After a quick catch up with the IBM Operational Decision Manager (business rules) and IBM SPSS Modeler teams to talk about their cloud and Spark enhancements respectively, it’s time for more on Watson Analytics. I last blogged about Watson Analytics last year so it will be interesting to see what’s been going on since then.
IBM Watson Analytics now has 500,000 registered professionals with widespread adoption across multiple industries. It’s positioned as a way to get an initial cognitive focus on your business. Last week they announced a set of data connections for data sources, an ability to do secure data access and some new social analytics.
From a product point of view, several new features have been introduced:
- From a usage perspective the most effective driver of successful use is the availability of suitable data – once people have uploaded data they work with it more for instance. The new sample dataset marketplace is a way to get rapid access to data they can work with to learn.
- Data replacement has been automated, ensuring that existing explorations and dashboards refresh automatically when data is replaced by updated data.
- Integration with DataWorks provides a set of additional data connections, new data quality and shaping services and a secure gateway to on premise data sources. These connectors also allow the quality and shaping services to be applied to the data before it is uploaded for access in Watson Analytics.
- The tool supports conversations for collaboration, allowing users to discuss an asset and poll colleagues for instance. Users can save the conversation with the asset also.
- Results can be shared by downloading explorations, predictions, dashboards as images, PDFs or PowerPoint files. Private view links can also be used to share results inside Watson Analytics.
Meanwhile in the lab they are developing expert storybooks that are co-branded templates to deliver best practice analytic presentation templates. In addition there has been a significant focus on social analytics, especially social analytics beyond engagement.
A Watson Analytics customer gave a couple of good use cases. In particular there is great value in rapidly identifying data sources as potentially useful, allowing effective analysis of them without having to integrate them into the data warehouse/ETL process first. Essentially they can load up the data and immediately get some suggested ways to view and analyze the data. This saved a bunch of time in terms of data preparation, conversion, analysis etc.
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