The Difference Between Analytics and Advanced Analytics

As applications and technology evolves more advanced forms appear that can be confusing to those still grappling with the basics. One such area in Business Intelligence [BI] is analytics. The benchmark today is ‘advanced analytics’ – so just what is the difference? Many organizations don't understand that reporting and analytics are different practices, often with different data requirements.

• Standard analytics is based on traditional data warehouse-driven reporting and OLAP practices
• Advanced analytics is the result of multiple, converging trends:
  
  • Complex SQL-driven queries - query-based analytics (which relies on complex SQL statements to define recent business events)
  • Predictive analytic technologies - uses data mining and statistical methods to anticipate future events
  • Higher performance databases

As organizations' recognize the need to understand constantly changing business environments and to discover opportunities for cost reductions and new sales targets.

Advanced analytics isn't an out-of-box solution – certain challenges must be overcome, in particular:

• Database development from a traditional data warehouse to support the requirements of reporting and online analytic processing (OLAP), to a powerful analytic databases suitable for advanced analytics, whether query-based or predictive.
• Issues with data – integration, data quality, and data modelling which can make or break the success of any predictive analytic practice. Traditional data management practices do not meet the needs of advanced predictive analytics.

A Seven Step Approach to Advanced Analytics

1. Get clear how you plan to use advanced analytic technologies – set key goals such as to discover existing relationships, anticipate the future, and adapt to change. The value of predictive analytics is the discovery of unknown facts and relationships, the confirmation of known or suspected relationships, and the leverage of those relationships for better decision-making.
2. Integrate these goals with business activity, ROI and business value.
3. Scale up current reporting and data warehouse infrastructures - in particular, data integration processes to handle larger data volumnot compromise operational system performance.
4. Format data for advanced analytics - to be consumed by a range of analytic technologies, including traditional OLAP tools, query-based analytic tools, and predictive analytic tools. Data flattening may be required as most algorithms are optimized to run fast and accurately with a flat record structure.
5. Distinguish between data warehouses, data marts, and analytic databases - an EDW can handle both query-intense and predictive-scoring workloads, plus it can manage the low-level, detailed data that advanced analytics often requires. Design a data warehouse architecture that's able to accommodate analytics.
6. Develop appropriate parameters for advanced analytics - the available parameters limit the breadth of the OLAP analysis, and require technical development of parameters.
7. Apply the outcomes of advanced analytic practices to existing BI and DW activities.

Next: Alerting

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