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BI congres 2016-4: Hoe groei je als organisatie in analytische maturiteit? - Natan Meekers -SAS

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9de BI congres van het BICC-Thomas More: 24 maart 2016

Waar traditionele BI voornamelijk beschrijft van WAT er gebeurd is, kunnen we met Self-Service BI een stapje verder gaan en een eerste verklaring geven WAAROM iets zich voordoet. Als we echter tot de wortel willen geraken, moeten we gebruik maken van Analytics.

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BI congres 2016-4: Hoe groei je als organisatie in analytische maturiteit? - Natan Meekers -SAS

  1. 1. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. BUILD ANALYTICAL MATURITY
  2. 2. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. I. INTRODUCTION II. WHAT IS IT? III. APPROACH & METHODOLOGY IV. MARKET OBSERVATION V. CUSTOMER STORY
  3. 3. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. NATAN MEEKERS Data & Analytics Advisor natan.meekers@sas.com +32 2 766 08 35 NatanMeekers @NatanMeekers
  4. 4. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. What happened? Standard reports Where exactly is the problem? Query drill down Why is this happening? Statistical Analysis What if these trends continue? Forecast & predict What is the best that can happen? When is a problem happening ? Alerts Raw data Clean data Optimise Competitive Advantage $ Degree of Intelligence THE PATH FROM DATA TO VALUE
  5. 5. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. 40BUSINESS ANALYTICS YEARS OF CUSTOMER SATISFACTION & LOYALTY* #1 58OFFICES WORLDWIDE
  6. 6. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. WOULD YOU RATHER LOOK AHEAD OR BEHIND?
  7. 7. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. ANALYTICS WHAT EXACTLY IS IT? Philip R. Bevington McGraw-Hill, 1969 DATA REDUCTION & ERROR ANALYSIS
  8. 8. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. ANALYTICALLY UNDEVELOPED ANALYTICALLY AWARE ANALYTICALLY INFORMED ANALYTICALLY RELIANT ANALYTICALLY INNOVATIVE LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL 5 Isolated analytics use. Unsophisticated tools and practices predominate Predictive analytics usage is part of mission critical applications only. Full benefits are not understood by a majority in the organization. Analytics usage consists primarily of tactical and ad hoc approaches. Analytics dev. and deployment is constrained, yet departments have their own experts and/or initiatives. Analytics talent is centralized into larger groups. Management understands and supports analytics for strategic value, thus bringing business units into alignment Company is committed to analytics as part of its future growth plan. Business units embrace their own transformational analytical plans.
  9. 9. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. APPROACH & METHODOLOGY PREDICTIVE ANALYTICS EXPLORATION, VISUALIZATION & DESCRIPTIVE STATISTICS DASHBOARDING & REPORTING
  10. 10. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. Hybrid Analytic APPROACH FOR Complex Problems ENTERPRISE DATA KNOWN PATTERNS UNKNOWN PATTERNS COMPLEX PATTERNS UNSTRUCTURED DATA ASSOCIATIVE LINKING HYBRID APPROACH RULES Rules to surface known issues ANOMALY DETECTION Algorithms to surface unusual behaviors PREDICTIVE MODELS Identify patterns and relationships to anticipate future events TEXT MINING Enhance analytic methods with unstructured data NETWORK ANALYSIS Associative discovery through automated link analysis across heterogeneous data
  11. 11. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. Hybrid Analytic APPROACH DATA DEPLOYMENTDISCOVERY
  12. 12. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. • Assess your current readiness • Available Skills • Information Processes • Technical Infrastructure • Culture • Conduct a gap analysis • Identify starting points • Develop a roadmap APPROACH & METHODOLOGY
  13. 13. BIG DATA ANALYTICS IMPROVES DECISIONS Strategic Decisions Tactical Decisions Operational Decisions Big choices of Identity and Direction Long term How to manage performance to achieve the strategy Middle term Daily high-volume business decisions Short term VALUE = NUMBER OF DECISIONS x VALUE IMPROVEMENT PER DECISION Ex. Focus on physical stores Ex. Changes in assortiment Ex. Product promotions
  14. 14. 53% 41% 47% 25% 6% 15% 0% 10% 20% 30% 40% 50% 60% Make data-driven decisions "very frequently" Make decisions "much faster" than market peers Execute decisions as intended "most of the time" PERCENTAGE OF RESPONDENTS BY DATA CAPABILITIES Top performer Everyone Else TOP PERFORMERS
  15. 15. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. 16% 13% 7% 10% 6% 3% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% Revenue (organic, non- acquisition) Operating cash flow Operating costs BOTTOM LINE IMPROVEMENTS YOY Advanced Analytics & Big Data All Others Source: Aberdeen Group, July 2014
  16. 16. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. BIG FOOD COMPANY 1.000.000.000 UNITS / DAY 10.000 PRODUCTS TO MARKET
  17. 17. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. Seasonal influences Different sales regions Many product categories Complexity of perishable nature of goods Retail trends Abundance of departments
  18. 18. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. PRODUCTION How many units do we need to produce? When to produce these products? MARKETING What is the impact of my campaign on sales? Can I drive demand with my campaigns? SUPPLY CHAIN Optimize routes to supply Better planning 50% LESS BIASED FORECAST ABILITY TO SHAPE DEMAND
  19. 19. Copyr ight © 2016, SAS Institute Inc. All rights reser ved. ONE WHO DOES NOT LOOK AHEAD REMAINS BEHIND. BRAZILIAN QUOTE

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