Defining and Applying Data Governance in Today’s Business Environment

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Defining and Applying Data Governance in
Today’s Business Environment
Joe Caserta
President
Caserta Concepts
December 8-12, 2014
The Westin Beach Resort
Ft. Lauderdale, Florida

Top 20 Big Data
Consulting - CIO Review
Joe Caserta Timeline
Launched Big Data practice
Co-author, with Ralph Kimball, The
Data Warehouse ETL Toolkit (Wiley)
Dedicated to Data Warehousing,
Business Intelligence since 1996
Began consulting database
programing and data modeling 25+ years hands-on experience
building database solutions
Founded Caserta Concepts in NYC
Web log analytics solution published
in Intelligent Enterprise
Formalized Alliances / Partnerships –
System Integrators
Partnered with Big Data vendors
Cloudera, Hortonworks, IBM, Cisco,
Datameer, Basho more…
Launched Training practice, teaching
data concepts world-wide
Laser focus on extending Data
Warehouses with Big Data solutions
1986
2004
1996
2009
2001
2010
2013
Launched Big Data Warehousing
(BDW) Meetup-NYC ~1500 Members
2012
2014
Established best practices for big
data ecosystem implementation –
Healthcare, Finance, Insurance
Top 20 Most Powerful
Big Data consulting firms
Dedicated to Data Governance
Techniques on Big Data (Innovation)

Enrollments
Claims
Finance
ETL
Ad-Hoc Query
Horizontally Scalable Environment - Optimized for Analytics
Big Data Cluster
Canned Reporting
Big Data Analytics
NoSQL
Databases
ETL
Ad-Hoc/Canned
Reporting
Traditional BI
Mahout MapReduce Pig/Hive
N1 N2 N4N3 N5
Hadoop Distributed File System (HDFS)
Traditional
EDW
Others…
Today’s business environment requires Big Data
Data Science

Innovation is the only sustainable
competitive advantage a company can have.

Why is Big Data Governance Important?
 Convergence of
 Data quality
 Management and policies
 All data in an organization
 Set of processes
 Ensures important data assets are formally managed throughout the
enterprise.
 Ensures data can be trusted
 People made accountable for low data quality
It is about putting people and technology in place to fix and
prevent issues with data so that the enterprise can become
more efficient.

•Data is coming in so
fast, how do we
monitor it?
•Real real-time
analytics
•What does
“complete” mean
•Dealing with sparse,
incomplete, volatile,
and highly
manufactured data.
How do you certify
sentiment analysis?
•Wider breadth of
datasets and sources
in scope requires
larger data
governance support
•Data governance
cannot start at the
data warehouse
•Data volume is
higher so the process
must be more reliant
on programmatic
administration
•Less people/process
dependence
Volume Variety
VelocityVeracity
The Challenges With Governing Big Data

What’s Old is New Again
 Before Data Warehousing Data Governance
 Users trying to produce reports from raw source data
 No Data Conformance
 No Master Data Management
 No Data Quality processes
 No Trust: Two analysts were almost guaranteed to come up
with two different sets of numbers!
 Before Big Data Governance
 We can put “anything” in Hadoop
 We can analyze anything
 We’re scientists, we don’t need IT, we make the rules
 Rule #1: Dumping data into Hadoop with no repeatable process, procedure, or
data governance will create a mess
 Rule #2: Information harvested from an ungoverned systems will take us back to
the old days: No Trust = Not Actionable

Making it Right
 The promise is an “agile” data culture where communities of users are encouraged
to explore new datasets in new ways
 New tools
 External data
 Data blending
 Decentralization
 With all the V’s, data scientists, new tools, new data we must rely LESS on HUMANS
 We need more systemic administration
 We need systems, tools to help with big data governance
 This space is EXTREMELY immature!
 Steps towards Big Data Governance
1. Establish difference between traditional data and big data governance
2. Establish basic rules for where new data governance can be applied
3. Establish processes for graduating the products of data science to
governance
4. Establish a set of tools to make governing Big Data feasible

Process Architecture
Communication
Organization
IFP
Governance
Administration
Compliance
Reporting
Standards
Value Proposition
Risk/Reward
Information
Accountabilities
Stewardship
Architecture
Enterprise Data
Council
Data Integrity
Metrics
Control Mechanisms
Principles and
Standards
Information Usability
Communication
BDG provides vision, oversight and accountability for leveraging
corporate information assets to create competitive advantage,
and accelerate the vision of integrated delivery.
Value Creation
• Acts on Requirements
Build Capabilities
• Does the Work
• Responsible for adherence
Governance
Committees
Data Stewards
Project Teams
Enterprise
Data Council
• Executive Oversight
• Prioritizes work
Drives change
Accountable for results
Definitions
Big Data Governance Organization

•This is the ‘people’ part. Establishing Enterprise Data Council,
Data Stewards, etc.Organization
•Definitions, lineage (where does this data come from),
business definitions, technical metadataMetadata
•Identify and control sensitive data, regulatory compliancePrivacy/Security
•Data must be complete and correct. Measure, improve,
certify
Data Quality and
Monitoring
•Policies around data frequency, source availability, etc.Business Process Integration
•Ensure consistent business critical data i.e. Members,
Providers, Agents, etc.Master Data Management
•Data retention, purge schedule, storage/archiving
Information Lifecycle
Management (ILM)
Components of Data Governance
• Add Big Data to overall framework and assign responsibility
• Add data scientists to the Stewardship program
• Assign stewards to new data sets (twitter, call center logs, etc.)
• Graph databases are more flexible than relational
• Lower latency service required
• Distributed data quality and matching algorithms
• Data Quality and Monitoring (probably home grown, drools?)
• Quality checks not only SQL: machine learning, Pig and Map Reduce
• Acting on large dataset quality checks may require distribution
• Larger scale
• New datatypes
• Integrate with Hive Metastore, HCatalog, home grown tables
• Secure and mask multiple data types (not just tabular)
• Deletes are more uncommon (unless there is regulatory requirement)
• Take advantage of compression and archiving (like AWS Glacier)
• Data detection and masking on unstructured data upon ingest
• Near-zero latency, DevOps, Core component of business operations
For Big Data

Big Data Governance Realities
 Full data governance can only be applied to “Structured” data
 The data must have a known and well documented schema
 This can include materialized endpoints such as files or tables OR
projections such as a Hive table
 Governed structured data must have:
 A known schema with Metadata
 A known and certified lineage
 A monitored, quality test, managed process for ingestion and
transformation
 A governed usage  Data isn’t just for enterprise BI tools anymore
 We talk about unstructured data in Hadoop but more-so it’s semi-
structured/structured with a definable schema.
 Even in the case of unstructured data, structure must be
extracted/applied in just about every case imaginable before analysis
can be performed.

The Data Scientists Can Help!
 Data Science to Big Data Warehouse mapping
 Full Data Governance Requirements
 Provide full process lineage
 Data certification process by data stewards and business owners
 Ongoing Data Quality monitoring that includes Quality Checks
 Provide requirements for Data Lake
 Proper metadata established:
 Catalog
 Data Definitions
 Lineage
 Quality monitoring
 Know and validate data
completeness

Big
Data
Warehouse
Data Science Workspace
Data Lake – Integrated Sandbox
Landing Area – Source Data in “Full Fidelity”
The Big Data Governance Pyramid
Metadata  Catalog
ILM  who has access,
how long do we
“manage it”
Raw machine data
collection, collect
everything
Data is ready to be turned into
information: organized, well
defined, complete.
Agile business insight through data-
munging, machine learning, blending
with external data, development of
to-be BDW facts
ILM  who has access, how long do we
“manage it”
Data Quality and Monitoring 
Monitoring of completeness of data
ILM  who has access, how long do we “manage it”
Data Quality and Monitoring  Monitoring of
completeness of data
 Hadoop has different governance demands at each tier.
 Only top tier of the pyramid is fully governed.
 We refer to this as the Trusted tier of the Big Data Warehouse.
Fully Data Governed ( trusted)
User community arbitrary queries and
reporting

People, Processes and Business commitment is still critical!
 - (Incubating) promises many of the features
we need, however is fairly immature (Version 0.5).
Recommendation: Roll your own custom lifecycle management
workflow using Oozie + retention metadata
The Information Lifecycle Part of Big Data
Caution: Some Assembly Required
The V’s require robust tooling:
 Unfortunately the toolset is pretty
thin: Some of the most hopeful tools
are brand new or in incubation!
 Components like ILM have fair
tooling, others like MDM and Data
Quality are sparse

Master Data Management
 Traditional MDM will do depending on your data size and
requirements:
 Relational is awkward, extreme normalization, poor usability and
performance
NoSQL stores like HBase has benefits
 If you need super high performance low millisecond response times to
incorporate into your Big Data ETL
 Flexible Schema
 Graph database is near perfect fit. Relationships and graph analysis bring
master data to life!
Data quality and matching processes are required
Little to no community or vendor support
More will come with YARN (more Commercial and Open Source IP
will be leveragable in Hadoop framework) -
Recommendation: Buy + Enhance or Build.

Data
User
Interface
Services
WorkflowRules
Security
Members Providers
Agents Plans
Policies
Consistent Policy
Enforcement and Security
Integration with exiting
ecosystem
Data Governance through
Workflow Management
Data Quality enforcement
through metadata-driven
rules
Time-Variant Hierarchies
and attributes
High Performance, Flexible,
Scalable Database – Think
Graph!
Master Data Management Components

Staging
Library
Consolidated
Library
Standardization Matching
Integrated
Library
Survivorship
Source ID Name Home Address Birth Date SSN
SYS A 123 Jim Stagnitto 123 Main St 8/20/1959 123-45-6789
SYS B ABC J. Stagnitto 132 Main Street 8/20/1959 123-45-6789
SYS C XYZ James Stag NULL 8/20/1959 NULL
Source ID Name Home Address Birth Date SSN Std Name Std Addr MDM ID
SYS A 123 Jim Stagnitto 123 Main St 8/20/1959 123-45-6789 James Stagnitto 123 Main Street 1
SYS B ABC J. Stagnitto 132 Main Street 8/20/1959 123-45-6789 James Stagnitto 132 Main Street 1
SYS C XYZ James Stag NULL 8/20/1959 NULL James Stag NULL 1
MDM ID Name Home Address Birth Date SSN
1 James Stagnitto 123 Main Street 8/20/1959 123-45-6789
Mastering Customer and Provider Data
Validation

The Reality of Mastering Data

Graph Databases (NoSQL) to the Rescue
 Hierarchical relationships are never
rigid
 Relational models with tables and
columns not flexible enough
 Neo4j is the leading graph database
 Many MDM systems are going graph:
 Pitney Bowes - Spectrum MDM
 Reltio - Worry-Free Data for Life Sciences.

Securing Big Data
 Determining Who Sees What:
 Need to be able to secure as many data types as possible
 Auto-discovery important!
 Current products:
 Sentry – SQL security semantics to Hive
 Knox – Central authentication mechanism to Hadoop
 Cloudera Navigator – Central security auditing
 Hadoop - Good old *NIX permission with LDAP
 Dataguise – Auto-discovery, masking, encryption
 Datameer – The BI Tool for Hadoop
Recommendation: Assemble based on existing tools

• For now Hive Metastore, HCatalog + Custom might be best
• HCatalog gives great “abstraction” services
• Maps to a relational schema
• Developers don’t need to worry about data formats and
storage
• Can use SuperLuminate to get started
Recommendation: Leverage HCatalog + Custom metadata tables
Metadata

They gave
developers and data
scientists a reason to
use it:
• Easy to use storage
handlers
• Automatic partitioning
• Schema backwards
compatibility
• Monitoring and
dependency Checks
The Twitter Way
 Twitter was suffering from a data science wild west.
 Developed their own enterprise Data Access Layer (DAL)

Data Quality and Monitoring
 To TRUST your information a robust set of tools for continuous
monitoring is needed
 Accuracy and completeness of data must be ensured.
 Any piece of information in the Big Data Warehouse must have
monitoring:
 Basic Stats: source to target counts
 Error Events: did we trap any errors during processing
 Business Checks: is the metric “within expectations”, How
does it compare with an abridged alternate calculation.
Large gap in commercial projects /open source project offerings

Data Quality and Monitoring Recommendation
DQ
metadata
Hive
Pig
MR
Quality
Check
Builder
DQ
Notifier
and
Logger
DQ
Events and
Timeseries
Facts
DQ ENGINE
• BUILD a robust data quality
subsystem:
• HBase for metadata and error
event facts
• Oozie for orchestration
• Based on Data Warehouse ETL
Toolkit

Closing Thoughts – Enable the Future
 Today’s business environment
requires the convergence of data
quality, data management, data
engineering and business policies.
 Make sure your data can be trusted
and people can be held accountable
for impact caused by low data
quality.
 Get experts to help calm the
turbulence… it can be exhausting!
 Blaze new trails!
Polyglot Persistence – “where any decent
sized enterprise will have a variety of different
data storage technologies for different kinds of
data. There will still be large amounts of it
managed in relational stores, but increasingly
we'll be first asking how we want to manipulate
the data and only then figuring out what
technology is the best bet for it.”
-- Martin Fowler

Thank You
Joe Caserta
President, Caserta Concepts
joe@casertaconcepts.com
(914) 261-3648
@joe_Caserta

Defining and Applying Data Governance in Today’s Business Environment

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