The document discusses how big data technologies can be mapped to data management concepts from the Data Management Body of Knowledge (DMBOK) framework. It provides examples of how technologies like Hadoop can be used to address DMBOK functions such as data storage and operations, data integration, data quality, metadata management, data warehousing and business intelligence, data security, and data governance. The document is presented by Mike Jennings from Walgreens Boots Alliance and provides his background and expertise in enterprise architecture and information management.

1. Improving Data Management through
Utilizing Big Data:
Mapping a Technology to a Data Concept
March 10, 2015
Mike Jennings – Walgreens Boots Alliance
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2. Big Data
Defining
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Describe any voluminous amount of structured,
semi‐structured and unstructured data that has
the potential to be analyzed for information
From www.bizcubed.com.au

3. Enterprise Data Management Framework
Starting EDM Definition
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4. Enterprise Data Management Framework
Context with the DMBOK Framework
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5. Enterprise Data Management Framework
Alternative EDM FrameworkMetadata Management
Data Context
Data Model/Classification
Data Structure and Framework
Structured
Data
Management
Unstructured
Data
Management
Master Data &
Reference Data
Management
Business
Intelligence &
Data
Warehousing
Data Quality
Management
Data Security
Management
Data
Integration
Management
Data Delivery
Management
Data Governance
Policies, Processes, Standards,
Organization, and Stewardship
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8. 8
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9. DMBOK Functions & Big Data Projects
Data Storage & Operations
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The technologies and processes organizations use to maximize or improve
the performance of their data storage resources.
File system that provides the ability to store large volumes of
structured and unstructured data
Operations, resource (node), and scheduling management for
write and read to the cluster
Workflow scheduling component for data transformations
Manages services, configurations, and their synchronization
across the cluster

10. DMBOK Functions & Big Data Projects
Data Integration & Interoperability
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The combination of technical and business processes used to combine
data from disparate sources into meaningful and unified view, according
to business requirements and accepted practices.
Provides real‐time processing of data streams for monitoring
and alerts.
Provides ability to import data from a RDBMS to HDFS.
Provides ability to collect, aggregate, and move huge log files ).
into HDFS (e.g., apps, GPS, social, sensors, other).
Provides high volume fault tolerant publish & subscribe
messaging for real‐time analysis.

11. DMBOK Functions & Big Data Projects
Data Quality
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A measure of the degree to which data satisfies the information needs of its
consumers, reflects the nature and state of the real world concepts to which it
relates, is coherent within itself, and provides value in the decision‐making
processes for which it is to be utilized.
Provides relational structure to HDFS data. File
formats can be applied to data from HDFS or local file
system
Provides ability to import data from a RDBMS to
HDFS. Imported data can be constrained through
import control arguments and basic SQL execution.
Provides ability to collect, aggregate, and move huge
log files ). into HDFS (e.g., apps, GPS, social, sensors,
other). Flume agent can be use with predefined data
patterns (sinks) to ensure data format.

12. DMBOK Functions & Big Data Projects
Meta‐data
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All the physical data and knowledge about the business and technical
processes used by an organization. Meta‐data is knowledge about the
organization’s data.
Provides data lineage between data sources and the cluster
including integration with the metastore/catalog (e.g., Hive
HCatalog).
Provides relational structure to HDFS data. File formats can be
applied to data from HDFS or local filesystem

13. DMBOK Functions & Big Data Projects
Documents & Content
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The management of documents and non‐structured content found
in audio, video, email, images, etc. and the meta‐data associated
with this material
Provides ability to collect, aggregate, and move huge log files ).
into HDFS (e.g., apps, GPS, social, sensors, email, other).
Provides ability to search of data in the cluster by indexing to
enable full text search.

14. DMBOK Functions & Big Data Projects
Data Warehousing & Business Intelligence
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A data warehouse is a subject‐oriented, integrated, time‐variant and non‐volatile
collection of data in support of management's decision making process. Business
Intelligence is the collection of activities that allow an organization to analyze data
and make decisions based on facts from historical and predictive data sets.
Provides fast big table access to large quantities of data
typically on top of the cluster.
Provides compute algorithm typically used to produce output data
from a large volume of data in the cluster for consumption.
Provides semantic layer for accessing data in the cluster.
Provides a enhanced compute approach typically used to produce
output data from a large volume of data in the cluster for consumption.
Provides a in‐memory compute method typically used to produce
output data from a large volume of data in the cluster for consumption
(e.g., machine learning algorithms).

15. DMBOK Functions & Big Data Projects
Data Security
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Data security concerns the protection of data from accidental or intentional
but unauthorized modification, destruction or disclosure through the use of
physical security, administrative controls, logical controls, and other
safeguards to limit accessibility.
Provides security authorization (grant/revoke), policy
administration, and audit for the cluster.
Provides service level authorization for users/groups.
Provides semantic layer (table) for accessing data in the cluster
that can be secured.

16. DMBOK Functions & Big Data Projects
Data Governance – Potential Opportunity Areas
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Provides the organizational oversight, processes and methods to
effectively manage data as an asset across the organization
Provides data lineage between data sources and the cluster
including integration with the metastore/catalog (e.g., Hive
HCatalog).
Provides relational structure to HDFS data. File formats can be
applied to data from HDFS or local filesystem
Provides ability to search of data in the cluster by indexing to
enable full text search.
Provides security authorization (grant/revoke), policy
administration, and audit for the cluster.

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Data about core business entities and concepts, independent of
transactions, and data that defines the set of permissible values to be
used by other data fields
DMBOK Functions & Big Data Projects
Reference & Master Data – Potential Opportunity Areas
Provides ability to import data from a RDBMS to HDFS.
Provides semantic layer for accessing data in the cluster.

18. Bio
Michael Jennings
Senior Director, Enterprise Data Architecture
Walgreens Boots Alliance
1419 Lake Cook Road, MS: L497
Deerfield, IL 60015 USA
847 964 7692
Mike.Jennings@Walgreens.com
www.linkedin.com/in/micahelfjennings
Michael Jennings is a recognized industry expert in enterprise architecture and information
management with more than twenty-five years of experience in various industries. Mike speaks
frequently on enterprise architecture and information management concepts and practices at major
industry conferences.
He is a co-author of the book "Universal Meta Data Models" (2004) and a contributing author to the
books "Building and Managing the Meta Data Repository" (2000) and “The DAMA Guide to the Data
Management Body of Knowledge - DMBOK” (2009).
Mike was recognized with the 2013 DAMA International Professional Achievement Award and as
one of Information Management Magazine’s 25 Top Information Managers for 2012.
He currently serves as VP of Programs for the Wisconsin DAMA Chapter and as VP of Operations
for DAMA International.
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