The document discusses data-driven decision making and data management. It introduces Deployments Factory SA, which provides data-driven project, program, and portfolio management solutions. The presentation covers: 1) How data-driven decision making can improve organizational performance 2) Challenges of managing large, diverse data sources 3) The "information virtuous cycle" and how the DataFactory concept transforms raw data into useful information to support better decisions 4) Examples of real applications in different domains like project management, risk management, and strategic execution.

1. Data-driven (Project) Management
From 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. Intro
Deployments 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. 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. 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. 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. VVV & Challenges
 What's New? Three key differences with business analytics (VVV)
 Volume
 Velocity
 Variety
 2 examples
Source http://www.kaushik.net/
 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 person's 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. 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 : http://www.micfarris.com/2011/10/hillion-on-what-is-a-
 From What do we think? : hippo style intuitive decisions data-scientist/
 To What do we know? decisions based on evidence
Conclusion
 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. 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. 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. 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. 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. What do they want?
 Make better decisions
12

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. 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 Information
Organisations use
other data to
complete theirs
16

15. 3 possible levers for improvement
Driving actions through
existing management
rogram
Other
sources
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. 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
rogram
from multiple systems & referentials orignal data sources drilldown capacity
Program Governance
1 2 3
Transformation
1 3
“systems Rep. “actionable
produce data, information is
not 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. 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

18. Data-driven
PROJECT, PROGRAM, PORTOFOLIO
MANAGEMENT SOLUTIONS
20

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. 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. Data-driven
BUSINESS SOLUTIONS
23

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 informations
International projects Project operational informations
24

23. Application 4 : Risk & Basel 2 chain
Entity 1
Risk & Basel 2 CHAIN
Ref 1
Entity 2
Ref 2
Entity 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. Application 5 : Corporate Reporting
Global
Factoring
Belgium 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. Data-driven
FUTURE SOLUTIONS
27

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. Application 6 : Strategic Execution Office (2/2)
Run Actions
Strategic Actions
Change actions
STRATEGIC STRATEGIC DATAFACTORY
MODULE Strategic
STRATEGY
Governance
CHANGE DATAFACTORY
Change
Governance
RUN DATAFACTORY
Run
Governance
STRATEGIC EXECUTION OFFICE
29

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. 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. 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. 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. Thank you
Thibaut De Vylder
 Deployments Factory SA
 tdv@depfac.com
 Mobile : +32 478 69 21 86
 @Thibaut73
Deployments Factory SA
 Rue Guillaume Stocqstraat 79
 1050 Brussels
 http://www.deploymentsfactory.com
 @depfac
 Tel : +32 2 290 63 90 Fax : +32 2 290 63 99
34

33. Appendix
35

34. Concept#01 : DataFactory Architecture (level 1)
A. Capture C. Transform D. Restitute
B. Store
36

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 - Reports
Documents, Quality - Dashboards
indicators - Triggers &
emails, pdf…
Unstructured & KQI’s exceptions
Extractors - Other
…
Data
Quality
37

36. Concept#02 : Unit Bridge
Unit 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