBM has not outlined publicly their integration plans between the two platforms but it’s easy see this as the direction in which Cognos is headed in terms of functionality and aesthetics. As impressive as the above features are the infrastructure currently surrounding Watson Analytics for collaboration, metadata management, security, etc… is non-existent. I suspect that Cognos will be a primary vehicle bringing Watson to the enterprise.
Made it to the end? Man you must be as nerdy about this stuff as I. Check back soon for detailed explorations of Watson Analytics’ core features, as well as my thoughts on where all this is headed.