Analytics ecosystem: BI, Visual DM and DataMining

1. Visualization Tools Visual Data Mining Data Mining
Exploring the evidence Discovering the hidden Predictive models managed by experts
Accurate predictive models for core and key
Distributing reports and dashboards Analyzing raw data to obtain
business issues to be developed
that are based on a predefined data model immediate key business insights
by expert Mathematicians and Statisticians
to a large number of users through intuitive and fast Data Mining techniques
Visualizing sales per region Customer churn prediction: why are customers leaving? who will Money laundering patterns
Margin per product leave next? Risk scoring
Examples Cost per channel Which is the best product to recommend to each customer? Risk limit per customer
Benefit per month/quarter/year Identifying cross and up selling opportunities Fraud prediction
Tracking KPIs, goals and achievements How are customers going to respond to a specific campaign?
Customers’ future behaviour
Users Occasional and business users. Analysts, power users and business users. Mathematicians and Statisticians.
Making reports available to end users on a visual and Dynamic analysis of large data sets for immediate key insights. Accuracy on statistical models for core and key topics (fraud,
Very good at
predefined reporting and dashboarding environment. User-friendly, powerful and intuitive Data Mining techniques. risks, forecasting…) to be developed by expert data miners.
Outcome Flexible reports and dashboards. Fast insights and advanced analytics with large volumes of data. Accurate statistical and predictive models.
Scope Departamental scope. Flexible and visual reporting and Departamental deployment & corporate scope. Fast predictive Corporate and departamental deployments.
dashboarding. models. Immediate reactions to business opportunities. Core business models and scorings.
Exploration techniques (drill down, dice, slice, Exploration techniques: drill down, dice, slice, aggregate,
Exploration aggregate, break down,...) that depend on predefined break down, etc. Not limited to any predefined OLAP, Non-visual programming language.
filters, measures and dimensions. measures neither dimensions.
User autonomy Low. Dependence on the IT department to create new No dependence on IT or on data miners. Users self-sufficiency to
None. Dependence on data miners.
charts and reports or to include new data. discover and interpret immediate insights, freely and visually.
Analytics & No advanced analytical techniques. Limited data Venn, Pareto, Pivot Tables, clustering, profiling, Decision Tree,
Many algorithms aimed at experienced data miners.
Engineering engineering; data can not be enriched on the fly. forecasting,... and data engineering on the fly (aggregates,
Data engineering based on expert programming.
expressions, decodes, percentiles, numeric bands...)
Set-up time Weeks to months. Any new report takes hours to days. Days to weeks. All analysis can be instantly performed by users. Months. Any new model takes days to weeks.
Technical features Predefined data model. OLAP or in-memory DB No cubes nor OLAP needed. Column-based and in-memory DB
Powerful hardware required.
technology. Middle to high hardware level required. technology. Large data sets. Light hardware required.