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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
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
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.
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.
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.
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.
Business analytics is how organizations gather and interpret data in
order to make better business decisions and to optimize processes.
Analytics are defined as the extensive use of data, statistical and quantitative
analysis, explanatory and predictive modeling, and fact-based decision-making.
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).
Focus on developing five fundamentals
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 !
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 !
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.
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.
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…….
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.
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)
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 ?)
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.
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.
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.
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!
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?
Good luck and now on with your work

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Business Analytics Lesson Of The Day August 2012

  • 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