The article gives a short introduction to Business Analytics. It also gives a few tips on what to focus on when starting its implementation.

1. 26
What is Business Analytics?
TEKST: ISABELLE VALETTE, Sherpa Consulting AS
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Business Analytics (BA) refers to the use of
analytics together with business proces-
ses. In “The Secrets of Analytical Leaders”,
Wayne Eckerson defines it well: “analytics
describes the people, processes, and
technologies that turn data into insights
that drive business decisions and
actions.” I would like to add that the people,
processes, and technologies need to cover
5 fields of expertise to turn data into insights:
1. Business process understanding
2. Business Intelligence
3. (Big) Data
4. Statistics
5. Security and data privacy
Analytics is a key tool to improve business
IQ. It can be applied to many areas: to
increase sales and sales process effec-
tiveness, to improve customer loyalty, to
develop new products, to optimize pricing
models, marketing mix, channel distribution,
you name it. This versatility is probably why,
when applied and implemented efficiently,
analytics has the power to give your compa-
ny a greater competitive edge and take your
sales and profitability to the next level. The
easiest way to get started with analytics is
to apply it to your marketing campaigns. The
example to the right is taken from the finan-
cial industry. It shows the successful impact
on sales of a Direct Marketing campaign
enhanced with analytics. The lowest bar is
a control group and refers to the “natural”
sales of a product X without marketing or
analytics used. The highest bar is the target
group and shows the power of combining
analytics with marketing.
What is required to get started
with analytics?
PEOPLE
Well, you need people. In the world of
analytics, this usually means an analyst. This
is the first prerequisite. What rules do you
need to follow when choosing an analyst for
analytics work?
Well, the analyst:
- should have superior knowledge of the
business they are creating insight for.
- should master quite a few statistical and
mathematical methods and techniques.
(I could never quite decide which of these
first two rules should come first.)
- must know the data used by the business
and where to find it.
- must know quite a bit about IT and es-
pecially programming, BA software, and IT
architecture.
- must have extensive knowledge about data
security and data privacy.
- should be structured, communicative,
creative, unafraid, efficient, hard-working,
smart, service-oriented, sharp, solution
focused, patient, and cooperative.
Analytics and Marketing impact on sales

2. 27
This very sexy job has recently found a fancy
tittle to go with it: data scientist.
BUSINESS
PROCESSES
There are 4 proces-
ses involved in
creating analytics: a
statistical process, a
data process, an IT
process, and a business process like sales,
marketing, or HR. The statistical process is a
must to produce analytics and often refers to
a statistical methodology. CRISP-DM (Cross
Industry Standard Process for Data Mining)
is a methodology often used by analysts to
create analytics. It focuses on the workflow
that extracts insight out of data using ap-
plied statistics (econometric modeling),
mathematics (multivariate analysis), and/or
machine learning (data mining).
When you start out with analytics, it is often
best to minimize the complexity around the
processes involved. The statistical process,
as we have seen, is a must. When it comes
to the other processes, try to simplify them
as much as possible to begin with. That’s
because, once the size of data and the
number of processes increase, analytics be-
comes more than just statistical techniques.
Why? Because this is when the require-
ments for analytics also increase in comple-
xity. If you have one task with little data, use
excel. If you have many tasks with a lot of
data, then you need the big analytical guns:
an IT architecture to handle massive data-
sets, analytics software to perform complex
analytical processes efficiently, and people
who know how to make all this stuff work.
IT or BUSINESS
INTELLIGENCE
ARCHITECTURE
Business intelli-
gence (BI) covers the
processes, methods,
and IT-solutions used
to provide fact based insight to the busi-
ness. BI is normally used in the company or
organization as a decision support system.
The platforms for BA and BI are intimately
connected with one another and the line
between the two can be vague. To simplify:
BI is like a magical hat, and the business
analyst is the magician pulling out the rabbit
(i.e. insights).
There are many ways to get started with
BA. I have seen amazing business insights
created with only the statistics tool’s func-
tionalities (Analysis ToolPak) in Excel, and
in the beginning, that might be sufficient to
produce insight. You can also download
Analytics software for free, such as RapidMi-
ner, R, or Python. If you are not very familiar
with various programming languages, I
would recommend RapidMiner; the user in-
terface is easiest to learn, the online tutorials
are sufficient, and the software runs on both
Windows and Mac OS X.
(BIG) DATA
Data refers to a
piece of information
or a fact. The terms
data, information,
and knowledge are
frequently used for
overlapping concepts. They represent,
however, different level of abstraction and
reasoning. Data is the lowest level, informa-
tion comes next, and finally, knowledge is
the highest level among all three. Predictions
are often seen as the highest form of know-
ledge creation. Predicting the future with
predictive analytics has been what one often
associates with analytics; however, it is only
one dimension of analytics. Descriptive and
prescriptive analytics can also create a lot of
value for your business.
“What is Big Data?” is a question that I get
asked a lot lately. In May 2011, the McKin-
sey Global Institute released a paper that
defines “Big Data” as “data sets whose size
is beyond the ability of typical software da-
tabase tools to capture, store, manage and
analyze.” Here we are not talking about gi-
gabytes, or a few terabytes, but hundreds or
even thousands of terabytes. In “Taming the
big data tidal wave”, Bill Franks digs into the
characteristics of this new source of data:
Volume, velocity, variety, and complexity.
IBM added another characteristic to “Big
Data”: Veracity. The volume of “Big Data” is
huge, no doubt about it. However, it is not
because it is big that it is insightful.
So, what is all the fuss around “Big Data”,
and how does it relate to analytics? “Big
Data” is nothing but a new source of data
(Twitter, Internet and phone logs, etc.) fed
into the analytical processes. Traditionally,
analytics started with building statistical
models on historical data. Past events, past
information, and past history were used to
predict the future. “Big Data” is a powerful
new source of information because it relates
very much to real time events and real time
action that, combined with all the history,
can become an explosively accurate and
informative input to create analytics. “Big
Data” can enhance the power of analytics. It
may also contribute to a change in the tradi-
tional way of looking at a customer; we may
see a swift change in the future: from valuing
the most loyal and/or the most profitable
customers to valuing the most loyal, profita-
ble, and/or the most influential customers.
Isabelle Valette
Head of Business Analytics
Sherpa Consulting
STATISTICS/
MATHEMATICS
TECHNIQUES
AND METHODS
The goal of analytics
is to create insights
generating true
business impact. Every field of research that
helps you achieve this is part of the analy-
tics methodology. The analyst must at all
times chose the tools that are best suited
and most appropriate to solve the busi-
ness problems at hands. John Chambers,
the eminent and distinguished statistician,
explains: “Greater Statistics can be defined
simply, if loosely, as everything connected
to learning from data, from the first plan-
ning or collection to the last presentation
and report.” In this aspect, many disciplines
will cross the path of “Greater Statistics” to
help extract insights from data: econometric
modeling, MapReduce algorithms, multi-
variate analysis, data mining, mathematical
or linguistic computation, SQL or Java
programming, geocoding, anthropology, etc.
The analyst would be wise to open his mind
to many fields to improve his/her insight
creation skills. Therefore debating the diffe-
rences between, and the respective value of,
different fields of insight creation is pointless,
since they have different role, purpose, and
benefits. The question anyone should ask is
“is this method or technique contributing to
the insight creation process?” If the answer
is yes, than this tool should be included in
the analytics methodology.
SECURITY &
DATA PRIVACY
The elephant in the
room is usually rela-
ted to privacy and se-
curity issues around
data usage. It is es-
pecially relevant with the emergence of “Big
Data” technology. Compliance challenges
are a great opportunity for resources from
different departments to come together,
brainstorm, and find consensus on how the
data is going to be used and not used. Once
consensus has been reached, one would be
advised to anchor the data usage guidelines
with top management to secure under-
standing, ownership, and approval on data
usage, privacy, and security.
To summarize, analytics is ultimately about
creating business value and having a busi-
ness impact.
I wish you all the best with your analytical
ambition.