OK - I admit it: when I see a pie-chart in business analytics, my blood pressure rises and, yes, I am apt to tell the analyst exactly what I think of the monstrous, hard to read, waste of space and ink they created.
I am most definitely NOT the first person to suggest that pie-charts are over used and close to useless. Google 'Pie Charts are Evil' and see for yourself. This is an area where career analysts tend to agree, yet, in practice, pie-charts are very, very heavily (ab)used. In the hope that I can influence even a handful of people to create fewer of these eyesores I'm adding my voice to the argument.
Business Analytics and Predictive Modeling for CPG/Retail
Analytic tools "so easy a 10 year-old can use it"
If
you search the web you'll find lots of analytic tools to support your business:
tools to help with forecasting, inventory optimization, risk analysis,
simulation for production lines and warehouses, production scheduling,
supply-chain network design, vehicle loading, price-sensitivity modeling and
planogram building - and that is very, very far from being an exhaustive list.
Some
of these tools are bought as a service that includes expertise to prepare your
data, do the modeling work for you and configure the system to meet your needs.
These tools will be much more expensive than the 'roll your own' variety and
the more frequently that expertise is required, the more you will pay.
The Primary Analytics Practitioner
The field of Business Analytics can be very complex. Top level
analysts are experts; just like medical specialists, they have undergone years
of additional training and know their area of specialty (perhaps price
sensitivity, multivariate statistical modeling, survey analysis or mathematical
optimization) backwards. Keeping with this analogy, most business managers are as well informed as
to what business analytics can do for them as a patient heading in to see their
primary physician; perhaps less so.
Bringing your analytical guns to bear on Big Data – in-database analytics
I've blogged before about the need to use the right tools to hold and
manipulate data as data quantity increases (Data Handling the Right Tool for the Job). But, I really want to get to some value-enhancing analytics and as data grows it becomes increasingly hard to
apply analytical tools.
Let’s assume that we have a few Terabytes of data and that it's sat in an industrial-strength
database (Oracle, SQL*Server, MySQL, DB2, …) - one that can handle the data
volume without choking. Each of these databases has its own dialect of the querying
language (SQL) and while you can do a lot of sophisticated data manipulation, even a simple analytical routine like calculating correlations is a chore.
Cluster Analysis - 101
The current Wikipedia page on Cluster Analysis, excerpted below, is correct, detailed and makes absolute sense. Then again, if you do not have a background in statistical modeling, I'm guessing these two paragraphs leave you no wiser.
Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters.
Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.
Wikipedia 4/2012
In this post I hope to provide a workable introduction for people that need to be educated consumers of cluster analysis.
Reporting is NOT Analytics
Reporting is about what happened; Analytics is about answering "what if" and "what's best" questions. Most of the materials that land on
a VP/Director’s desk (or inbox) are examples of reporting with no analytical
value added.
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