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B Qu Transforming Data Into Competitive Advantage
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B Qu Transforming Data Into Competitive Advantage

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  • 1. Page 1
    Transforming Data into Competitive Advantage Easily and (relatively) Cheaply
    A Presentation by
    Jacques de Cock Anton Dominique
    CEO BQu Director LSM
    [Date]
    1
  • 2. Page 2
    Agenda
    Introduction
    Clear Information is Key
    Problems with Traditional Market Research and Data Management
    Changes in Information and Environment making Change in Management Inevitable
    Good Market(ing) Information Practice
    Benefits
    Key Success Factors
  • 3. Good Information and Clear Analysis – the starting point for Strategic Thinking
    • Strategic Thinking is structuring and distilling from the maelstrom of information and opinion what the key facts and actions are that will create Competitive Advantage
    • 4. Identifying key trends
    • 5. Creating simple causal models
    • 6. Understanding positioning and key strengths
    • 7. Analysis is the critical starting point of Strategic Thinking.
    • 8. Clear analysis key to evaluating situation
    • 9. Dynamic analysis more valuable than single point analysis
    • 10. Opinion cheap, factual analysis creates possible consensus
    • 11. Good Analysis is only possible with Good Data
    • 12. All information valuable, need to understand it
    • 13. Analysis based on misleading data creates misleading information and wrong decision
    • 14. All information has weaknesses, need to understand them to make sure the conclusion still stands
  • Page 4
    Five attitudes stand in the way of good usage of Data and Clear Analysis in management
    • Better blame someone else
    • 15. Fear of doing own analysis
    • 16. Argue about data rather than action
    • 17. Lack of time, so need to use what already exists
    Only rely on published information
    • Data gathering and analysis is a learned skill, not innat!
    • 18. Data is plentiful but takes time to understand and analyse
    • 19. Good research requires knowledge of sources
    • 20. Everyone finds different data that conflicts
    The myth of the easily available data
    • Want to cover all options
    • 21. Use all possible known analytical models
    • 22. Result is confusion and lack of clear understanding and direction
    “Kitchen Sink” approach
    • Everyone has their own data
    • 23. Not sharing it is keeping power
    • 24. Only analysed and distilled information is communicated
    • 25. Gut Feel is used unchecked, except maybe in brainstorms
    • 26. Only facts that confirm views are used and acknowledged as right
    • 27. If a fact challenges the perceived view the facts are challenged
    Information is Power
    Mistaking Hypothesis for Fact
  • 28. Page 5
    Changes in the Information Environment make changes in management vital
    • There is no need to be selective anymore
    • 29. The whole of the detailed operational data of the company can be stored on the cheapest computer
    Data Storage is virtually free
    • Primary data is available and often free
    • 30. Often ill defined and difficult to find
    • 31. Incomplete and confusing
    Base data available and plentiful
    • Gathering own primary data easy and cheap
    • 32. Market research and customer satisfaction easy to gather
    • 33. Every event creates valuable information
    Gathering Own Data Much Easier
    • Multiple sources prevalent
    • 34. Definitions different
    • 35. Confusion created and maintained
    Data Confusion Prevalent
  • 36. Page 6
    Good Practice can be summarized in three elements
    Focus
    • Focus analysis on the information needed rather than available
    • 37. Ensure all analysis supports decision making requirements
    • 38. Consolidate primary
    • 39. Both internal and external
    • 40. Make available to allno complex software
    • 41. No gate keepers
    • 42. No specialist to access data, maybe specialist to ensure data is easy to access and analyse
    Easy to Access Raw Data
    • All data clearly explained
    • 43. Limitation explicit
    • 44. Inferred data differentiated from primary data
    • 45. Sources explicit
    • 46. Shared experiences
    Clear Data Validation
  • 47. Information gathering should focus on the information that is uniquely valuable to you
    Market
    Company
    Company
    Market
    • Segmentation and penetration
    • 48. Financial Performance
    • 49. Product/Customer Contribution
    • 50. Profit drivers metrics and trends
    • 51. Margins and trends
    • 52. Causal Drivers of Good & Bad performance
    • 53. Employee satisfaction
    • 54. Marketing events and metrics
    • 55. Overall Trends
    • 56. Profitability
    • 57. Distribution Channels
    • 58. Technology Trends
    • 59. Regulatory Trends
    • 60. Constraints
    • 61. Competing Markets
    • 62. Overall Economic Conditions
    Clients
    Competitors
    Clients
    Competitors
  • Company data shared enables all parts of the company to gain better insights
    Market
    Company
    Example: Operational Data Helps Marketing
    Airline Regional Marketing Planning Support
    Making Information Accessible
    Regional Distribution
    Clients
    Competitors
    Enables Tailored Insights
    Down to Local Authority
  • 77. Having a simple causal model creates a simple way of providing insightful information
    Market
    Company
    Example: Causal Demand Model
    - Market Build Up Method CD Demand -
    Total Population
    61m
    CD Buyers
    10m
    CD Bought
    6 per Year
    Avg Price
    £15
    Market
    £900m
    Clients
    Competitors
  • Competitor Information needs to be captured when discovered and made easy to share
    Market
    Company
    Example: Competitor Database
    - Education Market; Database focussing on Key Information-
    Courses
    Articles/ Documents
    About
    Provides
    About
    Institution
    Gives
    About
    At
    Clients
    Competitors
    Works at
    About/By
    Has
    Lecturer/ Staff
    Locations
  • 90. Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Objective
    • Reason for analysis
    • 91. Problem to be solved
  • Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Issues
    Objective
    • Reason for analysis
    • 92. Problem to be solved
    • 93. Key Questions
    • 94. What we need to know the answer to
  • Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Hypothesis
    Issues
    Objective
    • Key Questions
    • 95. What we need to know the answer to
    • 96. Reason for analysis
    • 97. Problem to be solved
    • 98. What we think the answer is
    • 99. Our Gut Feel
  • Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Analysis
    Hypothesis
    Issues
    Objective
  • Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Findings
    Hypothesis
    Issues
    Objective
    Analysis
    • What we think the answer is
    • 108. Our Gut Feel
    • 109. Key Questions
    • 110. What we need to know the answer to
    • 111. Reason for analysis
    • 112. Problem to be solved
    • 113. The answers
    • 114. The facts
    • 115. Validation of our Gut Feel
    • 116. The distillation of the Analysis
    • 117. Only the information that is required
  • Focus Your Analysis on what is required to make The Decision
    The Scientific Model
    Objective – Issue – Hypothesis- Analysis – Findings – Conclusions
    Conclusions
    Findings
    Hypothesis
    Issues
    Objective
    Analysis
    • The distillation of the Analysis
    • 118. Only the information that is required
    • 119. What we think the answer is
    • 120. Our Gut Feel
    • 121. Key Questions
    • 122. What we need to know the answer to
    • 123. Reason for analysis
    • 124. Problem to be solved
    • 125. The answers
    • 126. The facts
    • 127. Validation of our Gut Feel
    • 128. The solution to the objective
    • 129. The answers to the issues
  • Using this model enables to quickly reach decisions based on factual information
    Example: Plan Validation
    - Army Recruitment Objective-
    Conclusions
    Objective
    Issues
    Analysis/Findings
    No
    Potential only 18,000
    Even though good conversion track record
    How big is this segment?
    1.2 million and declining
    How many are suitably qualified?
    Can we recruit 20,000 16-19 year olds this year?
    Only 50%
    How many are interested?
    Very few at 5%
    Review plan, in particular marketing to increase interest
    How many do we think we can convert?
    Very Successful at 60%
  • 130. Make Sure Everyone Shares the Same Data
    • Single Database (not in the computing sense)
    • 131. All in one place (virtual)
    • 132. All accessible by all (within reason)
    Create Central Repository
    • Do not force people to re-invest the wheel
    • 133. The sources of information are specific to you
    • 134. Also enable discussion of the sources to that the knowledge can be enriched
    Share Sources
    • Use simple web project tools or off the shelf Wiki to start if really no time
    • 135. Do not go for complex IT driven and highly developed process
    • 136. The purpose is not to lose information, insights and analysis
    Keep Tools Simple
    • More computer literate
    • 137. More time
    • 138. Good learning experience
    Use junior & admin staff
    Most Important : Make it part of Day to Day Operations
  • 139. Data needs to be clearly understood and validated in particular if shared
    • Period coverage
    • 140. Date of acquisition
    • 141. Source of data
    • 142. Coverage
    • 143. Sample or total population
    • 144. All date is valuable
    • 145. All data is possibly misleading
    • 146. The limitation and usability of data needs to be understood
    • 147. Any questions or doubts also need to be specified
    • 148. The fact that limitations and imperfections are known is good
    • 149. Most data has these and not knowing them is worse
    • 150. Show all data as all could be useful at some time in some way
    Don’t Go for perfection
    Understand limitation
    Understand source
  • 151. Good & Effective Data Management is Simple but requires Discipline and Clarity of Thought and Objective
    • Determine your needs
    • 152. Fight the Internal Hurdles
    • 153. Have a Data Gathering – Management and Distribution Process
    • 154. Focus both Data Gathering on what is “uniquely” beneficial to you
    • 155. Focus each analysis to the Objective at hand
    Most Importantly
    • Keep it simple
    • 156. Make it part of day to day operations