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
- Standard analytics is based on traditional data warehouse-driven
reporting and OLAP practices
- Advanced analytics is the result of multiple, converging trends:
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
- 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.
- Integrate these goals with business activity, ROI and business
- Scale up current reporting and data warehouse infrastructures
- in particular, data integration processes to handle larger data
volumnot compromise operational system performance.
- 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.
- 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.
- Develop appropriate parameters for advanced analytics - the
available parameters limit the breadth of the OLAP
analysis, and require technical development of parameters.
- Apply the outcomes of advanced analytic practices to existing
BI and DW activities.
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