Business Analytics                         Lesson of dayI would like to thank my parents, Google, Wikipedia, the Garter or...
Some parts of this presentation are not the product of my grey cells.I would like to thank the contributorsThat is why I a...
Data analysis has been used in business since the dawn of the industrial era ,from the time management exercises initiated...
A statistical application:VietCong bodycount during the Vietnam War.“”How many gooks did we kill today ? 150 Good work “”T...
Since then, analytics have evolved with the development of enterpriseresource planning (ERP) systems, data warehouses, and...
Phenomenal storage facilities are not the only technological frontier:statistical software, high-end 64-bit processors (gi...
Business analytics is how organizations gather and interpret data inorder to make better business decisions and to optimiz...
Analytics are defined as the extensive use of data, statistical and quantitativeanalysis, explanatory and predictive model...
Analytics may be used as input for human decisions.However, in business there are also examples of fully automated decisio...
Focus on developing five fundamentals
i. Management BOTTOM UP/TOP DOWN Committment  A broad analytical approach to business calls for big changes in  culture, p...
ii. A strong base of skills in use of data   It is FUNDAMENTAL to have a broad base of employees who are data-   savvy (kn...
iii.   Fact-driven business processes       Effective analytical competitors begin with “a single version of the       tru...
iv. Vision-driven business processes   Action time is not the same as fact driven.   You need to dream the future by telli...
v.   Technology to capture, sort, and make sense of data     Today the processing power to support an analytics thrust is ...
Five strategies that you MUST pursue if you want to get things done:    a. Technical excellence in yourself       Tomorrow...
Pause & think1.   Innovation, innovation, innovation2.   Solution, solution, solution.Don’t think in terms of Ligne Magino...
The fifth strategy (information effectiveness) involves using analytics andapplying information to how (business) decision...
Challenges of analyticsAnalytics is dependent on data.If there is no data, there can be no analytics.Claro ? Claro que si,...
Challenges of analyticsFor many organizations aspiring to be analytical competitors, theprimary problem is not that they l...
Challenges of analyticsUnlike so-called “mature” economies, in some of the fastest growingdeveloping economies, such as In...
A company that finds it has poor-quality data should postpone any plans tocompete on analytics and instead should fix its ...
Food for your thoughts                    Now THINKWhat are your questions , what are your needs, what is  you assessment,...
Good luck and now on with your work
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Business Analytics Lesson Of The Day August 2012

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Business Analytics Lesson Of The Day August 2012

  1. 1. Business Analytics Lesson of dayI would like to thank my parents, Google, Wikipedia, the Garter organization, common goodsense and above all the hard knocks & knuckles school I frequented in life which made megrow to understand that business is geniality. Thank you to the reviewersMr. 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. 2. Some parts of this presentation are not the product of my grey cells.I would like to thank the contributorsThat is why I am not copyrighting this presentation
  3. 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 themechanized assembly lines developed by Henry Ford (fordism).But it began to command more attention in the late 60s when computerswere used in experiments to aid decision-making.These decision support systems addressed repetitive and non-strategicactivities such as financial reporting.Analysis of statistics became routine in the 1970s with the arrival ofpackaged computer applications.But few embraced the strategic use of data; number-crunching was leftlargely to statisticians.
  4. 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. 5. Since then, analytics have evolved with the development of enterpriseresource planning (ERP) systems, data warehouses, and a wide variety ofother hardware and software tools and applications.But until recently, companies have focused on analyzing historical datarather than developing predictive analytics for decision-making.Many companies today are collecting and storing a mind-bogglingquantity of data by overcomputing !Too much ! Too much data I mean!In just a few years, the common terminology for data volumes has grownfrom megabytes to gigabytes to terabytes (TB) — a trillion bytes.Some corporate databases are even approaching one petabyte — aquadrillion bytes — in size.The 583 terabytes TB in Wal-Mart’s data warehouse, for example, is farmore than the digital capacity needed if all 17 million of the books in theU.S. Library of Congress were fully formatted.
  6. 6. Phenomenal storage facilities are not the only technological frontier:statistical software, high-end 64-bit processors (gig power of 64), andspecialty data appliances can quickly churn (eat and digest) throughenormous amounts of data— and do so with greater sophistication.
  7. 7. Business analytics is how organizations gather and interpret data inorder to make better business decisions and to optimize processes.
  8. 8. Analytics are defined as the extensive use of data, statistical and quantitativeanalysis, explanatory and predictive modeling, and fact-based decision-making.
  9. 9. Analytics may be used as input for human decisions.However, in business there are also examples of fully automated decisions thatrequire minimal human intervention. Context……….In businesses, analytics (alongside data access and reporting) represents asubset of Business Intelligence (BI).
  10. 10. Focus on developing five fundamentals
  11. 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. 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. 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. 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. 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. 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. 17. Pause & think1. Innovation, innovation, innovation2. Solution, solution, solution.Don’t think in terms of Ligne Maginot everywhere, be lucidlyoffensive 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. 18. The fifth strategy (information effectiveness) involves using analytics andapplying information to how (business) decisions are made, rather than howinformation 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. 19. Challenges of analyticsAnalytics 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 conductsurveys or a census to obtain data.In many cases to save expenses, organizations can look for data obtainedfrom situations that are similar but not quite meet the current requirements,and make minor modificationsHowever, in these cases, businesses should be aware of the risks inherent inusing data obtained in such manner.
  20. 20. Challenges of analyticsFor many organizations aspiring to be analytical competitors, theprimary 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 anderroneous data entries.According to a Gartner study, an significant proportion of all businessdata is inaccurate.Garner estimates that at least 25 percent of critical data withinFortune 1,000 companies will continue to be inaccuratethrough 2007.In a separate study only a little more than a third of executiveswere “very confident” in the quality of their corporate data.
  21. 21. Challenges of analyticsUnlike so-called “mature” economies, in some of the fastest growingdeveloping economies, such as India and China, analytics has to contendwith "noisy" data wherein the data is incomplete (e.g. credit rating of acustomer) or suspect (e.g. demographic information of a mobile telecomcustomer) or plain missing.A new generation of analytical algorithms that compensates for this"noise" appropriately helps in deployment of analytics solutions withoutthe need to rely on fixing data - something that may never be possible inthe near future.Such innovative algorithms are an example of technology innovations indeveloping markets that promise to leapfrog existing methods that havebeen developed primarily for mature markets and not easily transposablegiven constraints such as data sanctity.
  22. 22. A company that finds it has poor-quality data should postpone any plans tocompete on analytics and instead should fix its data first. GARBAGE IN GARBAGE OUTUPS demonstrates the patience that is often necessary.Although they had been collecting customer information for more than fiveyears, it took more than half that time to validate that data before it wasusable.Never expect, always INSPECT. I mean double, triple check until youare sure that the data is rock solid!
  23. 23. Food for your thoughts Now THINKWhat are your questions , what are your needs, what is you assessment, do you have solutions, are your solutions the right ones and verifiable?
  24. 24. Good luck and now on with your work

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