Latest Big Data technologies are allowing businesses to process ever growing volumes of data. But it is data quality, not quantity that's key to maximizing insight with Big Data analytics.
Is it possible to orchestrate and apply governance to all of that data and deliver it in a way that it can easily be consumed by the end user?
Learn how to take the uncertainty out of the data foundation for analytics by turning raw data into relevant and actionable information.
2. 2
The story of information democratization
Mass media
(passive Consumer)
Internet
(active consumer)
Web 2.0
and Social Networks (prosumer)
3. 3
Information democratization in our organizations
Reporting
(passive Consumer)
Business Intelligence
(active consumer)
Big Data
Analytics (prosumer)
4. 4
BI as we believe it should goNow we have the building blocs
From static reports to information discovery
Analytic apps rather than reports and queries
Self Service without constraints
Predictive analytics and machine learning
Manage the long tail of information
Data Lakes
Business user
Advanced users
(Data scientists)
IT teams
BigDataData
Discovery
DataViz
Embedded
Analytics
5. 5
Organization struggle to turn data into action
But there is still a huge content gap
Of customer contact
Data is inaccurate
25%
Of contact Data has
at 1+ change each year
71%
Sources: Ventana Research, Sirius Decision, Integrate, Experian,Gartner, Privacy Clearinghouse, Target Marketing, Forbes insights
Personal records compromised
in the US since 2005
534M
Time spend to preparing and quality
proofing data for analysis.
14M$
34
Data sources make-up
“customer data”
Of marketing orgs do not have a
customer 360° view across channels
Veracity
Volatility
Accuracy
compliance
Accessibility
Productivity
45%
65%
6. 6
From a fully IT driven model…
…to a federated and collaborative
responsibility model
IT Lines of
Business
Evolutionpath
From Data Management… …to Information Governance
Governance models are needed…
7. 7
… Together with the right set of tools
Data
Inventory
Data
Prepa-
ration
Master
Data
Mgmt.
Data
Integra-
tion
Access the data.
Document & reuse.
Discover the Data.
Augment and connect.
Productize the
Data.
Sanction the Data.
Share and monitor.
8. 8
Data Curator (Data steward)
Turns disparate data sources into a
sanctioned asset
Accountable of the shared content
Ensures that the data is reusable,
documented, secured…
Empowering the leaders of the Data Driven
Organization
Line of Business User (Marketing, Finance, Operations, HR, …)
Deals with data every day…
No IT skill, heavy user of ad hoc tools especially Excel
Agile in Excel data crunching , macros & scripting, pivot
tables…
Data Architect (IT)
Collects and ingests raw data, distribute
to the Enterprise
Empowers users with a data self-service
Integration Cloud Citizen
Data integration is part of her daily job
e.g.. Integrate 3rd party leads/contacts
into CRM.
Data prep as prerequisite to Integration
App Developer
Uses APIs to incorporate relevant, timely, trusted
data into applications
9. 9
The 3 pillars of the Information Refinery
Ingest data as it comes
Classify and prioritize based on relevance
Discover the data
Create and test the models
Turn information into a trusted and shared asset
Deliver Actionable insights
The Information
Refinery
The Data Lab
The Data Lake/
Reservoir
10. 10
From Customer data into a golden record that makes every click personal
Turning raw data into actionable information.
Transform customer facing tasks into
data driven process :
1. Collect data across touch-points
2. Transform into a 360°view
3. Turn data into insights with segments,
scores, forecasts and recommendations
4. Connect in real-time with your customer
and take action
Customer Data Platform
System of
interaction
System
of record
System
of insight
System
of engagement
Legacy
Systems
ERP
CRM
Cloud
Apps
Internet
of Things
Web Logs
NoSQL
Predictive
analysis
Inbound/Outbound
campaigns
Customer Facing
Devices
CRM
E-commerce
Social
Networks
Machine
Learning
Recommendatio
Analytics