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At JTA we have significant experience developing Business Intelligence capabilities that unlock information.


Every enterprise is unique. At JTA we it must define, capture and report on information in a way that is tailored to its needs.


We help our clients understand which solutions and technologies best prepare them for the information agility they will need to meet future business challenges

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What do we mean by Online Analytical Processing (“OLAP”)?

Tags: by: Jonathan Tooley

Online Analytical Processing (“OLAP”) is a set of powerful technologies which can help businesses to understand data.  I would like to describe here in very simple terms some of the things OLAP does.

Holding Data

Typically an OLAP system will import data from underlying sources of information such as a traditional database.  These traditional databases can, in turn, import or connect to a large number of legacy systems or even simple sources such as spreadsheets.  An OLAP system will have a very efficient way of storing the data that it uses to ensure that costs are minimised and that performance is maximised.  These systems are cheap to run and allow data to be retrieved very quickly.

As part of the way they hold data OLAP systems will often store a cache of pre-calculated queries that allows them to respond very quickly to problems posed in the business.  They can even analyse the types of queries that the business is asking and adjust the cache of pre-calculated results to include questions that are more likely to come up.

OLAP storage systems also have security functions built in to allow for very sophisticated security rules to be put in place for sensitive data.

Exposing Business Relationships – Dimensions

A business is a highly dynamic entity with many different parts.  Obviously any technology that seeks to understand a business must also consider all of these moving parts.  Traditional reports used to focus on just one aspect of a business; they may have reported that revenue has increased but would typically not be able to say what effect that had on other linked aspects such as profitability or delivery lead times.  This was the one-dimensional thinking of yesterday.

OLAP databases are often called cubes because the have many facets or dimensions.  It is typical for an OLAP cube to consider relationships between a series of dimensions.  Typical dimensions include customers, products, dates and times, geographical location, sales etc.  The relationships are formed because a typical transaction will invoke many links in these dimensions.  A customer will purchase products at a particular location and time.  The delivery may occur at different locations and times and the payment at other locations and times.  This kind of multidimensional analysis really focuses questions around the Who, What, Where and When of doing business.

  • Who could be a good customer or supplier?
  • What might be a good area for us to target a campaign to increase sales?
  • When is a good time to place replacement orders from our suppliers given likely demand and seasonality?
  • Where are we not performing as well as we should?  Which sales areas are winners and which are losers?


Along with the idea of dimensions comes the concept of hierarchies.  A hierarchy is often used in many parts of business and the OLAP technology has ways to store and analyse these.  Essentially a hierarchy is where a whole is split into many parts which may also split into many part themselves.  It is common to view geographical data in a hierarchy.  A company may split its activities into large regions such as Central Europe and Asia.  Each region will have subsidiaries in the countries of that region and each country will further split into operating areas such as states, cities, towns and so forth.


OLAP cubes can perform calculations using all of the data stored in the cube.  These calculations are powerful because they will link many data sources together.  Here are some typical examples:

  • The production of growth rates from raw data.  The system can easily provide month-on-month, quarter-on-quarter, year-on-year or trailing 12 month averages.  OLAP cubes can easily generate compound annual growth rates too.
  • Consolidations of detailed country data into regional, area and world totals.
  • Production of business ratios linking completely different data sources such as sales volume per number of Salesforce heads.

Cubes can also allow for ad-hoc queries to be formulated by users.  An analyst in a supermarket may wish to correlate sales of ice cream with rises in temperature to understand if there is a pattern that can be deciphered.  The great advantage of understanding these patterns is that it allows business to be very proactive and not reactive.


Our OLAP system holds all of our data together in a way that we can see all the relationships between the dimensions.  It also allows us to drill in to the data to gain insight;  we can see what is happening right now and also compare to past history;  we can drill down to see why things are happening by identifying problem (or good) products, customers, locations; we can use forecasting, trends, seasonality and other inputs to tell us what is likely to happen in the future.  Data can be explored from any viewpoint and this will allow users to really understand what is driving the business.

Competitor Intelligence Case Study

Tags: by: Jonathan Tooley

The Brief

Our client wanted to include detailed discussions on competitor growth and strategy during their business planning cycle.  They had already started to do this some years previously by buying expensive competitive intelligence.  The issue was the value gained in the discussions did not justify the cost of getting the research and there was no overall framework that all subsidiaries followed.  We proposed a new mechanism with simpler research and a data warehouse and reporting tool.

Competitive Intelligence Research

The research was derived from SEC Filings usually the form 10-K submitted each year.  The research covered 21 large corporations from the Software industry and involved selecting as much detailed information as was available in the filing and loading it into a purpose built database. Starting from the form 10-K allowed to have great confidence in the initial values however these numbers were often not sufficiently granular to allow for meaningful debate in the organisation.  Because of this we developed models to generate much more granular data with the constraint that it always consolidated to give the same values as the 10-K.

Boosting quality and user ‘buy-in’

By adopting a modelled approach we introduced some inaccuracy in the data and the users were hesitant to trust the values from the model.  Both of these issues were addressed by our development of an on-line feedback tool.  The tool allows users to comment on all of the detailed data and to suggest alterations and submit locally sourced research papers.  Users are encouraged to attach documentation to justify their feedback.

The tool allows administrators to review the suggested changes and to accept or reject the suggestions from the field.  Users receive automated e-mail responses from the system to confirm that their changes were accepted.

The data warehouse and on-line reports

The resulting database automatically handled conversions between the various reporting periods of the competitors and provided consolidations of data into the reporting structure of our client. The database also calculated Quarter-on-Quarter and Year-on-Year growth values and revenue mixes.  The database fed a series of on-line reports built to bring many advantages to our clients:

  1. Users can see competitive research for many companies in one place: In this case an easily accessed portal.
  2. Values can be more easily compared because they are all rebased to calendar years instead of the different Fiscal Years shown on the original Form 10-Ks.
  3. The on-line reports can be printed or used in internal presentations.
  4. The data warehouse can be used to build complex models to estimate more detailed data from the original filings.


The production of the on-line tool, along with a management process to gather input from the field has greatly improved the reliability of the model, allowed us to receive local insight for low cost and encourages users to buy-in to the data.

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