Business analytics involves collecting and analyzing large amounts of data to help companies make better business decisions. While data analysis has been used in business for over a century, it is only recently that companies have had the capabilities to analyze huge volumes of data in real-time and make predictive decisions. However, many companies still struggle with issues like poor quality data that can lead to inaccurate analyses. To successfully implement business analytics, companies need to focus on developing skills, ensuring accurate data, and having the right technologies to capture and make sense of their data.

1. Business Analytics
Lesson of day
I would like to thank my parents, Google, Wikipedia, the Garter organization, common good
sense and above all the hard knocks & knuckles school I frequented in life which made me
grow to understand that business is geniality.
Thank you to the reviewers
Mr. David Menga – R & D Research Engineer (specialist in man/machine interaction ) – EDF
Ing. Filippo Heilpern - Consultant in BD & International Corporate Executive
Mr.Brice Gibodeau – Network Director- UBISOFT
My son Lorenzo

2. Some parts of this presentation are not the product of my grey cells.
I would like to thank the contributors
That is why I am not copyrighting this presentation

3. Data analysis has been used in business since the dawn of the industrial era ,
from the time management exercises initiated by F. W.Taylor
(taylorism) in the late 19th century to the measured pacing of the
mechanized assembly lines developed by Henry Ford (fordism).
But it began to command more attention in the late 60s when computers
were used in experiments to aid decision-making.
These decision support systems addressed repetitive and non-strategic
activities such as financial reporting.
Analysis of statistics became routine in the 1970s with the arrival of
packaged computer applications.
But few embraced the strategic use of data; number-crunching was left
largely to statisticians.

4. A statistical application:
VietCong bodycount during the Vietnam War.
“”How many gooks did we kill today ?
150
Good work “”
The war was lost. The count was good !
Statisticians do not win wars.

5. Since then, analytics have evolved with the development of enterprise
resource planning (ERP) systems, data warehouses, and a wide variety of
other hardware and software tools and applications.
But until recently, companies have focused on analyzing historical data
rather than developing predictive analytics for decision-making.
Many companies today are collecting and storing a mind-boggling
quantity of data by overcomputing !
Too much ! Too much data I mean!
In just a few years, the common terminology for data volumes has grown
from megabytes to gigabytes to terabytes (TB) — a trillion bytes.
Some corporate databases are even approaching one petabyte — a
quadrillion bytes — in size.
The 583 terabytes TB in Wal-Mart’s data warehouse, for example, is far
more than the digital capacity needed if all 17 million of the books in the
U.S. Library of Congress were fully formatted.

6. Phenomenal storage facilities are not the only technological frontier:
statistical software, high-end 64-bit processors (gig power of 64), and
specialty data appliances can quickly churn (eat and digest) through
enormous amounts of data— and do so with greater sophistication.

7. Business analytics is how organizations gather and interpret data in
order to make better business decisions and to optimize processes.

8. Analytics are defined as the extensive use of data, statistical and quantitative
analysis, explanatory and predictive modeling, and fact-based decision-making.

9. Analytics may be used as input for human decisions.
However, in business there are also examples of fully automated decisions that
require minimal human intervention. Context……….
In businesses, analytics (alongside data access and reporting) represents a
subset of Business Intelligence (BI).

10. Focus on developing five fundamentals

11. i. Management BOTTOM UP/TOP DOWN Committment
A broad analytical approach to business calls for big changes in
culture, process, behavior and skills for all employees.
Bottom Up and Top Down.
Both approaches are necessary to get a clear view of the
challenge.
Get out of the comfort of having “mamma’s” company take
care of your business needs & demands !
Changes must be initiated and implemented by management who is
passionate about analytics and fact-based decision-making.
Lucidity is key. No ego interference !

12. ii. A strong base of skills in use of data
It is FUNDAMENTAL to have a broad base of employees who are data-
savvy (know and can use)— or who can quickly become data-savvy.
This DEMANDS training and rewarding staff possessing analytical
skills and bringing tangible results, at ALL management levels.
It underlines & highlights the need to understand where those skills
matter most and where they will matter most in the future.
The future is today !

13. iii. Fact-driven business processes
Effective analytical competitors begin with “a single version of the
truth
i.e.: There is only one thruth. Yours
Business is not a democracy in action !
There can be no conflicting views of the same metrics that stymie (slow
down) too many companies.
What’s DEMANDED is an integrated, cross-company view of the data
, a state that will require business process redesign on a large scale.

14. iv. Vision-driven business processes
Action time is not the same as fact driven.
You need to dream the future by telling yourself, as step one, how do
I go from A to B.
Define your strategy based on your own sense of understanding
of today’s context and needs, dynamics of context and also the
capability to influence the context.
Pragmatism and step by step process is key.
Stay grounded !
Have confidence in yourself ! Think out of the box.

15. v. Technology to capture, sort, and make sense of data
Today the processing power to support an analytics thrust is readily
available.
There is wider use of dedicated “business intelligence appliances” —
supercomputer — like machines that can quickly find and sort data in
large databases and analyses, as well as cloud computing {( access to
technology-enabled services from the Internet ("in the cloud")}
Much of the necessary analytical software is also available.
Master it ! Extend the range of your “senses” by factoring in
technological add-ons.
It won’t give you insight, it will give your tech power and access
to new sources, info and data.
Real-time BI, in which automated decisions are embedded in operational
business processes, is gaining ground…….

16. Five strategies that you MUST pursue if you want to get things done:
a. Technical excellence in yourself
Tomorrow you must be better than today
b. Clarity of decisions. Honesty with self. (don’t fall in love with your data).
c. Agility, use your brains, factoring of risk in the mental process.
Who tells me that that, is that ?
d. K.I.S.S.S
Keep it simple, sharp, short .
e. Information effective
Keep in mind that the business challenges of tomorrow are initially
fought with the means and tools of yesterday.
Your “yesterday” will determine & insure your solution stability/instability.

17. Pause & think
1. Innovation, innovation, innovation
2. Solution, solution, solution.
Don’t think in terms of Ligne Maginot everywhere, be lucidly
offensive and not scared.
De l‘Audace, encore de l‘Audace, toujours de l‘Audace !!
(as good-old-boy Danton said before they chopped his head off)

18. The fifth strategy (information effectiveness) involves using analytics and
applying information to how (business) decisions are made, rather than how
information is moved around a company’s computer systems
(I mean: no pen pushing! Do you know what effort it takes to push a pen ?)

19. Challenges of analytics
Analytics is dependent on data.
If there is no data, there can be no analytics.
Claro ? Claro que si, hombre !
However, if data is sparse or non-existent, an organization can conduct
surveys or a census to obtain data.
In many cases to save expenses, organizations can look for data obtained
from situations that are similar but not quite meet the current requirements,
and make minor modifications
However, in these cases, businesses should be aware of the risks inherent in
using data obtained in such manner.

20. Challenges of analytics
For many organizations aspiring to be analytical competitors, the
primary problem is not that they lack data.
It is that they must contend with dirty, inaccurate and toxic data.
The Iraqui WMD case is selfexplanatory.
The challenge is that they do not know which data is trustworthy —
clean — and which contains duplicates, outdated records and
erroneous data entries.
According to a Gartner study, an significant proportion of all business
data is inaccurate.
Garner estimates that at least 25 percent of critical data within
Fortune 1,000 companies will continue to be inaccurate
through 2007.
In a separate study only a little more than a third of executives
were “very confident” in the quality of their corporate data.

21. Challenges of analytics
Unlike so-called “mature” economies, in some of the fastest growing
developing economies, such as India and China, analytics has to contend
with "noisy" data wherein the data is incomplete (e.g. credit rating of a
customer) or suspect (e.g. demographic information of a mobile telecom
customer) or plain missing.
A new generation of analytical algorithms that compensates for this
"noise" appropriately helps in deployment of analytics solutions without
the need to rely on fixing data - something that may never be possible in
the near future.
Such innovative algorithms are an example of technology innovations in
developing markets that promise to leapfrog existing methods that have
been developed primarily for mature markets and not easily transposable
given constraints such as data sanctity.

22. A company that finds it has poor-quality data should postpone any plans to
compete on analytics and instead should fix its data first.
GARBAGE IN GARBAGE OUT
UPS demonstrates the patience that is often necessary.
Although they had been collecting customer information for more than five
years, it took more than half that time to validate that data before it was
usable.
Never expect, always INSPECT. I mean double, triple check until you
are sure that the data is rock solid!

23. Food for your thoughts
Now THINK
What are your questions , what are your needs, what is
you assessment, do you have solutions, are your
solutions the right ones and verifiable?

24. Good luck and now on with your work