Discover how to design and implement Big Data Solutions | Good Rebels

1. Confidential & internal use
Big Data
Building the Cognitive Era
Good Rebels
Kevin Sigliano kevin.sigliano@goodrebels.com
Partner

2. Kevin Sigliano
kevin.sigliano@goodrebels.com | www.linkedin.com/in/kevinsigliano | twitter.com/kevinsigliano

3. Agenda 3
Agenda
The Digital & Cognitive Era
What is Big Data?
Apps of Big Data
Big Data Roadmap
Success Cases of BD in FMCG
Benefits

4. Welcome to the Digital
Transformation Era

5. Consumer
Corporate
The pace of Speed

6. Growing channels

7. New experiences

8. Data Growth
50X Growth
All Data between 2010 - 2020
100X Growth
Mobile Data between 2010 - 2020

9. 50%
OF FORTUNE 500.
BUSINESS INSIDER

10. Digital
Transformationpowered by Big Data
Disruption
10

11. What is Big Data?

12. What is Big Data? 12

13. What is Big Data? 13
Team performance | Transfers | Players health | Competitors | Stadium engagement | Branding | Match Satisfaction | Ecommerce
Real Madrid

14. What is Big Data? 14
Footfall | Margin | Demand | Employee | Customer Satisfaction | Security | Growth | Traffic | Weather
Walmart

15. What is Big Data? 15
Airport Traffic | Crime | Digital Conversations | Ecommerce | Phising | Terrorism | Image Recognition
Central Inteligence Agency

16. What is Big Data? 16
New products Interest | Sales | Profiling | Store Performance | Testers | Location | Weather | Seasonality
Puig
www.admira.com

17. What is Big Data? 17
Data Driven Businesses

18. What is Big Data? 18
_Definition (wikipedia)
Big data is a term for data sets that are so large or complex that
traditional data processing application software is inadequate to deal
with them.
Challenges include capture, storage, analysis, data curation, search,
sharing, transfer, visualization, querying, updating and information privacy.

19. Nombre de Sección | Nombre de Subsección | 19
_Definition of Big Data (BIZ)
Massive Data Adoption for business purposes.
Business Applications
Technology Cognitive
New Business Model
Reduce Costs
Detect Risks
Process
Efficiency
Customer
Insights

20. 10%

21. 20%
Search online
21 CNBC 2017 - IBM Interview

22. 80%
Rest of Data
22 CNBC 2017 - IBM Interview

23. 203 B $ 2020
130 B $ in 2016
23

24. Big Data Challenges
Why Now?

25. Big Data Challenges
Why Now?

26. Big Data Challenges
Why Now?

27. Big Data Challenges
Why Now?
Google Technology Conference

28. VOLUME - VERACITY - VELOCITY - VARIETY

29. Volume
•Terabytes to Pentabytes of
data.
29Big Data Challenges
Mercola

30. Veracity
•Value of Data
30Big Data Challenges

31. Velocity
•90% of the worlds data has
been generated in the last
two years.
31Big Data Challenges
150 M
Emails sent
20 M
Whatsapp
conversations
6 M
Facebook posts
3 M
Youtube video
views
2,5 M
Google searches
350 K
Tweets shared
1 minute of speed
EXCELACOM

32. FraudLogs Payments MarketingInfluence
Variety
32Big Data Challenges
•Data in many forms
(structured, unstructured,
text, multimedia,…)
CRM Mobile InStore ERPSales
Web Text Social WeatherMultimedia

33. Apps of Big Data

34. Big Data Apps Driven by Business Solutions

35. Apps of Big Data
Life Time Value
Marketing Mix
Omnichannel
Customer Advocacy and
satisfaction
EXPERIENCE
PERFORMANCE ENGAGEMENT
INTELLIGENCE
Big Data Challenges

36. 36
Sales - Amazon
Impact of Big Data

37. 37
Innovation - Aliseda
Impact of Big Data

38. 38
Customer Satisfaction - Ikea
Impact of Big Data

39. 39
Risk - Banco Popular
Impact of Big Data

40. 40
Health - Medtronic
Impact of Big Data

41. 41
Big Data Impact
Impact of Big Data
Brand Client Business
Awareness
Meaningful
Power
Life Time Value
Engagement
WOM
Profitability
Risk
Growth
Reputation Satisfaction CoA

42. Big Data Roadmap

43. Big Data Fundamentals 43
Roadmap
Strategy Leadership Team
Customer
Experience
Data
Architecture
Technology Innovation
Change
Management
- Strategic
Framework
- Command Center
- Scorecard
- Defined Objectives,
Plan and Teams
- CEO Understanding
and commitment
- Executive
committee
engagement
- Budgets
- Validated KPIs
- Incentives
- Transformation
army definido
- Dinámica
colaborativa activa
- Estrategia de
cultura de equipo
- Análisis de
evolución
- Customer Journey
- Analysis of
Channels and
touchpoints
- Redesign of the
Customer Data
Model
- DB Cliente
Integration
- Data lake
- Data Frames
- OLAP
- Data Extracting
- Data Integration
- Data Normalization
and Cleaning
- Data Validation
- ETL processes
- Programming
platform
- Machine Learning
- Visualization and
operational tools
- Algorithms (R)
- Business
Intelligence
platform
-
-
- Big Data
Observatory
- Big Data Startup
- Data Scientist
Challenges
- Connect with
Kaggle
- Identification of BD
leaders per area
- Change
Management
Program
- BD Acceleration
Program
- BD Communication
Agile Framework

44. 44
Integration
Big Data Fundamentals

45. 45Big Data Fundamentals
Technologies

46. 46
Profiles
Big Data Fundamentals

47. 47
Agile
Big Data Fundamentals

48. 48
Big Data Terms
Algorithms: Mathematical formulas or statistical processes used to analyze data. These are used in software to
process and analyze any input data.
Analytics: The process of drawing conclusions based on raw information through analysis, otherwise
meaningless data and numbers can be transformed into something useful.
Descriptive Analytics: Condensing big numbers into smaller pieces of information. This is similar to
summarizing the data story.
Predictive Analytics: Studying recent and historical data, analysts are now able to make predictions about the
future.
Prescriptive Analytics: Finally, having a solid prediction for the future, analysts can prescribe a course of
action. This turns data into action and leads to real-world decisions.
DaaS: Data-as-a-service treats data as a product. DaaS providers use the cloud to give on-demand access of
data to customers. This allows companies to get high quality data quickly.
Big Data Fundamentals

49. 49
Big Data Terms
Data Mining: Data miners explore large sets of data to find patterns and insight. This is a highly analytical
process that emphasizes making use of large datasets.
Machine Learning: An incredibly cool method of data analysis, machine learning automates analytical model
building and relies on a machine’s ability to adapt. Using algorithms, models actively learn and better
themselves each time they process new data.
SQL: Also known as Structured Query Language, this is used for the managing and stream processing of data. It
is used to communicate with and perform tasks on a database. Standard commands include “Insert,” “Update,”
“Delete,” “Create,” and “Drop.” Data appears in a relational table with rows and columns.
R: R is a horribly named programming language that works with statistical computing. It is considered one of
the more important and most popular languages in data science.
Big Data Fundamentals

50. Success Cases of BD in FMCG

51. 51
360º Media Understanding
Success Cases FMCG

52. 52
Trade Marketing
Success Cases FMCG

53. 53
Consumer Profiling
Success Cases FMCG

54. 54
Social Media
Success Cases FMCG

55. Benefits

56. 56
Survival
Impact of Big Data

57. 57Impact of Big Data

58. Nombre de Sección | Nombre de Subsección | 58
Business Benefits
IBM Research 2016
Better strategic decisions
69%
Improved Operations
54%
Customer Understanding
52%
Cost Reductions
47%

59. 59Impact of Big Data
Enables Well Informed Decions
63%
Reduces Wasted Resources
57%
Predicts Risk of Downtime
56%
Predicts Needs for Repair
51%
Detects Security Issues
47%
Improves Supply Chain
Management
46%
Predicts Workload
43%
Forecasts Staffing Needs
33%
Business Benefits
IBM Research 2016

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