AWC Career Bootcamp- August 21, 2013

Careers in Data Management 101
AWC Career Bootcamp
Seattle, WA
August 21, 2103
Patricia A Gilson
Principal
PAG Systems, LLC
8/21/2013http://dama-ps.org
1

Disclaimer of Following Material
• This is a very high overview of the different careers in
data management. In no way is the material contained
here exhaustive but rather it is meant just to be an
overview of the different roles in data management.
2

Agenda and Talking Points
• What is Data Management?
• An Overview of Data
• What are Data Management Subject Areas?
• What are Data Management Roles?
• What Careers are in Data Managements?
• How to Can I Get a Career Data Management?
• How Can DAMA – Puget Sound Chapter Help Me in My
Career Goals?
3

Who Am I?
PatriciaAGilson
• Principal at PAG Systems, LLC
o A Technology-Based Company in Business and Enterprise
Architecture www.pagsystems.com
• VP of Marketing of DAMA-PS Chapter, DAMA
International
o An association of Data Management Professionals and
Practitioners www.dama-ps.org
• Member of Advisory Board for UW BI Certification
o Continuing Education Program in Business Intelligence
http://www.pce.uw.edu/certificates/business-intelligence-
decision-making.html
4

Data Management Is:
• According to the definition provided in the DAMA Data
Management Body of Knowledge (DAMA-DMBOK) is:
"Data management is the development, execution and
supervision of plans, policies, programs and practices
that control, protect, deliver and enhance the value of
data and information assets.”
5

Data is Everywhere
It is created with every purchase or transaction by a
consumer.
It is created every time someone surfs the web, opens a
new account with a bank or utility company.
It is created by your employer or your school.
6

Data is Collected and Regulated for
Security
• Companies
 Clothes
 Grocery
 Telecommunications
 Car
• Agencies
 Government
• Institutions
 Educational
7

What is Done with Collected Data
• Retail companies use it to determine consumer habits and forecast future
sales
• Companies use it to study past sales and/or existing sales
• Companies use it to gathering information on specific groups
• Customer Service departments use it for better service
• Companies use it to determine which age groups to target or what to type
of marketing campaigns to create
• The Government collects data from the census and tabulates it to see
measure population growth
• Car insurance companies use the number of accidents by each age group to
determine risks and how much to charge them
• Hospitals collect data to see how they are performing with infection rates or
performance rates
8

This Data is Collected in Databases
•First it’s stored in Transactional Databases
•Then it’s moved to Data Warehouses and
Operational Data Stores
•Then it’s reported on for marketing, trending and
sales forecasting
9

Subject Areas of Data
• Operational Databases
• Data Warehouses
• Database Administration
• Data Governance
• Information Security
• Data Quality
• Master Data Management
• Reference Management
• Metadata Management
• Data Integration
• Data Virtualization
• Data Analysis
10

Operational Databases
• Operational databases allow a business to enter, gather,
and retrieve large quantities of specific information, such
as training status, personal employee information, sales,
customer complaints, and previous proposal information.
Storing information in a centralized area can increase
retrieval time for users. An important feature of storing
information in an operational database is the ability to
share information across the company. Operational
databases can be used to monitor activities, to audit
suspicious transactions, or to review the history of
dealings with a particular customer. They can also be part
of the actual process of making and fulfilling a purchase,
for example in e-commerce.
11

Data Warehouses
• A Data Warehouse (DW, DWH, or EDW) is a central repository that is
created by integrating data from one or more disparate sources. They
store current as well as historical data and are used for creating trending
reports for various department reporting such as annual and quarterly
comparisons. The typical DW uses staging, data integration, and access
layers to house its key functions. The staging layer or staging database
stores raw data extracted from each of the disparate source data
systems. The integration layer integrates the disparate data sets by
transforming the data from the staging layer often storing this
transformed data in an operational data store (ODS) database. The
integrated data are then moved to yet another database, often called
the data warehouse database, where the data is arranged into
hierarchical groups often called dimensions and into facts and aggregate
facts. The combination of facts and dimensions is sometimes called
a star schema. The access layer helps users retrieve data.
12

Database Administration
• Databases hold valuable and mission-critical data.
Database administration is the function of managing and
maintaining database management systems (DBMS)
software and hardware. Database administration work,
usually performed by DBAs, is complex, repetitive, time-
consuming and requires maintenance 24/7 to keep
systems running and current.
13

Data Governance
• Data governance is a set of processes that ensures that
important data assets are formally managed throughout
the enterprise. Data governance ensures that data can be
trusted and that people can be made accountable for any
adverse event that happens because of low data quality.
14

Information Security
• Information security concerns the use of a broad range of controls to
protect the databases against data corruption and/or loss caused by
the entry of invalid data or commands, mistakes in database or
system administration processes, and sabotage/criminal damage.
The security protections encompasses the data, the applications or
stored functions, the DBMS, the servers and the associated network
links. It also protects against compromises of confidentiality,
integrity, and availability, and involves various types or categories
of controls, such as technical, procedural/administrative and
physical.
15

Data Quality
• Quality of data in general is crucial to decision-making and
planning, as well as needed for valid business processes. Data
is deemed high quality if it correctly represents the real-world
construct to which they refer. As data volume increases, the
question of internal consistency within data becomes
paramount, regardless of fitness for use for any external
purpose. The aim of building a data warehouse is to have
an integrated, single source of data that can be used to
make business decisions. Since the data is usually
sourced from a number of disparate systems, it is
important to ensure that the data is standardized and
cleansed before loading into the data warehouse.
16

Master Data Management
• Master Data Management (MDM) strategies ensure that an
organization does not use multiple and potentially inconsistent,
versions of the same data in different parts of its operations.
With MDM, the process of record linkage is used to associate
different records that correspond to the same entity. It also
addresses issues with data quality, consistent classification and
identification of data, and data-reconciliation issues. The MDM
hub, where the ‘single source of data’ is stored is used to
synchronize the disparate source master data, the managed
master data extracted from the master data management hub is
again transformed and loaded into the disparate source data
system as the master data is updated.
17

ReferenceData Management
• Reference Data is data from outside the organization
(often from standards organizations) which is, apart from
occasional revisions, static. This non-dynamic data is
sometimes also known as "standing data“ because it
changes so slowly. Examples would be zip codes, country
names and other data that defines the master data.
Management of this data is essential to keep data
current and correct.
18

Metadata Management
• Metadata Management can be defined as the end-to-
end process and governance framework for creating,
controlling, enhancing, attributing, defining and
managing a metadata schema, model or other structured
aggregation system, either independently or within a
repository and the associated supporting processes
which is used often to enable the management of
content. For web-based systems, URLs, images, video etc.
may be referenced from a triples table of object,
attribute and value.
19

Data Integration
• Data Integration is the process that involves
combining data residing in different sources and
providing users with a unified view of these data. This
process becomes significant in a variety of situations,
which include both commercial (when two similar
companies need to merge their databases) and scientific
(combining research results from different bioinformatics
repositories) domains. Other than merging data, it’s
used for creating data warehouse systems.
20

Data Virtualization
• Data virtualization is used to describe any approach to data
management that allows an application to retrieve and
manipulate data without requiring technical details about the
data, such as how it is formatted or where it is physically
located. It does not attempt to impose a single data model on
the data (heterogeneous data) and also supports the writing
of transaction data updates back to the source systems. This
concept and software is a subset of data integration and is
commonly used within business intelligence, service-oriented
architecture data services, cloud computing, enterprise
search, and master data management.
21

Data Analysis
• Analysis of data is the process of inspecting, cleaning,
transforming, and modeling data with the goal of
discovering useful information, suggesting conclusions,
and supporting decision making. Data analysis has
multiple facets and approaches, encompassing diverse
techniques under a variety of names, in different
business, science, and social science domains.
22

Data Analysis - continued
• A form of data analysis is data mining, which is a particular
technique that focuses on modeling and knowledge discovery
for predictive rather than purely descriptive purposes.
• Another form of data analysis is Business intelligence, which
relies heavily on aggregation, focusing on business
information.
• In statistical applications, some people divide data analysis
into descriptive statistics, exploratory data analysis that
focuses on discovering new features in the data,
and confirmatory data analysis that confirms or falsifies
existing hypotheses.
23

Data Analysis - continued
• Predictive analytics focuses on application of statistical or
structural models for predictive forecasting or
classification, while text analytics applies statistical,
linguistic, and structural techniques to extract and
classify information from textual sources, a species
of unstructured data. All are varieties of data analysis.
• Data integration is a precursor to data analysis, and data
analysis is closely linked to data visualization and data
dissemination.
24

Data Management Roles
• Data Stewards and Data Custodians
•Analysts - Data, Business, Functional, Data
Quality, Data Scientists and Data Mining
• Architects and Modelers
• Data Security Managers
•DatabaseAdministrators
•Integration Specialists
25

Data Stewards or Data Custodian
• Are owners of the data and are the Subject Matter
Experts (SME) for that data and ensures that each
assigned data element:
 Has clear and unambiguous value definition
 Does not conflict with other data elements
 Has adequate documentation on appropriate usage and
notes
26

Analysts
• Data analysts will inspect, clean, transform, and
analyze data with the goal of discovering useful information,
suggesting conclusions, and supporting decision making.
• Functional and Business Analysts analyzes the existing or ideal
organization and design of systems, including businesses,
departments, and organizations.
• Systems Analysts researches problems, plans solutions,
recommends software and systems, at least at the functional
level, and coordinates development to meet business or other
requirements.
27

Analysts-continued
• Data Quality Analysts ensure state of completeness, validity,
consistency, timeliness and accuracy that makes data
appropriate for a specific use
• Data Mining Analysts focus on modeling and knowledge discovery
for predictive rather than purely descriptive purposes.
• Data scientists seek to use all available and relevant data to
effectively tell a story that can be easily understood by non-
practitioners. That can include varying elements, techniques and
theories from math, statistics, data engineering, pattern recognition
and learning, advanced computing, visualization, uncertainty
modeling, data warehousing, and high performance computing.
28

Analysts - continued
• Business intelligence covers data analysis that relies heavily on
aggregation, focusing on business information.
• In statistical applications, some people divide data analysis
into descriptive statistics, exploratory data analysis that
focuses on discovering new features in the data and
confirmatory data analysis that confirms or falsifies existing
hypotheses.
• Predictive analytics focuses on application of statistical or
structural models for predictive forecasting or classification,
while text analytics applies statistical, linguistic, and structural
techniques to extract and classify information from textual
sources, a species of unstructured data.
29

Data Architects and Modelers
• Create the models, policies, rules or standards that
govern which data is collected, and how it is stored,
arranged, integrated, and put to use in data systems and
in organizations. Data is usually one of
several architecture domains that form the pillars of an
enterprise architecture.
30

Data SecurityManagers
• Implement security controls to protect databases, the
data, the applications or stored functions, the database
systems, the database servers and the associated
network links) against compromises of their
confidentiality, integrity and availability. It involves
various types or categories of controls, such as technical,
procedural/administrative and physical. Database
security is a specialist topic within the broader realms
of computer security, information security and risk
management.
31

Database Administrators
• Develop and design database strategies, system
monitoring and improving database performance and
capacity, with planning for future expansion requirements.
They may also plan, coordinate
and implement security measures to safeguard the database.
• Systems DBAs (also referred to as Physical DBAs, Operations
DBAs or Production Support DBAs): focus on the physical
aspects of database administration such as DBMS installation,
configuration, patching, upgrades, backups, restores,
refreshes, performance optimization, maintenance and
disaster recovery.
32

Database Administrators - continued
• Development DBAs: focus on the logical and development
aspects of database administration such as data model design
and maintenance, DDL (data definition language) generation,
SQL writing and tuning, coding stored procedures,
collaborating with developers to help choose the most
appropriate DBMS feature/functionality and other pre-
production activities.
• Application DBAs: usually manage all the application
components that interact with the database and carry out
activities such as application installation and patching,
application upgrades, database cloning, building and running
data cleanup routines, and data load process management.
33

Integration Specialists
• Bring together component subsystems into one system
to ensure that the subsystems function together as
complete system. These are usually developers which
include many forms, like ETL, reporting, transactional
systems, etc.
34

How Can I Get Have a Careers in Data
Management
• Network by joining associations and groups
• Get certified
• Research on what is going on in your area
35

Networking Associations and Groups in
the Seattle Area
• DAMA-PS
• TDWI NW
• AWC Puget Sound
• Meetup Groups
• Special Interest Groups
• LinkedIn Groups – BI Over Beers
36

Certificationand EducationalPathin Data
Management
• CDMP Bootcamps by DAMA-PS
• ICCP Certifications
• UW BI and other Certifications
• Bellevue College programs and certifications
• Seattle Central Community College programs and
certifications
37

DataManagementAssociation– PugetSound
• A non-profit professional organization founded in
1987. Our objective is to promote and advance the
concepts of data and information resource
management in the Pacific Northwest.
• Our monthly DAMA-PS chapter meetings give
members and guests the opportunity to broaden their
knowledge on topics relevant to information and data
management. We also give them opportunities to
network with like-minded professionals and
practitioners in their fields.
38

DAMA-PSEnablesLocalData Management
Professionalsto:
• Meet regularly with other
professionals/practitioners on a monthly
basis
• Establish a strong network of peers
• Gain technical advice and career direction
• Join us on our website, signup for
newsletters, on LinkedIn or Meetup.com
39

And Good Luck in
You New Endeavor
40

AWC Career Bootcamp- August 21, 2013

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