Digitale Disruptie - BI slaat terug!

Inleiding
Digital Disruptie en BI
Dave Vanhoudt, BICC Thomas More

Onze missie
Een neutraal en onafhankelijk
platform voor samenwerking met
de bedrijfswereld met als
hoofddoel kennisdeling en
innovatie te stimuleren

Onze missie
“A time where technology and society are
evolving faster than the ability of many
organizations to adapt”

Agenda
• Sessie 1: Open source, meer dan disruptieve software?
Bart Maertens, Managing Partner, know.bi
• Sessie 2: M&S Analytics: Join the Big Data revolution!
Carl Sablon, Senior Consultant, Keyrus
Peter Poppe, Principal Consultant, Keyrus
• Sessie 3: Hoe het overzicht bewaren?
Jörgen Jacob, Business Unit Manager, Fit IT
• Sessie 4: Een versie van de waarheid: een achterhaald idee?
Tobias Temminck, Teradata, Benelux Technology Officer
• Sessie 5: Nood aan meer strategisch management?
Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More

Before lift-off
• Mogelijkheid tot korte interactie (vragen) na elke sessie
• Netwerk: TM_BICC met als key BICC1
• Twitter hastag: #BICongres15
• Twitter user: @BICC_ThomasMore

Sessie 1
Open Source
Meer dan disruptieve software?
Bart Maertens, Managing Partner know.bi

Open Source Business Intelligence

know.bi:
• Founded in 2012
• OSBI consultancy in Benelux and UK
• 5 consultants

What is OSS?
• “Open-source software (OSS) is computer
software with its source code made available
with a license in which the copyright holder
provides the rights to study, change and
distribute the software to anyone and for
any purpose”
• Free “as in speech” rather than “as in beer”

OSS Licensing
• Moving from copyleft (GPL family) to permissive (e.g. Apache
v2)
• 2015: Apache v2 (do wtf you want)
• Professional/Commercial OSS:
• Dual licensing/Open Core:
• Free Community Edition:
• go your own way
• Only free (as in beer) to download, use has a cost
• Paid Enterprise Edition:
• Pro approach (consultancy, support, training…)
• Enhanced functionality
• Beekeeper model

Evolution of OSS
• Infrastructure: Linux, OS on low level
hardware
• Middleware:
• databases (PostgreSQL, MariaDB)
• application servers (JBoss, Tomcat)
• Applications: Firefox, LibreOffice, GIMP
• OSS is ubiquitous: increased need for
standardization forces towards OSS

OSS Market Share
Open Source dominates in
• Supercomputing (485 of top 500
run Linux)
• Cloud computing (75% Linux)
• Web servers (65% Apache)
• Mobile
• Embedded
• IoT
• …

Why OSBI?
• Frequent releases (+/- 6 months)
• Support for cutting edge technologies (Big
Data!)
• Cost
• Flexibility to integrate in/with other platforms
• Easy to extend (plugin interfaces)
• Community ecosystem
• Avoid vendor lock in

OSBI Landscape• Data Integration:
• Talend
• Kettle/Pentaho Data Integration
• Reporting
• Eclipse BIRT
• Jasper Reports
• JFreeReport/Pentaho
Reporting
• OLAP
• Palo
• Mondrian/Pentaho Analysis +
Saiku
• Data Mining, Statistics:
• R
• RapidMiner
• Weka
• Platforms
• SpagoBI
• Pentaho
• Jedox

• Pentaho components:
• Data Integration (Kettle)
• Reporting OLAP (Mondrian)
• Data Mining (Weka)
• Dashboarding (CTools)
• BA server (security, scheduling)
• Community contributions (marketplace)

• Only complete OSBI platform in the market
• Founded in 2004
• (to be) acquired by HDS in 2015
• Open core:
• CE: OSS engines
• EE: CE + support, enhanced functionality
• Strong community in (ao) EU

Community involvement
• Marketplace:
• Kettle/PDI
• BA server
• Forums
• IRC (##pentaho)
• Social media
• Events: PBUG, PCM

Use Case: Cipal
• AthenaWeb: data warehouse solution for local
governments
• application-based data marts
• multitenant
• cloud-based
• flexible

Use Case: Cipal
Built end-to-end using Pentaho:
• PDI for extract, transfer, ETL
• Static reporting
• OLAP (Analyzer)
• Dashboards (EE dashboards + CTools)
• Role know.bi: coaching, infrastructure,
development
• Community involvement: PCM13, PBUG13,
PCM14, …

Sessie 2
Sales & Marketing Analytics
Join the Big Data revolution!
Carl Sablon, Senior Consultant, Keyrus
Peter Poppe, Principal Consultant, Keyrus

AGILITY I COLLABORATIVE INTELLIGENCE I INNOVATION I PERFORMANCE
CONSULTING I TECHNOLOGY
SALES AND MARKETING ANALYTICS
/ JOIN THE BIG DATA GENERATION!
PIETER VANDAMME
MARCH 2015

©Keyrus–Tousdroitsréservés
/ THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS
FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING
Understand
(context)
Model
(stakes)
Embed
(insights)
from intuition to business
issue formulation in a
data-centric problem
« data-analytic
thinking »
from modeling to
deployment
« analytic action »
Continuous monitoring
based on KPI’s and
« enrichment of the
data environment »

/ PRACTICAL ANALYTICS CASES IN MARKETING
CASE STUDY – SOLIDSTORE.COM
/ INTERNATIONAL RETAILER
/ CONSUMER ELECTRONICS, BOOKSTORE,
FASHION, LIFESTYLE
/ ONLINE SALES
/ PROMO DRIVEN

/ SALES FIGURES 2013 vs 2014
UNDERSTAND CUSTOMER BEHAVIOR
SOLIDSTORE.COM
Limited Analytical Capabilities

/ FIGURES PER COUNTRY
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
0%
2%
4%
6%
8%
10%
12%
Sales Per Country
Weight Country Evol vs N-1
SOLIDSTORE.COM
Reliability issues

/ VALUE SEGMENTATION: 2014 vs 2013
• +350 EUR/yearTier 1
•120 – 350 EUR/yearTier 2
•40 – 120
EUR/year
Tier 3
•- 40
EUR/year
Tier 4
18 k -4% 15,2M -5%
20 k -2% 4,1M -3%
19 k -4% 1,4 M -4%
21 k -1% 0,4 M -3%
€
27%
24%
25%
23%
2%
7%
20%
72%
SOLIDSTORE.COM
Labour intensive
Not Flexible

/ SOLIDSTORE.COM SALES AND MARKETING DATA ANALYTICS REQUIREMENTS
Extra capabilities required on top of traditional Enterprise
Business Intelligence to improve and
guarantee flexibility
Reporting
► Business and data expertise instead of
technology focus
Analytics
►Visual Story Telling with latest techniques
► Access to basic statistical algorithms
Data Integration
► Sandbox for advanced query
► Volatile and unstructured data access
High Performance
► Real-Time data exploration on granular data

Understand
(context)
Model
(stakes)
Embed
(insights)
« data-analytic
thinking »
from modeling to
deployment
data environment »
► Business Case Creation (Intuition)
► Data Discovery
► Data Acquisition

Decreasing Sales
 How does the different countries perform compared to each other ?
 Which product categories increase in sales compared to last year ?
 Does my different sales channels perform as expected ?
Customer Loss
 Do I lose high value customers compared to last year ?
 Are my customers loyal ?
Realize full potential of Online Channel
 How is the conversion of my web campaigns ?
 How can I optimize my campaign strategy approach ?
/ BUSINESS CASE (INTUITION)

Decreasing Sales

• How can I explain the
decreasing sales ?
• Channel specific performance:
online perform better than
stores, but below expectations
• Different performance between
countries: often product related
• Align marketing 2015 budgets
per country based on sales
facts per product category
• Extend dashboard with ROI and
budget figures
Question Insight Action Plan

Decreasing Sales
Customer Loss
 Do I lose high value customers compared to last year ?
 Are my customers loyal ?

decreasing sales ?
budget figures
• Do I lose high value customers ? • Customer trend towards lower
segments
• Positive inflow of new customers
in Tier 1
• Customer value segmentation
strongly related to products
bought (Hardware)
• Review customer segmentation
• Campaign Optimization: right
offer to the right customer at
the right time

decreasing sales ?
budget figures
segments
in Tier 1
bought (Hardware)
• Review customer
segmentations
the right time
• Do I realize the full potential of
my Online Channel ?
• Customers do not find easily
relevant products (Select No)
• Customers fall out too often at
Check Out (Time /
Transportation Costs)
• Credit Card process: time outs
• Recommendation Engine
• Renegotiate conditions with
delivery companies: calculate
potential lost sales
• Improve site performance on
peak moments

Understand
(context)
Model
(stakes)
Embed
(insights)
« data-analytic
thinking »
from modeling to
deployment
► Business Case Creation (Intuition)
► Data Discovery
► Data Acquisition
data environment »

/ DATA ECOSYSTEM ASSESSMENT
Enterprise ERP Data
Enterprise Data
Warehouse
Analytical Applications
Public Open Data
Web Data
Social Data
CRM Data
In-Store Terminal Data
enriched data
space of the
digital ecosystem
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights
Availability
Accessibility
Costs
Insights

Understand
(context)
Model
(stakes)
Embed
(insights)
« data-analytic
thinking »
from modeling to
deployment
data environment »
► Model
► Obtain Collateral Insights
► Enrich Data Sources

/ ACTIONS FOR ANALYTICS TEAM
MODEL CUSTOMER BEHAVIOR
decreasing sales ?
budget figures
segments
in Tier 1
bought (Hardware)
• Review customer
segmentations
the right time
• Do I realize the full potential of
my Online Channel ?
• Customers do not find easily
relevant products (Select No)
• Customers fall out too often at
Check Out (Time /
Transportation Costs)
• Credit Card process: time outs
• Recommendation Engine
• Renegotiate conditions with
delivery companies: calculate
potential lost sales)
• Improve site performance on
peak moments

/ PERSONALIZATION LEVEL
Mass
ACCURACY
COMPLEXITY
Segmentation
Personalised

/ COMPARE PREDICTIVE APPROACHES
Propensity model offer
Recommendation
engine
offer
Event Based
event/behaviour
change
A customer does not think in channels or campaigns ...
Right offer for the right customer in the right time

/ PLATFORM AS A SERVICE
KEYRUS EXPERTISE & KNOW-HOW
/ Data Integration
/ Transforming data into models
/ Publication of auto-scalable services
Data
Integration
Management
models
Development
models
Models
made by
services
Exported
Models
Test/validation
Interface
Interface
service call
Interface
data export
Integrate Generate
Keyrus ServicesClient Services
Test a
recommendation
Ask for a
recommendation

/ PLATFORM AS A SERVICE
/ Comparison of several possible
methodologies and configurations
of algorithms
/ Enabling publishing services in one
click. Analytics as a service
/ Auto scale-up of the cluster to start
the treatments and auto - scale
down . Cost and performance
optimization
/ Contains an API able to absorb a
variable load to 20 million customers
/ Two modes , Wizard and Flow
Designer offer assisted or customized
modelling
/ Methods developed on the basis of
open source algorithms
/ Service-oriented and modular
Architecture
/ Customizable extensions
/ Easy to test and deploy

MODEL MANAGEMENT
/Model Testing
Customer list
Customer’s
information
Recommandation
list for the
customer

Understand
(context)
Model
(stakes)
Embed
(insights)
« data-analytic
thinking »
from modeling to
deployment
data environment »
► Deploy
► Monitor
► Integrate

/ THE CAMPAIGN MANAGEMENT PROCESS
EMBED INSIGHTS IN DAY TO DAY PROCESSES
Communication and Campaign Policy
• Budget
• Platform Capacity
• Contact Rules
CustomerEligibility
Channel Product Reduction Validity Lay Out
Mail Books 5% 1 day Traditional
SMS Hardware 10% 1 week Fancy
Email DVD 15% 2 weeks Grouped
Coupon Toys 20% 1 month Multimedia
Accessories
Allocation Process
• Map the proposed offer to the customer
• Allocate taking into account eligibility and campaign policies

/ SOLIDSTORE DATA ECOSYSTEM
EMBED INSIGHTS IN DAY TO DAY PROCESSES
Applications
CRM Data
Terminals
ExternalExisting
Analytics
Data Sources
Ecosystem
Data Acquisition
& Qualification
Behavioral &
Predictive
Modeling
Data Ecosystem
Insights
► opportunities
► constraints
► limitations
► acquisition costs
<<enrich>>
<<get>>
machine learning
algorithms library
1
Model
Performance
Assessment
power, business
performance KPI’s
2
3
<<tune>>
<<enrich>>
technical and
technology services
experimental data lab
data pre-processing
routines and algorithms
Deployment
Feasibility &
Impact
<<finalize>>
4
ITERATIVE BUILD-UP

Approach
► Self service flexibility on top of traditional Business Intelligence
(volatile and unstructured data integration)
► Skilled consultants in Marketing Management, Data Science
and Campaign Management
► Based on Smart Visualization
► Advanced Analytical Capabilities beyond traditional models
(Statistics, Data Mining, Text Mining, Machine Learning)
► Iterative and collaborative approach
► Actionable
/ WRAP UP
KEY SUCCESS FACTORS
Understand
(context)
Model
(stakes)
Embed
(insights)

Platform
► Scalable solutions
► Analytics on Big Data High Performance platforms
(Volume/Time 2 Market/Structured and Unstructured)
► Skilled in both open source as traditional vendor
platforms and products
► Available in private and public cloud
► Embedded in the operational processes
/ WRAP UP
KEY SUCCESS FACTORS
Understand
(context)
Model
(stakes)
Embed
(insights)

One more thing...
Enterprise community of students
1 IMS

“Education is not the piling on of learning, information, data,
facts, skills or abilities – that’s training or instruction – but is
rather a making visible what is hidden as a seed...”
- Thomas More

Sessie 3
The Data Gods must be crazy...
Hoe het overzicht behouden?
Natalie Beernaert, Business Unit Manager, Fit IT

Predictive Analytics
Creating Business Value from your Data
Nathalie Beernaert – 26 Maart 2015

Agenda
●Introduction
●Value for the Customer
●Data Mining vs Predictive Analytics
●Learn from Experience
●Use Cases by Function
●Use Cases by Market
●Return on Investment

Fit IT at a glance
Strategic PartnersFacts & Figures
● 3 Activities
 Business Analytics
 Systems Engineering
 Business Applications
● > 14 Mio Revenue
● 90 FTE’s
Locations
 Ghent
 Brussels
 Antwerp

Value for the Customer
Reporting
Query, Search,
Reporting
COMPLEXITY
BUSINESS VALUE
What
happened
Why did it
happen
What’s
happening
now
What might
happen
Analysis
OLAP
Visualisation
Monitoring
Dashboards,
scorecards
Prediction
Predictive
Analytics

Data Mining versus Predictive Analytics
“Which products are
bought together”
=> CORRELATIONS
“Who buys a certain
product and why”
=> INFLUENCE
Predictive
Analytics
Data
Mining

Learn from Experience
Male, Ghent, Married, Children, ...

Use Cases by Function
Product Mix
Marketing
Predicting Life Time Value
Up Selling
Channel Optimization
Reactivation Likelyhood
Customer Churn
Risk
Credit Risk
Accounts Payable Recovery
Fraud Detection
Anti-Money Laundering
Treasury or Currency Risk
Churn
HR
Resume Screening
Training Recommendation
Talent Management
Employee Churn

Use Cases by Market
Product Mix
Life Science
Drug/chemical Discovery &
Analysis
Diagnostic
Targeting
(CRM)
Predicting prescription adherence
with different approaches to
reminding patients
Predicting drug demand in
different geographies for
different products
Churn
Retail
Merchandising
Shrinkage Analytics
Location of New Stores
Pricing
Market Basket
Analysis
Next Best Offer Analysis
Warranty Analytics
Insurance
Claims
Prediction
Investments
Product Mix
Agent and Brand Performance
Price Sensitivity

Return on Investment
Mailing to all customers (1.000.000)
 Cost: €2
 Profit/Conversion: €220
 Response rate 1%
 Profit: €200.000
Mailing to segment customers
(250.000)
 Cost: €2
 Profit/Conversion: €220
 Response rate 3%
 Profit: € 1.150.000

Thank you !
Axians - Fit IT nv
Guldensporenpark 35
9820 Merelbeke
nathalie.beernaert@axians.com
0476/27.66.03
jorgen.jacob@axians.com
0475/60.42.27

Sessie 3
The Data Gods must be crazy...
Hoe het overzicht behouden?
Jörgen Jacob, Business Unit Manager, Fit IT

Sessie 4
Een versie van de waarheid
Een achterhaald idee?
Tobias Temmink, BeNeLux Technology Officer, Teradata

INTEGRATED DATA IN THE BIG DATA ERA
Tobias Temmink – Technology Officer
March 2015

97
Data and Analytics Evolution
Application
Centric
Integration
Centralized
Decentralized
Capability
Rigid Agile
Data and
Analytic
Centric

98
Organization has a full fledged analytic architecture that is enterprise wide, fully automated,
integrated into processes, and sophisticated
Organization has high quality data. An enterprise wide analytics plan, governance principles, and
some automated analytics
Proliferation of BI tools and data marts but most data remains unintegrated. Non standardized, and
inaccessible
Organization collects transaction data efficiently but often lacks the right data for better decision
making
Organization is plagued by missing or poor quality data, multiple definitions of its data, and poorly
integrated systems
Stages of Analytic Maturity
Source: Davenport Harris, Competing on Analytics, Harvard Business School Press, 2007, pp156

99
FINANCE
Revenue
Expenses
Customers
CUSTOMER CARE
Customer
Products
Orders
Case History
SALES
Orders
Customers
Products
MARKETING
Customers
Orders
Campaign History
OPERATIONS
Inventory
Returns
Manufacturing
Supply Chain
Which plants are
using which suppliers
for EV batteries?
How many EV
batteries are in
inventory by
manufacturing plant?
What is the trend of
warranty costs?
What is the Year-Over-
Year growth in hybrid
sales?
How many people
reported an issue with
EV batteries last
month?
How many people
made a warranty claim
on Hybrid cars last
week?
How many sales of
hybrid cars have
been made quarter
to date?
What % of after
market accessories
are sold to hybrid
customers?
Which customers should
get upcoming email
communication on
hybrid car extended
warranties?
Which of our customers
are likely to buy a
hybrid car in the next 3
months?
54 32 29 49 66

101
2855
Given the rise in warranty costs, isolate the problem to be a specific plant, then isolate to a specific battery lot.
Communicate with affected customers, who have not already made a warranty claim on batteries, through
Marketing and Customer Service channels to recall cars with batteries.
Inventory
Returns
Manufacturing
Supply Chain
Customer Service
Orders
Revenue
Expenses
Case History
Customers
Products
Pipeline
Customers
Campaign History
FINANCE
SALESMARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
2855

102
2855
Inventory
Returns
Manufacturing
Supply Chain
Customer Service
Orders
Revenue
Expenses
Case History
Customers
Products
Pipeline
Customers
Campaign History
FINANCE
SALESMARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
Tightly Coupled

103
Is not about Volume, Velocity and Variety anymore….
It is about how you use the data and analytics

104
BIG DATA
WEB
Petabytes
CRM
Terabytes
Gigabytes
ERP
Exabytes
INCREASING Data Variety and Complexity
User Generated
Content
Mobile Web
SMS/MMS
Sentiment
External
Demographics
HD Video
Speech to Text
Product/
Service Logs
Social Network
Business Data
Feeds
User Click Stream
Web Logs
Offer History A/B Testing
Dynamic Pricing
Affiliate Networks
Search Marketing
Behavioral
Targeting
Dynamic Funnels
Payment
Record Support Contacts
Customer Touches
Purchase
Detail
Purchase
Record
Offer Details
Segmentation
DECREASING Value Density in the Data
Big Data: From Transactions to Interactions

106
2855
SENSOR
DIGITAL ADVERTISING
CLICKSTREAM INTERACTIONS
RATINGS & REVIEWS
CUSTOMER PORTAL
INTERACTIONS
EXTERNAL INTERACTIONS
SOCIAL MEDIA
IVR Routing
RFID
ELECTRONIC COMMERCE
FINANCE
SALES
MARKETING
Inventory
Returns
Manufacturing
Supply Chain
Customer Service
Orders
Revenue
Expenses
Case History
Customers
Products
Pipeline
Customers
Campaign History
OPERATIONS
CUSTOMER CARE –
AUDIO RECORDINGS
Maps
Telemetry
SERVER LOGS
CUSTOMER
EXPERIENCE

107
2855
Tightly Coupled
Loosely Coupled
Non Coupled

109 109
2855
FINANCE
SALES
MARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
By combining customer care ,
warranty, and supply chain data with
battery sensor data, it is discovered
that excessive heating on cells from a
specific manufacturer is the root cause.
CASE HISTORY
COSTSSENSOR
SUPPLY CHAIN

110 110
2855
MANUFACTURING
CAMPAIGN HISTORY
COSTS
PRODUCTS
FINANCE
SALES
MARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
Given the rise in warranty
costs, isolate the problem to
be a specific plant, then
isolate the specific lot.
Result is two-thirds of the bad
battery lot are fine, and
exclude them from the recall.
Communicate with affected
customers, who have not
already made a warranty
claim on batteries, through
Marketing and Customer
Service channels to recall
cars with batteries.
CUSTOMERS
CASE HISTORY
SENSOR

111 © 2014 Teradata
Customer
Segments
Product
Affinity
Predictive
Part
Customer
Churn
Customer
LTV
Non Coupled
Loosely
Coupled
Tightly
Coupled
Business
Generated
Human
Generated
Machine
Generated
Interaction
Generated
Operational
Intelligence
Predictive
Analytics
Graph
Analytics
Path Analytics
Machine Learning
Customer
Risk
E-Mail Offer
Reporting
Data
Business
Decision
Degrees
of Integration
Analytic
Analytic
Processes
Business
Process
Customer
Product
Email
Product Offer
Customer Care
Treatment

Sessie 4
Een versie van de waarheid
Een achterhaald idee?
Tobias Temminck, BeNeLux Technology Officer, Teradata

Afsluitende sessie & wrap-up
Nood aan meer strategische management?
Dries Van Nieuwenhuyse, BICC Thomas More

Strategie = TOP DOWN
Meestal start men met een schitterende missie en een veelbelovend
visie en zet men alles in het werk om die te realiseren…

Strategie = BOTTOM UP
• De vraag die zich stelt
is echter of het
allemaal zo helder was
van bij het begin…
• Hebben succesvolle
bedrijven niet altijd een
goede strategie?
• Komt het niet
van onderen
naar boven?

Strategisch management
• Combinatie van
ontdekken en
ontwikkelen
• Van mogelijkheden om
op herhaalbare wijze
• Waarde te creëren

• Huwelijk tussen
gehoopte toekomst,
haalbare toekomst en
noodzakelijke toekomst
• Hoe kunnen we het
verschil maken en
blijven maken?

• Een zinvol business model kan maar worden gerealiseerd via
een passend organisatie model

Performance Management en
Strategisch Management
• Kan Performance MANAGEMENT hier een bijdrage leveren?
• Kunnen we iets leren van Strategisch MANAGEMENT?
• Natuurlijk, wat had je gedacht?

Strategieformulering
• Waar zijn we nu goed in?
• Waar zijn onze concurrenten goed in?
• Waar liggen nog opportuniteiten en in welke mate?
Ansoff
Product-
Markt matrix
BCG product
portfolio

Strategieopvolging
• Balanced Scorecard: visualisatie van de realisatie van de
strategie
• Actual versus Target
• Stapje achteruit en zien

Strategie(bij)sturing
• Faciliteren van de kwantitatieve beleidsprocessen door PDCA-
cyclus heen
o Plan
 Budgettering
 Opvolging
o Do
 Operationele ondersteuning
o Check
 Actual versus budget
 Balanced Scorecard

Strategie(bij)sturing
• (Re)Act
o Performance MANAGEMENT
heeft ervaring met het
formuleren en opvolgen van
strategie
o Continu bijsturen van de
strategie
o Terugkoppeling naar de
strategieformulering
o Target setting om strategische
doelen ook effectief op te
volgen en te realiseren
o Evaluatie van hoe goed we
wel bezig zijn
o Predictie van potentieel
o Forecasting van wat mogelijk
resultaat van onze strategie
zou kunnen zijn

Strategie = TOP-DOWN
Strategie = BOTTOM-UP
• Performance MANAGEMENT
heeft ervaring met het formuleren
en opvolgen van strategie
• Strategisch MANAGEMENT helpt
creatieve vragen te stellen van
wat mogelijk zou kunnen zijn
• Beide zijn met elkaar getrouwd,
en dat is maar goed ook…

Afsluitende sessie & wrap-up
Nood aan meer strategische management?
Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More

Change is the law of life. Those who look only to
the past or present are certain to miss the future.
- John F. Kennedy

Digitale Disruptie - BI slaat terug!

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