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First Look – Quiterian


Quiterian is now Actuate BIRT Analytics

Quiterian is a Spanish company with offices in the US, Mexico and Europe. Quiterian Analytics aims to be complementary to traditional tools for reporting by helping companies get more value from their data sooner. In particular they aim to help companies anticipate the future by providing simple to use predictive analytics and by empowering users while reducing IT costs. They see the BI market in three broad segments:

  • Dashboards and reports – traditional BI tools as well as some of the newer, easier to use visualization tools – with little or no focus on advanced analytics
  • Data mining tools focused on professional data miners and statisticians, typically not terribly visual.
  • Visual Data Mining tools that site between the two aimed at analysts and business users with no dependency on IT or data miners. This is where Quiterian is targeted

They offer fast and comprehensive data analysis and visualization, self-service for non-technical users and aim to be very agile with no need to pre-create cubes etc. Specifically Quiterian Analytics is a web-based SaaS tool that includes functions for

  • Its own analytical database that is a columnar/in memory hybrid
  • Data acquisition, exploration and engineering
  • Advanced analytics
  • Distributing and sharing findings
  • Workflow

Users can identify various data sources and load them up using Quiterian’s ETL. Drag and drop exploration tools allow users to profile the data, examine the underlying records etc. Charts of distributions, various statistical measures and frequency distribution can be displayed easily. Subsets can be selected from the graphs and everything recalculated based on those subsets.

Pivot tables, venn diagrams, bubble charts and more can be used to see what is going on in the data. For instance the customers who don’t have a credit card but do have a current account and don’t have a credit card from another company. These analyses can be saved, exported and shared in a variety of ways.

Expressions, calculated values, can be defined and used in analysis. Users can also use more advanced tools such as aggregates across rows, tools to manage quartiles/deciles, or tools to apply Pareto distributions to see what percentage of profit comes from a certain percentage of customers for instance. Profiling tools can be used to assess correlation, for instance by identifying which attributes are strongly correlated with profitability. A profile can then be applied to a group based on a previous query or analysis. New selections can be based on these profile attributes and pivot tables and other kinds of analysis can build on these selections.

In addition the tool supports decision trees, clustering and forecasting for more advanced analytics. The decision tree data mining algorithm has some nice usability features with color coding, accuracy measures etc. This can then be saved as an algorithm and be applied to any set of records selected in the tool using any of the other elements.

Finally the workflow component allows tasks within the tool to be scheduled. You can schedule actions to take when a new model is created, as part of a scheduled campaign, when specific technical changes are made or just at a particular time. These tasks can apply models or algorithms, send emails etc. Quiterian is working on adding some specific action types to expand this e.g. invoking actions in salesforce.com or email marketing tools as well as twitter, SAP, Omniture and others. As the range of actions expands, the ability to embed these analyses into decisions will likewise expand. A PMML import/export capability is also in the works.



Comments on this entry are closed.

  • madhuban kumar September 11, 2013, 8:25 am

    Hi James
    This is a great repository of information. Just a quick one, could you point me to companies that are in decision management working with combining Hadoop and In Db analysis while having integrated rules for action?

    • James Taylor September 11, 2013, 1:13 pm

      Still working on collecting some of this data but right now I would look at FICO (working with Hadoop and PMML) and Zementis (providing PMML integration for various DBMS and Hadooop vendors). SAS is doing some work around in-database and Hadoop but I am not sure about the rules side. More by email