BDE2016

1. The world needs Smart Data
more than Big Data
Jacques Adriaansen
Big Data Expo Utrecht – September 21, 2016

2. 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

3. The Awesome Ways Big Data Is Used Today
To 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
Big Data has potential

4. Big Data has potential
but…
… there’s a bit more needed than ‘just’ new technology
and tooling, isn’t it?

5. Big Data has potential, and some huge challenges!
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

6. 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

7. Big Data Challenge 1
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/

8. 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

9. Big Data Challenge 2
No knowledge
nor insight
without (scarce)
Data Scientists
Programming
Business
Technology
Mathematics &
Modelling
Statistics
25 Skills Assessed in the
Data Science Survey

10. Optimization
Math
Graphical Models
Algorithms
Simulations
Bayesian Statistics
Data Management
Data Mining
Visualization tools
Statistical modeling
Science / Scientific Method
Communication
Managing unstructured data
Managing structured data
Natural Language Processing
Machine Learning
Big and Distributed Data
Big Data Challenge 2 – 25 Needed Data Scientist Skills
Systems Design
Systems Administration
Database Administration
Cloud Management
Back-End Programming
Front-End Programming
Product design and development
Project management
Business development
Budgeting
Governance & Compliance

11. Big Data Challenge 2
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 the
Data Science Survey

12. 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

13. 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
Predictive
Analytics

14. 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”
What’s needed for Real Business User Self-Service
Application
Logic
Complexity
Data Model
Complexity
Understand it and Hide it for the User

15. Predictive
Analytics

16. 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

17. Results @ Coca Cola Bottling Co. Consolidated (US)
Source: Presentation of Brett Frankenberg
Director 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 SAP
had a very short learning curve
(minutes to hours)
Significant opportunity to CLEANSE
our ERP Master Data
as well as Transactional data
Response times
to CREATE and EXECUTE reports
were extremely fast
(measured in minutes and seconds! vs hours)
80% of our TOTAL Business requirements for extensions
were able to be implemented and were live in less than 1 week!
(Several of these had been attempted using traditional BI Technologies – without success)

18. SUPPORT PROCESSESBUSINESS PROCESSES
Every Angle application at
StrategicTactical
I
N
S
I
G
H
T
Ad-Hoc
Self Service
Business
Analytics
Operational
A
C
T
I
O
N
Predefined
Operational
Exception Based
Action Lists
A
C
T
I
O
N
Corporate
Governance
Fraud and
Compliance
Operational
Exception Based
Action Lists
I
N
S
I
G
H
T
Data Integrity
Project
Management
Self Service
Business
Analytics

19. 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

20. Improvement Projects Focus
Operations
Excellence
Optimal
Stock
Optimal
Gridding
Sales
Strategy
Product
Range
Range Analysis
Sales Analysis
Grid Analysis
Grid Management
Weighted GMROI
Analysis
Process
Optimization
Working Capital
Management
Stock
Availability
Management
Warehouse
Capacity
Management

21. 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

22. Thank you for attending!
Contact details:
Email j.adriaansen@everyangle.com
LinkedIn https://nl.linkedin.com/in/jacquesadriaansenea
Or step by the Every Angle booth 42 (follow the scent of freshly baked waffles!)

23. Food, Pharma
& Chemicals
Utilities & Energy
Discrete Manufacturing
Wholesale, Retail
& Fashion

24. Backup slides

26. Product design and development
Project management
Business development
Budgeting
Governance & Compliance (e.g. security)
Skill set of a Data Scientist (team)
Programming
Business
Technology
Mathematics &
Modelling
Statistics

27. 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 &
Modelling
Statistics

28. 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 &
Modelling
Statistics

29. Skill set of a Data Scientist (team)
Systems Design
Systems Administration (e.g. Unix)
Database Administration (e,g, MySQL, NOSQL)
Cloud Management
Back-End Programming (e.g. JAVA/Rails/Objective C)
Front-End Programming (e.g. user interfaces, JavaScript, HTML)
Programming
Business
Technology
Mathematics &
Modelling
Statistics

30. 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 &
Modelling
Statistics