AWC Career Bootcamp- August 21, 2013

Careers in Data Management 101

AWC Career BootcampSeattle, WAAugust 21, 2103Patricia A GilsonPrincipalPAG Systems, LLC

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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.

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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?

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Who Am I?

Patricia A Gilson• 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 Certificationo Continuing Education Program in Business Intelligencehttp://www.pce.uw.edu/certificates/business-intelligence-decision-making.html

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http://www.pagsystems.com/

http://www.dama-ps.org/

http://www.pce.uw.edu/certificates/business-intelligence-decision-making.html

http://www.pce.uw.edu/certificates/business-intelligence-decision-making.html


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.”

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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.

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Careers in Data Management 101Data is Collected and Regulated for Security • Companies

Clothes Grocery Telecommunications Car

• Agencies Government

• Institutions Educational

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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

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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

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Reference Data 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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• Database Administrators• Integration Specialists

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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

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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.

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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.

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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.

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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.

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Data Security Managers

• 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.

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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.

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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.

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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.

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Careers in Data Management 101How 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

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Careers in Data Management 101Networking Associations and Groups in the Seattle Area• DAMA-PS• TDWI NW• AWC Puget Sound• Meetup Groups• Special Interest Groups• LinkedIn Groups – BI Over Beers

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Certification and Educational Path in 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

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Data Management Association – Puget Sound

• 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.

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DAMA-PS Enables Local Data Management Professionals to:

• 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

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And Good Luck in You New Endeavor

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AWC Career Bootcamp- August 21, 2013

Career

Transcript of AWC Career Bootcamp- August 21, 2013