T Ho M.Mindset Analytics.200901.Slideshare.V2

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Using data intelligently has already been around for decades …
So are the related issues…
The analytics market is offering an abundance of solutions to gear up your data abilities…
And customers/companies are increasingly more willing to deploy data in some way…
But are they really ready to turn their minds to data ?
This presentation provides the view of The House of Marketing on how companies should gradually structure their analytical perspectives and pace the required transformation in the organization.

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T Ho M.Mindset Analytics.200901.Slideshare.V2

  1. 1. The Mind-set Prior to Analytics (Perspective of The House of Marketing) January, 2009
  2. 2. – Draft – The House of Marketing (THoM) Customer orientation present in all four functional areas we specialize in Functional expertise Corporate and business unit strategy Growth and innovation strategy Strategy Business plan, modeling and scenario building Market and competitive analysis Marketing strategy and marketing plan Customer insights and intelligence Customer segmentation and value proposition Marketing Sales and channel management Customer relationship management Branding and pricing Product management and new product development Organization design and development of new organizations Program management and change management Organization Core process mapping and developing reference process models Communication strategy and plan Communication Marketing (communication) efficiency and effectiveness 2 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  3. 3. – Draft – Objective of today’s presentation Using data intelligently has already been around for decades … So are the related issues… The analytics market is offering an abundance of solutions to gear up your data abilities… And customers/companies are increasingly more willing to deploy data in some way… But are they really ready to turn their minds to data ? This presentation provides the view of The House of Marketing on how companies should gradually structure their analytical perspectives and pace the required transformation in the organization. 3 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  4. 4. – Draft – Agenda 1. Kondratieff’s case 2. The Kondra-stages 3. The Kondra-quences 4 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  5. 5. – Draft – Kondratieff Macro-economic analytics ‘avant la lettre’ Professor Nikolai D. Kondratieff – Social Sciences • Publishing in 1926 (that is 3 years prior to the ‘crash of 1929’) • Analyzing the capitalistic economy • “Die langen Well der Konjunktur”, Archiv für Sozialwissenschaft und Sozialpolitik vol.56, no.3, pp.573-609 Renown for building a macro-economic theory, amongst many … Still subject to discussion today: Believers vs. Non-Believers, despite • Scientific study • Data proven study (descriptive) • Historically proven (post-study) We’ll focus on the original way of working of Kondratieff in 1926 We’ll focus on the original way of working of Kondratieff in 1926 5 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  6. 6. – Draft – 6 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  7. 7. – Draft – Ambitions at the start of Kondratieff’s study Initial objectives of the study: • Unravel or structure the complexity of capitalist dynamics (i.e. find the main driving factors) • Analyze for trends or movements in the long term (i.e. economic shifts) - No prejudice on cyclical character 7 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  8. 8. – Draft – The analytical issues were no different than ours today The issues: Sounds familiar ? Comments Identifying what’s relevant (i.e. which Only interest rates and gold reserves as macro-economic factors to include) readily available macro factors Long/short observation time Maximally 140 years of data, eventually representing only 2,5 cycles Data availability Only some French and English data going back to 1800’s Data reliability Missing data (wars), different sources or collection methods within the same sample Data representativeness US proxying for England for some data; coal consumption proxying for industrial activity Normalization Divide by population data (if available) for comparison Mitigating short-term distortions Using new statistical techniques of 1919- 1920 Contextual issues Territory changes… 8 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  9. 9. – Draft – Results versus acceptance … • Revealing a likely high risk of depression (1929) • Cycles of 50 years (48-60yrs), on top of the known 7-11 yr Some results and 1,5yr cycles • Multiple options of concurring factors to trigger change Shedding light on relevant and less relevant factors • Cyclical character of mankind • The merits Denying linear behaviors • Gold production is not a determinant • … • • ‘He’s not so sure, is he?’ • ‘Cycles… really? Not a surprise’ • ‘We knew it was complex’ The comments • It falls short of clarifying the nature and types of the wave-like movements (cause vs. consequence) •… 9 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  10. 10. – Draft – What happened? Where did Kondratieff go wrong? Or did his audience? 10 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  11. 11. – Draft – Agenda 1. Kondratieff’s case 2. The Kondra-stages 3. The Kondra-quences 11 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  12. 12. – Draft – All analysts risk having a ‘Kondratieff experience’ In our experience discussing analytics with customers, The House of Marketing tackles the readiness and organizational understanding in phases … … as we often need to work around disconnects in • Language used • Perspective and interpretation • Mutual understanding between the analytical and the non-analytical individuals involved There is a lot more to prepare and align prior to any important analytical job, especially for an external provider 90% of a data mining job is data preparation … … but at least 50% of the whole job is building a common (data) mind-set of people 12 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  13. 13. – Draft – Today’s stakeholders’ mind-set process on analytics: Clearing out the clouds jointly Kondra-stage 1 Kondra-stage 2 Kondra-stage 3 1 2 3 Convinced that there is Understood that you’re Comfortable in not always (sufficient) data out there dealing with real life having the ‘why’ • Techniques • Ambiguity and • But there’s always more • Approaches probability than you know • Proxies • There’s no such thing • Hypotheses as exact sciences Comprehended that predictive is not descriptive Understood that data Perfection has generated • A world of difference needs to be ‘screened’ no results yet • It’s not just input • It’s about an And even descriptive is improvement vs. already more than reporting Comprehended that you today don’t have to wait for IT • You don’t need all Understood that your • IT awaits you data organization requires a multi-skilled (marketing) Pre-defined what you Understood your data and team want to DO in the end data processing • Application / • It’s never just data IT solutions eventually help implementation side • It’s never the right • Scaling data • Integrating • Automating 13 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  14. 14. – Draft – Discussing the mind-set stages helps the non-analytical Kondra-stage 1 Kondra-stage 2 Kondra-stage 3 1 2 3 Convinced that there is Understood that you’re Comfortable in not always (sufficient) data out there dealing with real life having the ‘why’ • Techniques • Ambiguity and • But there’s always more • Approaches probability than you know • Proxies • There’s no such thing • Hypotheses as exact sciences Comprehended that predictive is not descriptive Understood that data Perfection has no results • A world of difference needs to be ‘screened’ yet • It’s not just input • It’s about an And even descriptive is improvement vs. already more than reporting Comprehended that you today don’t have to wait for IT • You don’t need all Understood that your • IT awaits you data organization requires a multi-skilled (marketing) Pre-defined what you Understood your data and team want to DO in the end data processing • Application / • It’s never just data IT solutions eventually help implementation side • It’s never the right • Scaling data • Integrating • Automating Getting to data Getting to information Getting to data usage Getting started 14 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  15. 15. – Draft – Agenda 1. Kondratieff’s case 2. The Kondra-stages 3. The Kondra-quences 15 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  16. 16. – Draft – Analytics cannot (correctly) hold and prosper on its own Building common understanding Kondra-stage 1 Kondra-stage 2 Kondra-stage 3 1 2 3 of stakeholders Into the Getting to data Getting to Getting to data analytics Getting started information usage language Goal-based data Competitive Up to the Analytical management advantage managerial discovery (not DWH) creation side 16 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  17. 17. – Draft – Why your audience is everything: Professor Nickolai Kondratieff After the Russian Revolution of 1917, he helped develop the first Soviet Five-Year Plan, for which he analyzed factors that would stimulate Soviet economic growth. In 1926, Kondratieff published his findings. His report was viewed as a criticism of Stalin's stated intentions for the total collectivization of agriculture. Soon after, he was dismissed from his post as director of the Institute for the Study of Business Activity in 1928. He was arrested in 1930 and sentenced to the Russian Gulag (prison). His sentence was reviewed in 1938, and he received the death penalty, which was probably carried out that same year. Kondratieff's theories documented in the 1920's were validated with the depression less than 10 years later. Source: www.kondratieffwinter.com 17 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  18. 18. – Draft – The end Probably Kondratieff's greatest contribution to the science of investment is not his observation the world economy operates in long cycles Cycles would suggest a repetitive nature to events. While the underlying economic conditions will repeat over time due just to the physical nature of our world, our reactions will always be tempered by knowledge and experience. The history of man has been one long climb higher. Kondratieff recognized progress as the irreversible trend Imposed upon our progressive nature are the physical limits of life. It is the interaction of these physical limits with our dreams and aspirations that creates the constant push pull of the economy known as the Long Wave Source: www.kondratieffwinter.com 18 THoM.Mindset Analytics.200901.Slideshare.v2.ppt
  19. 19. – Draft – For more information on marketing analytics, contact Stijn Ghekiere Stijn.ghekiere@thom.eu +32 (0)474 94 60 15 The House of Marketing www.thom.eu Subscribe to our monthly Marketing Buzz newsletter on www.mingle.be (register) 19 THoM.Mindset Analytics.200901.Slideshare.v2.ppt

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