CPM, BI and Analytics – Are they Merging into One ?

With Business Intelligence Suites attaching to more and more disparate sources of information within a corporate environment, and the processing power and
availability of analytical systems within these Suites, are the three classic
disciplines of Corporate Performance Management (CPM), Business Intelligence
(BI) and Analytics beginning to merge into one seamless subject?

The Diciplines

Corporate Performance Management

CPM used to be the prime driver behind
improvements in an organizations performance within the market place. Utilizing
methods incorporating the Balanced Scorecard from Kaplan, Six Sigma or Lean the
back office staff would analyze the company business processes and seek to
improve their performance by analyzing the available metrics of past
performance, strategic planning, forecasting and workflow reporting. It would utilize
both financial and operational performance results to provide an indication of
success or failure against particular goal orientated targets, which would
point to the reasons for that result.

The typical strategy for CPM would

  • Selection
    of goals,
  • Consolidation
    of measurement information relevant to an organizations’ progress against these
    goals, and
  • Interventions
    made by managers in light of this information with a view to improving future
    performance against these goals.

Although presented here sequentially,
all three activities will run concurrently.

Business Intelligence

BI is the process of providing for better
decisions within the corporate or business area by utilizing processes, people
and related data tools and methodologies. It provides both historical and
current views of a company’s performance by utilizing data found in relational
databases, warehouses and data marts to organize historical and current
information. It differs from CPM in that it is not driven primarily by a
structured order of analysis against particular targets but allows the
companies analysts to produce reports that inform the organization of tactical
trending and opportunities. Recent developments have allowed for greater
flexibilities in the data sources integrated into the BI process and this along
with access to real time or near real time operation data in Point of Sale and
Customer Relationship Management systems has led to the provision of
‘Operational BI’.


Solving business problems utilizing
statistics along with computer technology has long been known as ‘Analysis or
Analytics’. Originally undertaken by mathematicians utilizing formulas against
company records without the assistance of computers or software these functions
are now carried out by algorithms being run using data mining against large databases
to extract a useful group of properties from the available data. This data is
then utilized with predictive and trend models to allow the company to form a future
strategy. It has however always been reliant on the quality of the data that
the algorithms have to work with.


In my experience of delivering leading
edge BI systems to corporate environments it can be seen that most organizations
have already moved their focus from the traditional methodology of utilizing
CPM to guide their corporate decision to some form of basic provision of BI
against either near real or real time operational figures to provide current
insight into current performance.

One of my most recent projects was to
enable the service quality team of a major retail bank to assist their branch
managers to show where benefits could be gained utilizing feedback provided
within customer phone questionnaire results, utilizing a base OLAP database
based on the month by month results of telephone surveys along with telephone
and accounts data. Utilizing this base data a predictive algorithm based on
survey results versus account closures over time periods that could be amended
by the user allowed the central team to predict how customer satisfaction would
drive account retention and overall profit trend at a branch level. This data
was then embedded into results dashboards, which were presented at all levels
of the company. This data led to a dramatic change in the companies’ service
quality rating within the industry, which conversely led to an increase in
account take up and therefore profit.

This shift of emphasis from management
of past performance and setting of future targets over a period of a year or
more to the fast-paced reactionary management currently seen has helped to
drive the requirements of the current BI Stack of applications in the market
place. The ability of many of the major BI Vendors (Cognos, SAP, Microstrategy,
Targit, Business Objects, IBM) to be able to set KPIs and provide a balanced
scorecard in the form of dashboards based on near real or real time operational
data has seen a merge of the traditional methods of CPM into operational BI as
business leaders realize the benefits of the ability of these new methods.

Most Business Analysts (BAs) and
managers that I speak to ask about the possibility of employing some form of
analytics within their chosen solution to provide a view of historical data and
trending results to enable them to predict future business performance and the
changes to the customer/business models. Understanding your clients’
requirements concerning their customers likes and dislikes and their
propensities to either view a particular web site or buy a particular product
can be used to provide better service and product offerings and therefore
should be discussed at an early stage of the BI implementation. SAS have
started to promote their analytics tools as part of their overall BI solution
and this along with offerings from other vendors is driving the requirement for
analytics to be incorporated in most corporations BI planning for the future.

Until recently, I have built and
installed algorithms into bespoke solutions that I have rolled out to give the
company a similar functionality to that as Clementine however, this has
meant that the BI solution has been more complex than some on offer in the
market but it has given the company what they wanted. However writing and
implementing complex SQL Stored procedures to provide mathematical analysis of
current data is at best time consuming and also not best practice as it means
that the customer will require support to change the solution to look at
different facts and dimensions within the algorithm if required. With SAP’s new
analytical tools using Clementine as its baseline and IBM purchasing outright
the company (SPSS) it is obvious that it will not be long before most, if not
all, BI applications will require at least a base model for predictive analysis
to compete in the market. This can be seen with Tibco’s delivery of their
‘Spotfire’ application
, which I had the chance to evaluate for a customer
last week. This application is more than either a BI tool or a pure Analytics
platform and performs the task exceptionally well. In testing, it allowed for
the BAs in the company to ask questions of the data formed in his own language
while allowing the results to be delivered quickly and efficiently to
dashboards and reports while allowing the business managers to look at
historical and charted data with ease.


I believe that the question asked at the
beginning of the article in fact has already being answered by the industries
move to a range of fully featured analytical BI Suites that provide not just
the ability to chart and report on historical data but also the full range of
predictive analysis that is required to support the modern day business


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Peter Evans

Peter Evans
Peter Evans
Peter Evans, a Business Intelligence and Data Warehousing Expert, Targit Certified Professional and industry recognized independent consultant specializing in delivery of applications utilizing primarily but not exclusively Microsoft technologies and in delivery of solutions to non standard cases. He enjoys explaining the methods e has employed in over sixteen years industry experience including work for major corporation and government clients.

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