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121211 depfac ulb_master_presentation_v5_1


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This presentation makes the link between a concept developed in the Harvard Business Review around data-driven decision making and real Deployments Factory achievements. It's focused on Project Management but is also valid for other management domains.
It was presented on the 11th of december 2012 at a ULB master in Management course whose teacher is Antonio Nieto Rodriguez.

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121211 depfac ulb_master_presentation_v5_1

  1. 1. Data-driven (Project) ManagementFrom a theoretical data management revolution to real business solutions Presentation to ULB Master in management Antonio Nieto Rodriguez V5.1. Thibaut De Vylder, CEO 12th of December 2012
  2. 2. IntroDeployments Factory SA Thibaut De Vylder Created in Sept 2000  Commercial Engineer ‘96 25 consultants active in Louvain School of Benelux Management Turnover 3.200.000 € in  Co-founder in 2000 2010/2011  Current CEO Active in  PMP, « Administrateur  Financial, Dredging, Parking, agréé » Guberna Retail industries  Belgian & European public institutions
  3. 3. Objectives Understand current management challenges & opportunities linked to modern data management Underline the lack of “information virtuous cycle” in most organisations Understand the “DataFactory” concept Present some real applications in project, program & portofolio management. 3
  4. 4. Agenda Part 1 - Management revolution: Data Driven Decision Making Part 2 - From data to decision : the Information Virtuous Cycle Part 3 – Real & Future Applications Conclusion
  5. 5. Performance?  Recent topic in HBR about "Bigdata : The Management Revolution" BigData: the management revolution, Andrew mcAfee & Erik Brynjolfsson, Harvard Business Review, Oct 2012, pp 61-68  Performance of data-driven companies  First study about 330 executives from North American companies  executed by McKinsey, MIT Center for Digital Business, Warton... Results  Data driven companies perform better on operational and financial objectives  Companies in the top 1/3 of their industry, considering themselves as ‘data-driven’, were, on average, 5% more productive and 6% more profitable 5
  6. 6. VVV & Challenges Whats New? Three key differences with business analytics (VVV)  Volume  Velocity  Variety 2 examples Source  Amazon vs. Traditional library  Sears Hadoop solution to reduce a promotion process from 8 weeks to less than one. Challenges  Technical Challenges  From ‘90 BI infrastructure (created before Internet) to Bigdata Tools  From ‘Kendall’ & dimensional analysis to Bigdata Techniques  Management Challenges  Mute “hippo” (highest-paid persons opinion) decising making that rely on experience and intuition using scarce and incomplete information  into question raisers ‘Computers are useless, they can only give you answers‘, Pablo Picasso 6
  7. 7. Areas impacted & conclusion 5 areas for change management  Leadership : new type of leaders  Talent Management : scarcity of data scientists  Technology  Data-Driven Decision Making (DDDM) shall replace HiPPO style decision making  Company Culture Source :  From What do we think? : hippo style intuitive decisions data-scientist/  To What do we know? decisions based on evidenceConclusion  Data-driven decisions tend to be better decision  Existing decision making processes will mute  Leaders will either embrace this or be replaced by others who do  ‘Data Science’ will become a key strategic resource for future competitive advantage  Companies that figure out how to handle domain expertise and data science will have competitive advantage on their peers 7
  8. 8. Agenda Part 1 - Management revolution: Data Driven Decision Making Part 2 - From data to decision : the Information Virtuous Cycle Part 3 – Real & Future Applications Conclusion
  9. 9. Organisations experience problems and issues rogram Organisation Project Management Process Governance Maturity issues problems Specific Architecture Management Staff Business Intelligence projects Reporting Issues 9
  10. 10. that they try to solve… rogram Organisation Hire/Train PM New Structure Hire experts Implement EPM tools New Organisation Implement BPM solutions Hire Senior Mgmt Implement ERP solutions Buy Analyse Launch Reporting Reporting BI Initiative tools needs 10
  11. 11. But most of the time, our clients observe that…  little or no synergies & effective collaboration impossible.  Many existing tools…  … with functional overlapping  quality issues everywhere.  Little time is spent in analysing.  People are looking for information anyway.  Improving requires much human and financial resources.  In yours? 11
  12. 12. What do they want?  Make better decisions 12
  13. 13. What does make better decision mean?  Through data-driven decision making processes  Fed by reliable, high-quality, fresh, qualified & complete information  Information that fits to the users’ specific needs & produced by a reliable, qualitative, auditable, fast information system that generates trustful & comparable info on a periodic manner  Based on real data coming from a variety of sources coming from …  Inside the organisation  From structured sources such as operational systems (accounting, ERP’s, EPM’s, Budgets, Referentials…)  And/or from semi-structured sources (Excel)  And/or from unstructured sources (Text documents, mails…)  Outside the organisation (such as benchmarks, social networks…)  Sources delivered by acknowledged teams that receive DQ feedback to improve their quality on a recurrent manner 13
  14. 14. Consider the information virtuous cycle rogram Other Organisation Governance sources Decisions impact the organisation Organisations Input is available to generate data make data-driven (referentials, decisions (faster, better progress, budgets, and more reliable) orders, invoices, decisions forecasts, meteo...) Data is controlled & transformed into intelligent Information (KPIs, trends...) Data InformationOrganisations use other data to complete theirs 16
  15. 15. 3 possible levers for improvement Driving actions through existing management rogram Othersources Organisation 4 Governance Restitution of right info, at the 1 Capture of data right time & in the right format 3 Transformation of data into info Data 2 Information Focus of DepFac intervention 17
  16. 16. A single DataFactory solution« Extractors » used as a selective tool Transformation of data into enriched Management reports and dashboards that only focus on key data sourced information not available as such in the with a few charts, some metrics and rogramfrom multiple systems & referentials orignal data sources drilldown capacity Program Governance 1 2 3 Transformation 1 3 “systems Rep. “actionable produce data, information isnot information” the key” Dash. Data 2 Information Using historical data to analyze trends & Distribution process to feed the right DQ issues identification and direct make decisions that affect the future success governance bodies with the right info feedback to the source owners of the organisation at the right moment 18
  17. 17. Agenda Part 1 - Management revolution: Data Driven Decision Making Part 2 - From data to decision : the Information Virtuous Cycle Part 3 – Real & Future Applications Conclusion
  19. 19. Application 1 : Enterprise PPPM (Project, Program, Portfolio management)Enterprise Program EPM & Project reporti Management tool ng ERP Accounting ERP reporti ng Budget & Plans Top Management PMO DATAFACTORY Portfolio XLS Managers Financial CSV Management Program & 35 different sources connected to fulfill all Project Management user needs @ IT PMO BNPP Fortis 21
  20. 20. Application 2 : Central Transformation Office Phase 3 • 400 Programmes Phase 2 • 1600 Projects • 200 Workgroups Top Management Phase 1 • 40 taskforces Domain Governance CPMO DATAFACTORY Metier & Phase 0 Functions Governance • Merger decision Program & Project Governance 22
  21. 21. Data-drivenBUSINESS SOLUTIONS 23
  22. 22. Application 3: Financial Reporting Head-office Top Management FINANCIAL DATAFACTORY Financial Department Metier & Functions Program & Project 450 projects Dredging, Civil Works, Offshore and Environment Financial informations Tender Budget Actuals Forecast Project management informationsInternational projects Project operational informations 24
  23. 23. Application 4 : Risk & Basel 2 chain Entity 1 Risk & Basel 2 CHAIN Ref 1Entity 2 Ref 2Entity 3 B2 B2 B2 Input Storing Preparing Calculating Reporting… Ref M Entity N Top Management BASEL2 DATAFACTORY Regulators Risk Governance Stress Testing & Simulations 25
  24. 24. Application 5 : Corporate Reporting Global FactoringBelgium France Nederland Italy … England Sales Sales Sales Sales Sales Finance Finance Finance Finance Finance HR HR HR HR HR Risk Risk Risk Risk Risk Operations Operations Operations Operations Operations Top Management Risk Governance CORPORATE DATAFACTORY Finance Governance Strategic Governance 26
  25. 25. Data-drivenFUTURE SOLUTIONS 27
  26. 26. Application 6 : Strategic Execution Office (1/2) ‘Change’ and ‘Run’ always coexist in organisations Strategy deals with both dimensions & experience two types of gaps STRATEGY TOP Strategic change gap Management Strategic run gap Management Operations 28
  27. 27. Application 6 : Strategic Execution Office (2/2) Run Actions Strategic Actions Change actions STRATEGIC STRATEGIC DATAFACTORY MODULE StrategicSTRATEGY Governance CHANGE DATAFACTORY Change Governance RUN DATAFACTORY Run Governance STRATEGIC EXECUTION OFFICE 29
  28. 28. Agenda Part 1 - Management revolution: Data Driven Decision Making Part 2 - From data to decision : the Information Virtuous Cycle Part 3 – Real & Future Applications Conclusion
  29. 29. Conclusion (1/2) Every single organisation in the world has the impression to be very different from its peers. Surprisingly, however, when it comes to the resolution of its problems, issues or to the improvement of its efficiency, it tends to rely on generic solutions proposed (or pushed) by the market. Not surprisingly, the latest solution implemented has to adapt to pre- existing items (referentials…) and often increases both the perceived and the real complexity. Experience showed us that even if management commitment and allocated resources are important, the benefits are not always present at the end, which generates a lot of dissatisfaction at all levels. 31
  30. 30. Conclusion (2/2) We think that organisations should first focus on leveraging on past investments, on existing solutions and processes and try to make them work more efficiently together, pushing them to their limits. For this, considering the information cycle as a whole, and acting simultaneously on the 3 levers, is a first important step towards global understanding and pragmatic implementation of a data-driven decision making management culture. This can be done short term, with limited resources, in a non intrusive manner and drive a positive attitude that benefits to all stakeholders. If successful in a particular domain, it can be extended to other contexts, showing then its real potential as new management practice. 32
  31. 31. Remember… Replace the hippo style decision making in your organisation or someone else will… Periodic & reliable information allow you to watch informational ‘movies’ and analyse trends that are far better than static pictures. Unstructured data’s are knocking on the door. They want to be taken into account. No quality, no trust Focus on what people want to know and see. Do not listen to those who tell you that what you want is not possible: they just don’t know. Be curious! 33
  32. 32. Thank youThibaut De Vylder Deployments Factory SA Mobile : +32 478 69 21 86 @Thibaut73Deployments Factory SA Rue Guillaume Stocqstraat 79 1050 Brussels @depfac Tel : +32 2 290 63 90 Fax : +32 2 290 63 99 34
  33. 33. Appendix 35
  34. 34. Concept#01 : DataFactory Architecture (level 1) A. Capture C. Transform D. Restitute B. Store 36
  35. 35. Concept#01 : DataFactory Architecture (level 2) A. Capture C. Transform D. Restitute ERP’s, Simulations EPM’s…Proprietary Enrichments Solution Web & B. Store Analysis custom Structured tools Extractors Measures & KPI’s XLS, CSV, XML… Semi- Reporting Raw Reporting Distribution Structured data data Extractors - ReportsDocuments, Quality - Dashboards indicators - Triggers &emails, pdf… Unstructured & KQI’s exceptions Extractors - Other … Data Quality 37
  36. 36. Concept#02 : Unit BridgeUnit Bridge engineering & operations DF Management System TRANSVERSAL OPERATE KNOWLEDGE - Strategy Execution - Transformation - Entreprise PPPM - DQ governance ENGINEER Front Office - PMO - Deployment… Back Office Biz Gov FUNCTIONAL Tech KNOWLEDGE - Risk - Finance - Facility - IT