The Journey to Success with Big Data

1© Cloudera, Inc. All rights reserved.
Matt Brandwein
@mattbrandwein @cloudera
The Journey to Success
with Big Data

Our relationship with data
is changing.

Data is now a strategic asset
Today, everything that can be
measured will be measured.
We all create data every day.
Machines create even more.
Today, data is the application.
We expect data to make our lives
better, but not at the cost of our
privacy.
Today, becoming data-driven is
a business imperative.
Competitive organizations
embrace agile methods.
Instrumentation Consumerization Experimentation

Data drives modern business, across industries
Increase conversion rates.
Improve product quality.
Optimize IT efficiency.
Secure the network (SIEM).
Assess risk, and detect fraud.
Retain data for compliance.
Build a 360° view of patient,
customer, employee, etc.
Grow share of wallet.
Reduce customer churn.
Customer and Channel Product and Service
Security, Risk, and
Compliance
Monetize data assets.

The journey is not easy.

60%
50%
90%
50%
By 2017,
By 2018,
of big data projects will fail to go beyond the pilot phase.
or fewer organizations will have made the cultural or
business model adjustments to benefit from big data.
of data lakes will be useless, with no business case.
of business ethics violations will be from improper
use of big data analytics.
Gartner “Predicts 2015: Big Data Challenges Move From Technology to the Organization” – November 2014

Assemble the right team.
Get the right architecture.
Adopt an agile approach.

Assemble the right team
IT
Ops, InfoSec, DBA, ETL
Data Team
BI, Analysts, Data Science
“Business” Users
Anyone
• Control costs while meeting SLAs.
• Maintain a secure environment.
• Deliver self-service where possible.
• Explore data and try new things.
• Use diverse analytic techniques.
• Need high performance and agility.
• Need insights at the right time, in real-time.
• Don’t have time or skills for advanced analysis.
• Results matter; what’s the business outcome?
Successful big data projects include representatives from all of the following groups:

Data
Sources
Data
Systems
Data
Access
Business
Analytics
Custom
Applications
Existing
Data
Databases
Operational
Applications
New Data
Limited Data
Inefficient to manage existing
data, let alone new data, while
maintaining performance at scale.
Data must be structured first.
Limited Insights
Power users struggle to analyze
complex data. Many users get
no data.
Hard to put insights into action.
Trust and Compliance
More data, more users, and
more tools create complexity.
Need to balance business agility
with security and governance.
Do we have the right architecture?
Traditional approaches
designed for structure:
• structured data
• structured analytics
• structured process

Introducing the enterprise data hub (EDH)
Powered by Apache Hadoop
A new way to work with data.
• One platform for unlimited data
• Unified, multi-framework data access
Cloudera delivers:
• Enterprise Security
• Data Governance
• Complete Management
• Open source, open standards
Security and Administration
Unlimited Storage
Process Discover Model Serve
Deployment
Flexibility
On-Premises
Appliances
Engineered Systems
Public Cloud
Private Cloud
Hybrid Cloud

Data
Sources
Data
Systems
Data
Access
Business
Analytics
Custom
Applications
Existing
Data
Databases
Operational
Applications
New Data
Enterprise Data Hub
Unlimited Storage
Manage Compliance
From risk due to regulations and
customer privacy concerns,
to trust in a secure and
compliant platform.
Unlock Value from Data
From analytics for some,
to insights for all.
Keep Unlimited Data
From disparate and limited views,
to unlimited information access.
Benefits of an enterprise data hub architecture

Adopt an agile approach
Collect, Create, Manage
unlimited data
Explore, Analyze
data in many ways
Operationalize
insights to drive action
• Inventory, prioritize, collect existing data.
• Identify new data sources that could be created.
• Instrument your physical and virtual assets.
• Start with single datasets. Connect, clean, and model.
• Enable deeper analysis and new self-service users.
• Move to multi-dataset and predictive analytics.
• Operationalize through existing BI tools and reports.
• Embed models in existing processes.
• Build new data-driven applications.
Successful big data projects break the process down into simple steps, and iterate:

Iterate to success
Real-time
ingest/serve
New data
sources
More complex
analysis
Add …
… over time
unlimited data
Explore, Analyze
data in many ways
Operationalize

Putting it together: Working with data in an EDH
unlimited data
Explore, Analyze
data in many ways
Operationalize
Agile Process Stage Data Treatment User Access
IT
Ops, InfoSec, DBA, ETL
Data Team
BI, Analysts, Data Science
“Business” Users
Anyone
1.
2.
3.
Enterprise Data Hub
Raw Data
Load, secure, govern, tag
Enriched Data
Blend, clean, structure
Trusted Data
Enable wide access

Start small. Think big.
Operational Efficiency New Business Value
Unlimited Storage
Optimize your architecture.
Reduce EDW costs,
improve performance.
Discover new insights.
Deliver true self-service BI.
Deliver real-time insights.
Build data applications.
Operational Data Store Data Discovery & Analytics Operational Analytics

What’s your data strategy?

Thank you!
Matt Brandwein
@mattbrandwein @cloudera

The Journey to Success with Big Data

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