Copyright © 2016, SAS Institute Inc. All rights reserved. B UILD ANALYTICAL MATURITY
Apr 15, 2017
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BUILDANALYTICAL
MATURITY
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I. INTRODUCTION
II. WHAT IS IT?
III. APPROACH & METHODOLOGY
IV. MARKET OBSERVATION
V. CUSTOMER STORY
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NATAN MEEKERSData & Analytics Advisor
[email protected]+32 2 766 08 35NatanMeekers@NatanMeekers
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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
OptimiseCompetitive Advantage $
Degree of Intelligence
THE PATH FROM DATA TO VALUE
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40BUSINESSANALYTICS
YEARS OF
CUSTOMERSATISFACTION & LOYALTY*
#1
58 OFFICESWORLDWIDE
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WOULD YOU RATHER LOOK AHEADOR BEHIND?
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ANALYTICS WHAT EXACTLY IS IT?
Philip R. Bevington McGraw-Hill, 1969
DATA REDUCTION& ERRORANALYSIS
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ANALYTICALLYUNDEVELOPED
ANALYTICALLY AWARE
ANALYTICALLY INFORMED
ANALYTICALLYRELIANT
ANALYTICALLYINNOVATIVE
LEVEL 1LEVEL 2
LEVEL 3LEVEL 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.
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APPROACH & METHODOLOGY
PREDICTIVEANALYTICS
EXPLORATION, VISUALIZATION &
DESCRIPTIVESTATISTICS
DASHBOARDING & REPORTING
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Hybrid Analytic APPROACH FOR Complex Problems
ENTERPRISE DATA KNOWNPATTERNS
UNKNOWNPATTERNS
COMPLEXPATTERNS
UNSTRUCTUREDDATA
ASSOCIATIVELINKING
HYBRID APPROACH
RULES
Rules to surface known issues
ANOMALYDETECTION
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
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Hybrid Analytic APPROACH
DATA
DEPLOYMENTDISCOVERY
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• Assess your current readiness• Available Skills• Information Processes• Technical Infrastructure• Culture
• Conduct a gap analysis• Identify starting points• Develop a roadmap
APPROACH & METHODOLOGY
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 inassortiment
Ex. Product promotions
53%
41%
47%
25%
6%
15%
0%
10%
20%
30%
40%
50%
60%
Make data-drivendecisions "very
frequently"
Make decisions"much faster" than
market peers
Execute decisionsas intended "most
of the time"
PERCENTAGE OF RESPONDENTS BY DATA CAPABILITIES
Top performer
Everyone Else
TOPPERFORMERS
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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
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BIG FOOD COMPANY
1.000.000.000 UNITS / DAY10.000 PRODUCTS TO MARKET
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Seasonal influences
Different sales regions
Many product categories
Complexity of perishable nature
of goods
Retail trends
Abundance of
departments
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PRODUCTIONHow many units do we need to produce?
When to produce these products?
MARKETINGWhat is the impact of my campaign on sales?
Can I drive demand with my campaigns?
SUPPLY CHAINOptimize routes to supply
Better planning
50% LESS BIASED FORECASTABILITY TO SHAPE DEMAND
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ONE WHO DOES NOT LOOK AHEAD REMAINS BEHIND.
BRAZILIAN QUOTE