ADVANCED ANALYTICS & OPTIMIZATION IS KEY TO INNOVATE AND OUTPERFORM QUANTS ANALYTICS TECHNOLOGY ACADEMY

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1 ADVANCED ANALYTICS & OPTIMIZATION IS KEY TO INNOVATE AND OUTPERFORM QUANTS ANALYTICS TECHNOLOGY ACADEMY

2 NOW IT IS THE TIME TO DISRUPT OR TO BE DISRUPTED

3 WHAT DO BUSINESS EXECUTIVES THINK BIG DATA IS? Source: IBM

4 WE CANNOT SOLVE OUR PROBLEMS WITH THE SAME THINKING USED WHEN CREATING THEM - Albert Einstein - ADVANCED ANALYTICS & OPTIMIZATION PROPOSITION

5 COMPETITIVE ADVANTAGE WHAT IF THESE TRENDS CONTINUE? WHY IS THIS HAPPENING? WHAT IS THE BEST THAT CAN HAPPEN? WHAT WILL HAPPEN NEXT? STATISTIC ANALYSIS FORE- CASTING PREDICTIVE MODELING OPTIMI- ZATION STANDARD REPORTS AD HOC REPORTS QUERY & DRILL- DOWN ALERTS WHERE EXACTLY IS THE PROBLEM? HOW MANY, HOW OFTEN, WHERE? WHAT ACTIONS ARE NEEDED? WHAT HAPPENED? DEGREE OF INTELLIGENCE ANALYTICS MATURITY CURVE Source: Davenport

6 DATA INVOLVEMENT DECISION MAKER INVOLVEMENT DESCRIPTIVE learn from hindsight DIAGNOSTIC find indications with insight DATA PREDICTIVE take preventive actions DECISION MAKING IMPLEMENT ACTIONS PRESCRIPTIVE choose from optimized alternatives OPTIMIZED fully automated optimal decisions INVOLVEMENT OF SUPPORTING DATA IN YOUR DECISION MAKING PROCES

MAKING IMPLEMENT ACTIONS Aggregation & Clustering Statistics Data Mining Machine Learning Forecasting

7 Data Architecture Data Cleansing Structured and Unstructured Data Text Mining and Video Analysis Real- time Connect Alerts Real- time System Integration Mobile Interaction DATA ADVANCED ANALYTICS AND OPTIMIZATION DECISION MAKING IMPLEMENT ACTIONS Aggregation & Clustering Statistics Data Mining Machine Learning Forecasting Optimization Visualization Drill- Down Tuning Scenario s and Advice DIFFERENT STEPS, DIFFERENT METHODS AND DIFFERENT TECHNOLOGIES

8 VISUALIZATION FORECASTING OPTIMIZATION

9 BIG DATA IS DATA CAN NOT BE PROCESSED BY NORMAL MEANS DUE TO VOLUME, VELOCITY, VARIETY AND/OR VERACITY

10 Source: IBM

11 BIG DATA REQUIRES YOU TO LEARN A NEW A- B- C WHAT YOU NEED ANALYTICS which is NOT the same as reporting/bi BUSINESS KNOWLEDGE with an eye for IoT and Big Data COMPUTER SCIENCE no, this is not computer programming

12 INKOOP EN BIG DATA ALGEMEEN Klik op het pictogram als u een afbeelding wilt toevoegen n Toegenomen rekenkracht, betere algoritmes en nieuwe technieken als textmining bieden legio kansen om aanbestedingsprocessen te optimaliseren. n Voorspelbaarheid en betrouwbaarheid staan daarbij centraal. n Hoe krijg ik de diensten of producten waarvoor ik aanbesteed op tijd en binnen budget geleverd? n Houden mijn scenario s rekening met wijzigende omstandigheden? n Deze vragen kunnen accuraat beantwoord worden met behulp van analytics en big data toepassingen.

INKOOP EN BIG DATA PROCES VAN AANBESTEDING Klik op het pictogram als u een afbeelding wilt toevoegen VOORBEREIDING SELECTIE REALISATIE Data over vergelijkbare projecten, aanbestedingen en/of

13 INKOOP EN BIG DATA PROCES VAN AANBESTEDING Klik op het pictogram als u een afbeelding wilt toevoegen VOORBEREIDING SELECTIE REALISATIE Data over vergelijkbare projecten, aanbestedingen en/of leveranciers Decision support systems Cost estimating Text/data mining Vergelijking/Scoring met behulp van Text/data mining, cost estimating, decision support Benchmarking Informatie over leveranciers Ontdekken martkverstoringen Ethisch inkopen: Andere manier van meten dan via steekproeven doordat alledata beschikbaar zijn Volgen realisatie project en vergelijkbare projecten: text/data mining projecten vb grote oliemaatschappij text miningop projecten

14 IN THEORY, THEORY AND PRACTICE ARE THE SAME, IN PRACTICE THEY ARE NOT - Albert Einstein - THE ANALYTICS ACADEMY BIG DATA ALLIANCE BIG DATA MBA BIG DATA & BUSINESS ANALYTICS

15 BIG DATA ALLIANCE

16 WORKSHOP INTERACTIVE SESSIONS

17 SO HOW TO GET STARTED WITH BIG DATA ANALYTICS?

18 DIFFERENT TYPES OF DATA REQUIRE DIFFERENT APPROACHES UNCOUPLED DATA LOOSELY COUPLED DATA TIGHTLY COUPLED DATA

19 SO... HOW DOES ANALYTICS IMPACT YOUR COMPANY THE NEXT FIVE YEARS? STAGE 4 ANALYTICAL COMPANIES How can we innovate and differentiate? STAGE 3 ANALYTICAL ASPIRATIONS What is happening now? STAGE 2 LOCALISED ANALYTICS What can we do to improve this activity? STAGE 1 ANALYTICALLY IMPAIRED What happened in our business? ANALYTICS MATURITY Source: Based on Davenport (2007)

20 ORTEC SURVEY - RANKING & SCORE ON CRITICAL SUCCESS FACTORS 1 CLEAR STRATEGY having a clear company strategy NOT SO GOOD GOOD 2 MANAGEMENT SUPPORT support by higher management 3 TALENT availability of talent and skills PRIORITY TRAINING training and education SYSTEMS/PROCEDURES the right supporting systems and procedures BUDGET having the availability of sufficient financial resources 7 ORGANIZATION organization supporting multi- disciplinary projects 8 LEGAL AND ETHICS sound procedures for legal and ethical issues

21 ORTEC SURVEY - BIG DATA ANALYTICS & OPPORTUNITIES FOR IMPROVEMENT

22 PERFORMANCE ASSESSMENT A BIRD EYE S VIEW ON HOW TO CLIMB THE ANALYTICS MATURITY CURVE PEOPLE & SKILLS PROCESS & ORGANIZATION TOOLING & TECHNOLOGY

23 DELTA MODEL WHERE IS YOUR COMPANY ON BIG DATA ANALYTICS? ANALYTICALLY IMPAIRED STAGE 1 EARLY STAGE ANALYTICS STAGE 2 LATER STAGE ANALYTICS STAGE 3 ANALYTICAL COMPANIES STAGE 4 DATA ENTERPRISE LEADERSHIP TARGETS ANALYSTS

24 ANY POWERFUL IDEA IS ABSOLUTELY FASCINATING AND ABSOLUTELY USELESS UNTIL WE CHOOSE TO USE IT

26 CRIME PREVENTION EFFECT ALLOCATION

27 ENERGY SMARTGRIDS

28 CONDITION BASED MAINTENANCE

29 q q q q q Detect news before it is news All Dutch tweets Natural Language Processing Key words and emotion Trend detection REALTIME TREND DETECTION FROM TWITTER

30 NATURAL LANGUAGE PROCESSING

31 SPORT ANALYTICS

32 q Flexible models q Bill of materials q Project phasing q Constructions sourcing q Rate of Exchange q Risk factors q High- performance computing COST ESTIMATION OF CAPITAL AND CONSTRUCTION

33 PORTFOLIO MANAGEMENT

34 SEARCH ENGINE ADVERTISEMENT BID OPTIMIZATION

35 SMART PUBLISHING TO ENGAGE WITH YOUR AUDIENCE

36 q q q q q q Online advertising Display optimization Real- time bidding Customer profiling High- performance computing Exogenous Data DISPLAY OPTIMIZATION WITH REALTIME BIDDING

37 HUMAN RESOURCES ANALYTICS

38 SUPPLY CHAIN DESIGN, FORECASTING AND OPTIMIZATION

39 q q q q q q q q q Using historic sales information Taking into account competition Customer Segmentation Demand forecasting Rule Based Pricing Pricing Elasticities Revenue Optimization Online and Offline Millions of SKU s DYNAMIC PRICING AND REVENUE MANAGEMENT

40 THE ROAD TO ANALYTIC MATURITY STARTS NOW

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