Each new release of Statistica builds upon our basic premise: Embedding analytics everywhere is the best route to better decision making. With Statistica 13, which we officially released in September and formally showcased to the world last week on the grand stage of Dell World, we’ve made it even easier to run any analytics on any data anywhere with new tools, deeper integrations and cutting-edge capabilities that push predictive algorithm model-building and scoring directly into the data source.
Even more powerful analytics
The latest improvements fit nicely into one of two buckets: General enhancements or new analytical capabilities. In the first bucket, the revamped GUI aligns with the most current Windows products to further our long-standing compliance with Windows standards. As a result, Statistica is more intuitive and easier to use than ever.
Next, we’ve strengthened integration with the Statistica Interactive Visualization and Dashboard engine so the entire experience of conceiving, authoring and rendering visualizations is done in Statistica. Additionally, our integrated web server ability handles visualization rendering and management, so now analytic output can be easily distributed globally for greater collaboration and information sharing.
About 90 percent of our new analytical capabilities came directly from recommendations by our world-class user community. Some, like our new Statistica stability and shelf life analysis as well as web data entry, were suggestions from pharmaceutical companies that appreciate our flexibility in addressing their specialized industry requirements.
Others, such as in-database and in-Hadoop analytics strengthen our leadership in big data predictive analytics by bringing math to the data. With this enhancement, data consumers can build an analytical model in Statistica, click one button and then deploy in a Hadoop cluster. This is great for organizations that stack a lot of data in a big data environment and want to get at it with predictive analytics.
Something for everyone
For data scientists, we’ve added stepwise modeling, lasso regression and tree clustering. And a new ability to mine Chinese text broadens our international scope as Statistica now is available in 12 languages.
Now not only do we speak more languages, we’ve expanded our community of analysts with in-database processing that enables them to run correlations on full-volume data. What we’ve done is decompose elements of algorithm formulas and simulations so they can be run directly in SQL, Oracle and Teradata—really any number of OLE or ODBC databases. What this means for our customers is they can better leverage big data investments while enabling people other than data scientists to run correlations. This extends the universe of users dramatically as anyone from a summer intern to a business analyst can run very sophisticated analytics without concerns about complex data management and sampling tasks.
Analyzing data ― even big data ― right where it lives
Processing data where the data resides, whether that’s a database or a Hadoop cluster, is another important part in Dell’s ongoing evolution to make Statistica accessible to a wider audience. We took another step in that journey in Statistica 13 with new Native Distributed Analytics (NDA) capabilities that integrate with Dell Boomi to transport analytic models anywhere in the world.
Dell Boomi is Dell’s integration platform that lets organizations connect any combination of cloud and on-premises applications without software or appliances. So, we applied this incredibly cool technology to let users run analytics directly where the data actually lives. It makes perfect sense. Take a model like a neural net and sent it across the network in the size of an email with an attachment. This lightweight model then is run against the data in a SQL database and results are returned quickly and efficiently.
With Boomi, we can take analytics to the data in a highly secure, efficient manner. This capability, which is unique to Dell, is designed to make analytics more accessible and available. That’s also part of our goal in extending the work we started with open source R and Azure ML last year. We’re making strides in collective intelligence to open our platform further and enable people to bring in models from all over the world to solve the toughest business problems.
How will Statistica 13 simplify your work?
Statistica 13 deals customers a winning hand with improvements, enhancements and new integrations that illustrate our continuing focus on lean-forward technology. If you’d like to learn more about how Statistica can simplify your work, check out our upcoming webcast, What’s New in Statistica 13.
What new features in our “lucky 13” release will help your organization do more with your big data predictive analytics? Connect with me on Twitter at @johnkthompson60 to share your thoughts.