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