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The world needs Smart Data more than Big Data
Jacques AdriaansenBig Data Expo Utrecht – September 21, 2016
1
Need for a bit Less Complex and Stable Big Data Landscape
Need for (scarce) Data Scientists delivering ‘Smart Data’
Big Data has potential, but…
2
3
The world needs Smart Data more than Big Data
Need for Real Business User Self-Service (RBUSS)
What RBUSS and Smart Data yielded for Coca Cola and JD Group
4
5
The Awesome Ways Big Data Is Used TodayTo Change Our World
01. Understanding and Targeting Customers 02. Understanding and Optimizing Business Processes 03. Personal Quantification and Performance Optimization 04. Improving Healthcare and Public Health 05. Improving Sports Performance 06. Improving Science and Research 07. Optimizing Machine and Device Performance 08. Improving Security and Law Enforcement 09. Improving and Optimizing Cities and Countries 10. Financial Trading
Source: Bernard Marr
https://www.linkedin.com/pulse/20131113065157-64875646-the-awesome-ways-big-data-is-used-today-to-change-our-world
Internet of Things
Social Media
Unstructured Data
Big Data has potential
Big Data has potential
Internet of Things
Social Media
Unstructured Data
AvailableStructured Data(e.g. ERP)The
Solution !!!
but…… there’s a bit more needed than ‘just’ new technology and tooling, isn’t it?
Big Data has potential
TheSolution !!!
, and some huge challenges!
AvailableStructured Data(e.g. ERP)
Internet of Things
Social Media
Unstructured Data
The new technology and tooling is there, yet it is
constantly evolving, so… what to choose?
No knowledge nor insight
without (scarce) Data Scientists
IT departments have a hard time responding to requests for new reports
and analysis
1
Need for a bit Less Complex and Stable Big Data Landscape
Need for (scarce) Data Scientists delivering ‘Smart Data’
Big Data has potential, but…
2
3
The world needs Smart Data more than Big Data
Need for Real Business User Self-Service (RBUSS)
What RBUSS and Smart Data yielded for Coca Cola and JD Group
4
5
Big Data Challenge 1
Internet of Things
Social Media
Unstructured Data
The new technology and tooling is there, yet it is
constantly evolving, so… what to choose?
Big Data Landscape 2016 (Version 3.0)
http://mattturck.com/2016/02/01/big-data-landscape/
1
Need for a bit Less Complex and Stable Big Data Landscape
Need for (scarce) Data Scientists delivering ‘Smart Data’
Big Data has potential, but…
2
3
The world needs Smart Data more than Big Data
Need for Real Business User Self-Service (RBUSS)
What RBUSS and Smart Data yielded for Coca Cola and JD Group
4
5
Big Data Challenge 2
TheSolution !!!
No knowledge nor insight
without (scarce) Data Scientists
Programming
Business
Technology
Mathematics & ModellingStatistics
25 Skills Assessed in theData Science Survey
OptimizationMath
Graphical ModelsAlgorithmsSimulations
Bayesian Statistics
Data ManagementData Mining
Visualization toolsStatistical modeling
Science / Scientific MethodCommunication
Managing unstructured dataManaging structured data
Natural Language ProcessingMachine Learning
Big and Distributed Data
Big Data Challenge 2 – 25 Needed Data Scientist Skills
Systems DesignSystems AdministrationDatabase Administration
Cloud ManagementBack-End ProgrammingFront-End Programming
Product design and developmentProject management
Business developmentBudgeting
Governance & Compliance
Big Data Challenge 2
TheSolution !!!
No knowledge nor insight
without (scarce) data scientists
You will have to form a team of experts
very scarce and highly skilled thus hard to find and quite expensive
25 Skills Assessed in theData Science Survey
1
Need for a bit Less Complex and Stable Big Data Landscape
Need for (scarce) Data Scientists delivering ‘Smart Data’
Big Data has potential, but…
2
3
The world needs Smart Data more than Big Data
Need for Real Business User Self-Service (RBUSS)
What RBUSS and Smart Data yielded for Coca Cola and JD Group
4
5
A business user can answer > 90% of his business questions
within 3 minutes time
without the help of IT
What is “Real Business User Self-Service”?
Problems to cope with to get there
Application Logic Complexity
Data Model Complexity
PredictiveAnalytics
ERP (e.g. SAP) already contains loads of data,and most companies have a lot of trouble building
information from that structured pool of data…
Example… “A Simple Business Question”
… more than 35 tables involved!
Estimated development : 40 days
Estimated IT lead-time : 3 months
What’s needed for Real Business User Self-Service
Application Logic
Complexity
Data Model Complexity
Understand it and Hide it for the User
PredictiveAnalytics
1
Need for a bit Less Complex and Stable Big Data Landscape
Need for (scarce) Data Scientists delivering ‘Smart Data’
Big Data has potential, but…
2
3
The world needs Smart Data more than Big Data
Need for Real Business User Self-Service (RBUSS)
What RBUSS and Smart Data yielded for Coca Cola and JD Group
4
5
Results @ Coca Cola Bottling Co. Consolidated (US)
Source: Presentation of Brett FrankenbergDirector SC Planning Coca Cola Bottling Co. Consolidated
http://www.everyangle.com/video/coca-cola-ccbcc-enabling-our-people-to-make-better-decisions/?pagenum=3
Business users familiar with SAPhad a very short learning curve
(minutes to hours)
Significant opportunity to CLEANSEour ERP Master Data
as well as Transactional data
Response timesto CREATE and EXECUTE reports
were extremely fast(measured in minutes and seconds! vs hours)
80% of our TOTAL Business requirements for extensionswere able to be implemented and were live in less than 1 week!
(Several of these had been attempted using traditional BI Technologies – without success)
SUPPORT PROCESSESBUSINESS PROCESSES
Every Angle application atS
trategicTactical
INSIGHT
Ad-
Hoc Self Service
BusinessAnalytics
Operational
ACTIONP
rede
fined Operational
Exception BasedAction Lists
ACTION
Corporate Governance Fraud and
Compliance
Operational Exception Based
Action Lists
INSIGHT
Data IntegrityProject
Management
Self ServiceBusinessAnalytics
Insight into SAP and Reporting at JDG (in less than two weeks)
P2P S2D O2C F2R MASTER DATA
ORDERS
STOCK
PROCESS
VENDORS
MERCHANDISE
LOGISTICS
LAYBY
FINANCE
EXCEPTION ACTION LISTS
GOVERNANCE
Improvement Projects Focus
OperationsExcellence
OptimalStock
OptimalGridding
SalesStrategy
ProductRange
Range AnalysisSales Analysis
Grid AnalysisGrid Management
Weighted GMROI Analysis
ProcessOptimization
Working CapitalManagement
Stock Availability
Management
Warehouse Capacity
Management
Business Value Assessment - Quantification Categories
U
Unavailable Information Burden
P Pinpoint Fraud
L Legal Compliance Risk
TTime Savings
FFunds Release
I
Interest Savings on Capital
U P L TFI
Thank you for attending!
Contact details:Email [email protected] https://nl.linkedin.com/in/jacquesadriaansenea
Or step by the Every Angle booth 42 (follow the scent of freshly baked waffles!)
Food, Pharma & Chemicals
Utilities & Energy
Discrete Manufacturing
Wholesale, Retail & Fashion
Backup slides
Product design and developmentProject managementBusiness developmentBudgetingGovernance & Compliance (e.g. security)
Skill set of a Data Scientist (team)
Programming
Business
Technology
Mathematics & ModellingStatistics
Skill set of a Data Scientist (team)
Optimization (e.g. linear, non-linear)
Math (e.g. linear algebra, real analysis, calculus)
Graphical Models (e.g. social networks)
Algorithms (e.g. Computational complexity, Computer Science theory)
Simulations (e.g. discrete, agent-based, continuous)
Bayesian Statistics (e.g. Markov Chains, Monte Carlo method)
Programming
Business
Technology
Mathematics & ModellingStatistics
Skill set of a Data Scientist (team)
Data Management (e.g. de-duplicating, integration, Web scraping)
Data Mining (e.g. Python, SPSS, SAS)
Visualization tools (e.g. mapping, Web-based data visualization)
Statistical modeling (e.g. general linear model, GIS, Spatio-temporal)
Science / Scientific Method (e.g. experimental & research design)
Communication (e.g. sharing results, writing publishing, presentation)
Programming
Business
Technology
Mathematics & ModellingStatistics
Skill set of a Data Scientist (team)
Systems DesignSystems Administration (e.g. Unix)
Database Administration (e,g, MySQL, NOSQL)
Cloud ManagementBack-End Programming (e.g. JAVA/Rails/Objective C)
Front-End Programming (e.g. user interfaces, JavaScript, HTML)
Programming
Business
Technology
Mathematics & ModellingStatistics
Skill set of a Data Scientist (team)
Managing unstructured data (e.g. noSQL)
Managing structured data (e.g. SQL, JSON, XML)
Natural Language Processing (text mining)Machine Learning (e.g. decision trees, neural nets)
Big and Distributed Data (e.g. HADOOP, Map/Reduce, Spark)
Programming
Business
Technology
Mathematics & ModellingStatistics