3 джозеп курто превращаем вашу организацию в big data...
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Transcript of 3 джозеп курто превращаем вашу организацию в big data...
Transforming your organization into a Big
Data companyBIG DATA | Digital October 2015
Josep Curto | Professor, IE Business School | CEO, Delfos Research
2
Agenda
Digital disruption (or why Big Data is one of the key strategies for your organization)
Business Models (The multiple personalities of Big Data)
Implementing Big Data (The moment of truth)
12
Based on…
S (Social)
M (Mobile)
A (Analytics)
C (Cloud)
helping to create
personal customer
experiences
14
Challenge 1: Capture
Source: Bitly http://blog.bitly.com/post/9887686919/you-just-shared-a-link-how-long-will-people-pay
Faster data, reduce lifespan
15
Challenge 2: Storage
0.8$ZB$
*Ze#abyte:)1,000,000,000,000,000,000,000)bytes)(#1#trillion#gigabytes)#
2009$
2020$
40##Ze5abytes*#
Source: IDC IDC Digital Universe Study, 2012, Sponsored by EMC
More data! what is relevant?
16
Challenge 3: Analisis
The more sources we have, the more complex to extract the value
Source: IDCIDC Digital Universe Study, 2012, Sponsored by EMC
2011: 50.07 Tb/s
2012: 86.54 Tb/s
Data in transit: 856 Tb/s
17
Challenge 4: Visualization
New data sources and formats and increased volume need new communication techniques
22
Veloc
ity Variety
Volume
· Batch· Near Real Time· Real Time· Streaming
· Structured· Semi-structured· Unstructured
Volume + Variety
Volume + Velocity
Velocity + Variety
· Terabyte· Petabyte· Exabyte· Zettabyte· Yottabyte
Volume + Velocity + Variety
25
Big Data is not new
KB
Files
Statistics
COBOL
GB
Tables
OLAP Cubes
SQL
TB
Semi-structured
Apps
XML
PB
Dynamic Variety
Mahout (& other)
NoSQL
Big
Data
Analítica
Language
60s 80 - 96 97 - 07 07 - ?
26
3Vs are not enough
Veloc
ity Variety
Volume
· Batch· Near Real Time· Real Time· Streaming
· Structured· Semi-structured· Unstructured
Volume + Variety
Volume + Velocity
Velocity + Variety
· Terabyte· Petabyte· Exabyte· Zettabyte· Yottabyte
Volume + Velocity + Variety
• Horizontal scalability • Relational constraints
27
Relational Constraints
Non Relational Schema-free
Distributed 4 Types: Key-value, column-oriented, graph, document
Relational Data is normalised and
static Relational schema
Data in one repository
32
Big Data
RDBMS
Data Visualization
Predictive Analytics
Hadoop +
Content Search & Analytics
In-memory Streaming Technologies
Object & Graph Databases
It is not the only one
33
We are moving from…
Information Systems
Data
Mobile Data
Machine Data
Social Media Data
Audio, video, text
Stream Data
Sou
rces
Corporate Information Factory / Data Warehousing
Stor
age
&
Pro
cess
ing
Information Management
Data Governance Master Data ManagementD
ata
Man
agem
ent
Ana
lysi
s
Analytics Operational Intelligence
Business Intelligence
34
to a new architecture
Information Systems
Data
Mobile Data
Machine Data
Social Media Data
Audio, video, text
Stream Data
Sou
rces
Corporate Information
Factory / Data WarehousingSt
orag
e &
P
roce
ssin
g
Information Management
Data Governance Master Data ManagementD
ata
Man
agem
ent
Ana
lysi
s
Analytics Operational Intelligence
Business Intelligence
Big Data
NoSQL In-memory MPP HPC
Data Products
36
The Big Data market is growing
Source: IDCIDC Worldwide Big Data Technology and Services 2010 – 2015 Forecast, March 2013
0
10
20
30
40
2011 2012 2013 2013 2015 2016 2017
32.4
25.7
20.4
16.1
12.69.8
7.4
37
Hadoop is leading the way
1991 1992 1994 1995 1997 2000
Project emerge
Community creation
Code is available,
community grows
First companies
Ecosystem emergence Mainstream,
M&A Starts
2006 2007 2008 2009 2012 2015
41
But our expectations are high
Fuente: GartnerHype Cycle for Big Data,2012
Fuente: GartnerGartner HypeCycle 2013
54
Data-driven Business Models
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
Data-driven Business
ModelKey Activities
Customer Segment
Revenue Model
Key Resources Cost Structure
55
Data
Synthesizing the different sources leads to the taxonomy
14
Data Sources
Internal existing data
Self-generated Data
External
Acquired Data
Customer provided
Free available
Open Data
Social Media data
Web Crawled Data
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
56
ActivitiesDimension: Activities
15
Key Activity
Data Generation Crowdsourcing
Tracking & Other Data Acquisition
Processing
Aggregation
Analytics
descriptive
predictive
prescriptive Visualization
Distribution
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
57
Value proposition
Dimension: Offering
16
Offering
Data
Information/Knowledge
Non-Data Product/Service
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
58
Revenue modelDimension: Revenue Model
17
Revenue Model
Asset Sale
Lending/Renting/Leasing
Licensing
Usage fee
Subscription fee
Advertising
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
59
Customers
Dimension: Target Customer
18
Target Customer B2B
B2C
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
60
Some types are emergingThe 6 BM types are characterised by the key activities and key data sources
29
Type F
Type A Type D
Type E Type B
Type C
Aggregation Analytics Data generation Fr
ee
ava
ilabl
e Cu
stom
er
prov
ided
Tr
acke
d &
ge
nera
ted
Key activity
Key
Data
Sou
rce
6 significant Business Model types were identified
28
Type B: “Analytics-as-a-Service”
Type C: “Data generation & Analytics”
Type D: “Free Data Knowledge Discovery”
Type A: “Free Data Collector & Aggregator”
Type E: “Data Aggregation-as-a-Service”
Type F: “Multi-Source data mashup and analysis”
Source: University of CambridgeA Taxonomy of Data-driven Business Models used by Start-up Firms
61
One example: CBInsights
Data-driven Business Model
Data Processing, Aggregation &
Analysis
Investors, media
Subscription fee
External Companies Data
Platform, data scientists
63
Big Data around the world
Credit Suisse Netflix
TescoWalMart
General Motors
Disney Metlife
Apple
Caesars Entertainment SpotifyHouston Rockets NFL
Many reasons to fail
IMPLEMENTATION FAILURE
COST
OPERATIONAL STRATEGIC
Lack of top management commitment
Unavailability of subject matters
experts
Unavailability of key users for
UTA
Poor quality of testing
Poor Knowledge
Transfer
Cost Overrun
Unrealistic ROI
TECHNOLOGY PEOPLE
Poor Data Quality
Over Customization
Inadequate data sources knowledge
Poor IT infrastructure
Poor ETL Quality
Poor BI product selection
User resistance To change
High rotation of Project team members
Inadequate resources
Poor user involvement
TACTIC
Inadequate training and education
Non-empowered decision-makers
Poor departmental
alignment
Inappropriate timing to go live
Poor communication
Unrealistic expectations
Inadequate functional
requirements
Inadequate project team composition
Poor project management
Unrealistic project scheduling Ineffective organizational
change management
74
Framing the problem
• Identify business needs • Conceptualise business opportunities • Determine Big Data type • Define Big Data Strategy
Pilot and beyond
• Develop model • Identify data set • Build / Buy / Subscribe to big data
architecture • Create pilot • Scale pilot
81
What we want
TimeTime to action
Lost
val
ue
Data latency
Analysis latency
Decision-making latency
Business Event
Data ready for analysis
Information
Action
Business Value
82
When we need themBI (mature) BA (mature) Big Data
(emerging)
Tools Query, reporting, OLAP, alerts Forecasting, regression and modeling and/ or BI
Machine learning, visualization
FocusWhat happened, how many, how often, what is the problem, what
action is needed
Why is this happening, what if these trends continue, what will happen
next, what is the best that can happen
Capture, storing and analyzing data: all
Use Reactive Proactive / Predictive / Prescriptive All / none
Types of data
structured Structured / semi-structured All
Data Complexity
Low Low / medium High
Scope Management Processes Vertical / processes
84
Traditional Knowledge, Literacy and Skills
Computer Literacy
Analytic Proficiency
Data Proficiency
Operational Proficiency
Total Information Proficiency
Building traditional capabilities and skills
Mastering technology
Automating clerical work
Reengineering business processes
Building ubiquitous knowledge bases
Optimizing all decisions
We want to create digital competencies
85
We need to measure the value (McDonald 2004)
Valu
e C
reat
ed
Overall Success of the Initiative
Implementation Success
• On- time • On-budget
User Success• User adoption • User Satisfaction • Data Problems
Operational Success• Productivity
Improvements • Process efficiency and
effectiveness • Key Performance
Indicators
Business Success• Return on Investment • Economic Value Add • Revenue increases • Cost Savings • Customer / Corporate
profits • Enables Business
Strategy and Competitive Advantage
• Create a formal, continuos process for measuring success and value generated
• Identify and measure results of each project phase• Establish realistic goals and expectations based on
capability / maturity