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Data Structures - The Cornerstone of Your Data’s Home

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To co-opt an old adage: “If data gets lost and no one knows where to find it, does it still take up hard-drive space?” In the interest of avoiding that unfortunate philosophical end, individual data structures enable sorting, storage, and organization of data so that it can be retrieved and used efficiently. Applying the correct data structure to different types of data—whether master, reference, or analytics—allows your organization to tailor its data management to fit its unique business needs.
In this webinar, we will:
Discuss the various data structures available and when to use each one, as well as different design styles for analytics
Illustrate how data structures should support your organizational data strategy
Demonstrate how each method can contribute to business value

Published in: Business
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Data Structures - The Cornerstone of Your Data’s Home

  1. 1. Tom Gartland & Peter Aiken, PhD Data Structures The Cornerstone of your Data's Home Copyright 2017 by Data Blueprint Slide # 1 • DAMA International President 2009-2013 • DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 Peter Aiken, Ph.D. • 33+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions:
 – US DoD (DISA/Army/Marines/DLA)
 – Nokia
 – Deutsche Bank
 – Wells Fargo
 – Walmart
 – … PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman 2 Copyright 2017 by Data Blueprint Slide #
  2. 2. Tom Gartland 
 
 
 • A 30+ year veteran of IT, Tom has done everything: – Quality assurance – Programming – Data analysis – Architecting – Business intelligence – Project management • Across a variety of sectors and industries – Finance – Private health care – Charity health care – Government services – Construction – Discrete manufacturing – Process manufacturing – Retail – Telecommunications – Consulting 3Copyright 2017 by Data Blueprint Slide # • Tom spends much of his personal time with 
 his wife and 7 Rhodesian Ridgebacks 4Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home
  3. 3. Maslow's Hierarchy of Needs 5Copyright 2017 by Data Blueprint Slide # You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Practices however 
 this will: • Take longer • Cost more • Deliver less • Present 
 greater
 risk
(with thanks to 
 Tom DeMarco) Data Management Practices Hierarchy Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Practices Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities 6Copyright 2017 by Data Blueprint Slide #
  4. 4. DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas Data architecture implementation Data 
 Governance Data 
 Management
 Strategy Data 
 Operations Platform
 Architecture Supporting
 Processes Maintain fit-for-purpose data, efficiently and effectively 7Copyright 2017 by Data Blueprint Slide # Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data 
 Quality DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas Data 
 Governance Data 
 Management
 Strategy Data 
 Operations Platform
 Architecture Supporting
 Processes 8Copyright 2017 by Data Blueprint Slide # Data 
 Quality 3 3 33 1 Strategy is often the weakest link!
  5. 5. 9Copyright 2017 by Data Blueprint Slide # Data Management 
 Body of Knowledge 
 (DM BoK V2)
 Practice Areas To do any of these well requires specific knowledge of the relevant data structures! 10Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home
  6. 6. Without Data Structures ... 11Copyright 2017 by Data Blueprint Slide # • Water into wine • Coal into gold • Proper usage is: – Semi-structured into more structured – Non-tabular data into tabular data – Operational question: how much of it? 12Copyright 2017 by Data Blueprint Slide # Unstructured data cannot be transformed into structured data! Wrappers
  7. 7. What is a data structure? • "An organization of information • usually in memory (for better algorithm efficiency) • such as queue, stack, linked list, heap, dictionary, and tree, or • conceptual unity, such as the name and address of a person. • It may include redundant information, such as length of the list or number of nodes in a subtree." • Some data structure characteristics – Grammar (rules) for data objects – Constraints for data objects – Sequential order – Uniqueness – Arrangement • Hierarchical, relational, 
 network, other – Balance – Optimality http://www.nist.gov/dads/HTML/datastructur.html 13Copyright 2017 by Data Blueprint Slide # How are data structures expressed as architectures? • Details are organized into 
 larger components • Larger components are organized into models • Models are organized into architectures A B C D A B C D A D C B 14Copyright 2017 by Data Blueprint Slide #
  8. 8. How are data structures expressed as architectures? • Attributes are organized into 
 entities/objects – Attributes are characteristics of "things" – Entitles/objects are "things" whose 
 information is managed in support of strategy – For example: person (name, dob, res, kids, phone) • Entities/objects are organized into models – Combinations of attributes and entities are 
 structured to represent information requirements – Poorly structured data, constrains organizational information delivery capabilities – For example: sales model, accounting model, reporting model • Models are organized into architectures – When building new systems, architectures are used to plan development – More often, data managers do not know what existing architectures are and - therefore - cannot make use of them in support of strategy implementation – For example: financial architecture or business intelligence architecture 15Copyright 2017 by Data Blueprint Slide # Sample Data Architecture Overview 16Copyright 2017 by Data Blueprint Slide #
  9. 9. 17Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home History (such as it is) • Automate existing manual 
 processing • Data management was: – Running millions of punched 
 cards through banks of sorting, 
 collating & tabulating machines – Results printed on paper or 
 punched onto more cards – Data management meant physically storing and hauling around punched cards • Tasks (check signing, calculating, and machine control) were implemented to provide automated support for departmental-based processing • Creating information silos • Data Processing Manager 18Copyright 2017 by Data Blueprint Slide #
  10. 10. • Data Processing Manager Chief Information Officer 19Copyright 2017 by Data Blueprint Slide # CFO Necessary Prerequisites/Qualifications • CPA • CMA • Masters of Accountancy • Other recognized 
 degrees/certifications • These are necessary 
 but insufficient 
 prerequisites/qualifications 20Copyright 2017 by Data Blueprint Slide #
  11. 11. CIO Qualifications • No specific qualifications • Typically technological fields: – Computer science – Software engineering – Information systems • Business – Master of Business Administration – Master of Science in Management • Business acumen and strategic perspectives have taken precedence over technical skills. – CIOs appointed from the business side of the organization • Especially if they have project management skills. 21Copyright 2017 by Data Blueprint Slide # What do we teach knowledge workers about data? What percentage of them deal with it daily? 22Copyright 2017 by Data Blueprint Slide #
  12. 12. 23Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home Data Leverage Less ROT Technologies Process People • Permits organizations to better manage their sole non-depleteable, non-degrading, durable, strategic asset - data – within the organization, and – with organizational data exchange partners • Leverage – Obtained by implementation of data-centric technologies, processes, and human skill sets – Increased by elimination of data ROT (redundant, obsolete, or trivial) • The bigger the organization, the greater potential leverage exists • Treating data more asset-like simultaneously 1. lowers organizational IT costs and 2. increases organizational knowledge worker productivity 24Copyright 2017 by Data Blueprint Slide #
  13. 13. Data Structure Questions Program F Program E Program D Program G Program H Program I Application domain 2Application domain 3 • Who makes decisions about the range and scope of common data usage? 25Copyright 2017 by Data Blueprint Slide # Running Query 26Copyright 2017 by Data Blueprint Slide #
  14. 14. Optimized Query 27Copyright 2017 by Data Blueprint Slide # Repeat 100s, thousands, millions of times ... 28Copyright 2017 by Data Blueprint Slide #
  15. 15. 29Copyright 2017 by Data Blueprint Slide # Data structures organized into an Architecture • How do data structures support organizational strategy? • Consider the opposite question? – Were your systems explicitly designed to be integrated or otherwise work together? – If not, then what is the likelihood that they will work well together? – In all likelihood your organization is spending between 20-40% of its IT budget compensating for poor data structure integration – They cannot be helpful as long as their structure is unknown • Two answers/two separate strategies – Achieving efficiency and 
 effectiveness goals – Providing organizational dexterity for rapid implementation 30Copyright 2017 by Data Blueprint Slide #
  16. 16. Data Models Used to Support Strategy • Flexible, adaptable data structures • Cleaner, less complex code • Ensure strategy effectiveness measurement • Build in future capabilities • Form/assess merger and acquisitions strategies 31Copyright 2017 by Data Blueprint Slide # Employee
 Type Employee Sales
 Person Manager Manager
 Type Staff
 Manager Line
 Manager Adapted from Clive Finkelstein Information Engineering Strategic Systems Development 1992 5 Basic Data Structures Indexed Sequential File: Built-in index permits location of records of persons with last names starting with "T" Index Program: Where is the record for person "Townsend?" Index: Start looking here where the "Ts" are stored Relational Database: Records are related to each other using relationships describable using relational algebra Flat File: Records are typically sorted according to some criteria and must be searched from the beginning for each access Program: Must start at the beginning and read each record when looking for person "Townsend?" Network Database: Records are related to each other using arranged master records associated with multiple detail records using linked lists and pointers Associative Concept-oriented Multi-dimensional XML database
 3NF
 Star schema
 Data Vault Hierarchical Database: Records are related to each other hierarchically using 'parent child' relationships 32Copyright 2017 by Data Blueprint Slide #
  17. 17. • The thought of a single monolithic data store which can service all of an organization’s information needs has long since been abandoned. In the modern data management topology, multiple data stores are created to service specific processing needs and user groups within the organization. • Implications: – The needs characteristics of the 
 multitude of the audiences served 
 by the data structures – Data lifecycle – The design styles (old and new) utilized 
 to organize the data to service the audiences – A breakdown of the various stores – The resultant store characteristics Single Data Store One Size does not satisfy all needs 33Copyright 2017 by Data Blueprint Slide # Payroll Application
 (3rd GL)Payroll Data (database) R& D Applications
 (researcher supported, no documentation) R & D Data (raw) Mfg. Data (home grown database) Mfg. Applications
 (contractor supported) 
 Finance Data (indexed) Finance Application
 (3rd GL, batch 
 system, no source) Marketing Application
 (4rd GL, query facilities, 
 no reporting, very large) 
 Marketing Data (external database) Personnel App.
 (20 years old,
 un-normalized data) 
 Personnel Data
 (database) Typical System Evolution 34Copyright 2017 by Data Blueprint Slide #
  18. 18. The Situation 35Copyright 2017 by Data Blueprint Slide # How many interfaces are required to solve this integration problem? Application 4 Application 5 Application 6 15 Interfaces
 (N*(N-1))/2 Application 1 Application 2 Application 3 RBC: 200 applications - 4900 batch interfaces 36Copyright 2017 by Data Blueprint Slide #
  19. 19. 0 10000 20000 1 101 201 Number of Silos Worst case number of interconnections The rapidly increasing cost of complexity • N – 6 / 15 – 60 / 1,770 – 600 / 179,700 – 200 / 19,900 – 200 / 5,000 (actual) 37Copyright 2017 by Data Blueprint Slide # © Copyright 2004 by Data Blueprint - all rights reserved!43 - datablueprint.com XML-based Integration SolutionXML-based Integration Solution Application 4 Application 5 Application 6 XML Processor Application 1 Application 2 Application 33-Way Scalability Expand the: 1. Number of data items 
 from each system – How many individual 
 data items are tagged? 2. Number of 
 interconnections 
 between the systems and the hub – How many systems are connected to the hub? 3. Amount of interconnectability among hub-connected systems – How many inter-system data item transformations exist in the rule collection? 38Copyright 2017 by Data Blueprint Slide # HUB
  20. 20. Conclusions • 1 data structure is not enough • Most organizations have far too many different data structures and they become barriers to progress and integration • Not much expertise to figure out these challenges 39Copyright 2017 by Data Blueprint Slide # 40Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home
  21. 21. Data Personas (The Requirements) Operational Performer Interested in alerts, notifications and reporting based on current values (real- time) data. They use the information to make decisions and changes in the transactional systems. These changes are targeted to improve the organizations ability to deliver in the short term. Operational Analyst (Manager) Interested in aggregated real-time data for their domain of responsibility. The data is displayed using visualization techniques of scorecards, charts and reports, preferably within a single dashboard. The searching is for favorable/unfavorable trends to indicate adjustments are needed in the staff & resource allocations. Data Analyst Responsible to support detailed and typically complex analysis requests from business users/consumers of data. The analyst role span both the operational and historical time windows and thus they need to be versed in both the operational and analytic environments. Data Miner/ Scientist Responsible for using statistical and machine learning techniques to identify patterns from the data. These patterns are correlated into insights and actions for better business outcomes. The miner may use operational and historical data for research. Executive Consumer Receives the data through summary dashboards with drill down/through capabilities. Request detailed analysis and reporting on High Value Question from the Data Analyst and Data Miners. These consumers are looking at the data to make short and long term decisions to improve the organizational efficiency and customer experience. Operational Analytic 41Copyright 2017 by Data Blueprint Slide # • Operational interest is high when data is introduced to the operational stores. This interest wanes over time. • Analytic interest is low when data is first introduced. The interest increases as the data is collected and combined with other enterprise data. Persona Data Interest Operational Interest Analytic Interest Interest Time 42Copyright 2017 by Data Blueprint Slide # Time Interest
  22. 22. Development Standards/Concrete Blocks 43Copyright 2017 by Data Blueprint Slide # Example: Set Analysis 44Copyright 2017 by Data Blueprint Slide # from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
  23. 23. 45Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home Data Topology Today Can Be Complex 46Copyright 2017 by Data Blueprint Slide # Data Mart Master Data OLTP 1 OLTP 2 OLTP n... Enterprise Data Warehouse (EDW) Operational Data Store (ODS) Data Mart Data Mart Event Data StoreBus OPS Events Tech OPS Events Technical MetadataMetadata StoreBusiness Metadata
  24. 24. Data Store Purpose a review of the Data Topology • Master Data – Master Data is the term used to describe the data domains that drive business activities. Master data is the data that must first be in place before business transactions can occur. Master data is often shared across the organizational business units and it is typically at the center of business strategies. The transaction defines the business/process event (order, dispatch, sales) while the Master Data describes the ‘who’ (customers, drivers, account reps), the ‘what’ (load), the ‘when’ (date, time) and the ‘where’ (origin and destination location). • Online Transaction Processing (OLTP) – “Transactional data” is the term used to describe the data involved in the execution of the business activities. Transactional data associates master data (i.e. customers and products) to a business activity that often represents a unit or work, such as the creation of an order. • The Master Data and OLTP stores are where data is initially created and persisted within the organization’s data and thus carry a special classification of System of Record (SOR). They are created to capture the transactional data as it arrives and makes the data available for the processes and services. The data arrives into these databases through manual entry or automated feeds. These data stores are logically (and sometimes physically) separated by the transactional subject area they are created to serve. OLTP1 OLTP2 OLTPn... Master Data 47Copyright 2017 by Data Blueprint Slide # Data Store Purpose a review of the Data Topology • Online Transaction Processing (OLTP) – “Transactional data” is the term used to describe the data involved in the execution of the business activities. Transactional data associates master data (i.e. customers and products) to a business activity that often represents a unit or work, such as the creation of an order. – The Master Data and OLTP stores are where data is initially created and persisted within the organization’s data and thus carry a special classification of System of Record (SOR). They are created to capture the transactional data as it arrives and makes the data available for the processes and services. The data arrives into these databases through manual entry or automated feeds. These data stores are logically (and sometimes physically) separated by the transactional subject area they are created to serve. • Master Data – Master Data is the term used to describe the data domains that drive business activities. Master data is the data that must first be in place before business transactions can occur. Master data is often shared across the organizational business units and it is typically at the center of business strategies. The transaction defines the business/process event (order, dispatch, sales) while the Master Data describes the ‘who’ (customers, drivers, account reps), the ‘how’ (order delivery type), the ‘when’ (date, time) and the ‘where’ (location, destination). 48Copyright 2017 by Data Blueprint Slide # OLTP 1 OLTP 2 OLTP n... Master Data
  25. 25. Data Store Purpose a review of the Data Topology • Operational Data Store (ODS) – An Operational Data Store (ODS) is created to integrate data from two or more SORs for the purposes of data integration. The ODS is normally created to satisfy reporting needs across functional SOR boundaries. The ODS should hold very little historical information and should focus on maintaining the most up-to-date data needed by the organization for daily operations. Depending on the application requirements, the ODS may institute a near real-time data feed from the source applications. The ODS is expected to be technically accurate and is considered to be an Authoritative Source. The data it contains can be used for non-critical needs instead of having to access the SOR. The more frequently the data is pushed into the ODS environment, the less reliance there will be on direct access to SORs for data reporting needs. • Enterprise Data Warehouse (EDW) – An Enterprise Data Warehouse (EDW) is responsible for collection and integration of data from either SORs or from the Operational Data Store. An EDW has an enterprise scope as it will pull from many (if not all) SORs. The focus of the data warehouse is to be historical in nature and in many instances is loaded with a latency (every 24 hours). The data warehouse is created to support historical analytics. The expectation of the data warehouse is to be exhaustive in the data it collects with a focus being on collecting and storing of the data. EnterpriseData Warehouse (EDW) Operational DataStore (ODS) 49Copyright 2017 by Data Blueprint Slide # Data Store Purpose a review of the Data Topology • Data Marts – A Data Mart is a subset of a data warehouse, it is created to address specific questions and/or subject area of questions. A Data Mart is built and tuned to deliver the data to the end users, it exists to get the data out from the data warehouse. Data Mart 50Copyright 2017 by Data Blueprint Slide #
  26. 26. Data Store Purpose a review of the Data Topology • Event Data Store – Is the data store which logs, stores and reports the discrete business and technical events which occur within the process. This data store is a critical, and often overlooked data domain for managing, controlling and creating transparency into the business processes. The events are used to report out the overall health of the processes in both business and technical terms. This consolidated solution is key to obtaining a 360 view of the processes. • Metadata Store – Metadata is a broad term which includes descriptive elements in both business and technical terms. It covers: business terms, data elements descriptions, element display formats, element valid values, element quality targets, etc. Metadata is critical to an organization as it describes the organization’s business and processing infrastructure in detail. Metadata is entertainingly defined as “data about the data”. That is, Metadata characterizes other data and makes it easier to retrieve, interpret and use information. Technical Metadata Metadata Store Business Metadata Event Data Store BusOPS Events TechOPS Events 51Copyright 2017 by Data Blueprint Slide # Operational i
 n 
 
 c
 o
 n
 t
 r
 a
 s
 t 
 
 w
 i
 t
 h Analytic Subject-Oriented Databases which are focused on a single or small set of business functions Integrated Collecting and semantically aligning data from disparate sources to achieve a homogeneous view Volatile Data which may change frequently Non-Volatile Data for which entered into the database will not change Atomic Low grain data, each transaction, each order with all of the attributes Aggregate A summary of multiple orders or transactions performed to transform the atomic detail into more comprehensible information Current Valued: The data and the system represents what is current in this moment; not yesterday, not last week --- now Time Variant Data: is marked and stored with a date/time element where questions of what was it yesterday and last week can be answered Data Store Characteristics 52Copyright 2017 by Data Blueprint Slide #
  27. 27. 53Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Data Structures: The Cornerstone of your Data's Home Data Structure Design Styles • 3rd Normal Form (3NF) – Inmon • Dimensional – Kimball • Data Vault – Lindstad 54Copyright 2017 by Data Blueprint Slide #
  28. 28. Design Styles – 3NF • 3rd Normal Form Modeling • A mathematical data design 
 technique founded in the early 
 70s by E.F. Codd. • Organizes data in simple rows 
 and columns - Entities • Creates connections between the 
 entities called relationships to show how the data is inter-related • It is purest form 3NF removes all data redundancies – a piece of data is stored only once • 3NF is based on mathematics, give the same facts to different modelers; the model should be the same. • Creates a visual (Entity Relation Diagram - ERD) which may be understood by less technical personnel • 3NF is the modeling style most popularly used for operationally focused data stores. 55Copyright 2017 by Data Blueprint Slide # Inmon Implementation 56Copyright 2017 by Data Blueprint Slide #
  29. 29. Design Styles – Dimensional • Created and refined by Ralph 
 Kimball in the 80s. • Organizes data in Facts 
 and Dimensions. Fact 
 tables record the events 
 (what) within the business domain 
 and the Dimension tables describe 
 who, when, how and where. • The data design style was created to 
 exploit the capabilities of the relational database to retrieve and report against large volumes of data. • Dimensional modeling sacrifices storage efficiency for analytical processing speed • There are 2 variations to Dimensional Modeling: Star Schema and Snowflake 57Copyright 2017 by Data Blueprint Slide # Kimball Implementation 58Copyright 2017 by Data Blueprint Slide #
  30. 30. Design Styles – Data Vault • One of the newer relational database modeling techniques • Data Vault modeling was conceived in the 1990s by Dan Linstedt • Data Vault models are designed for central data warehouses that store non-volatile, time-variant, atomic data • Relationships are defined through Link structures which promote flexibility and extensibility 59Copyright 2017 by Data Blueprint Slide # Data Vault Implementation 60Copyright 2017 by Data Blueprint Slide #
  31. 31. Hybrid Approach • (http://www.kimballgroup.com/2004/03/03/differences-of-opinion/) • Learn Data Vault – “dv-in-kimball-bus-architecture” 61Copyright 2017 by Data Blueprint Slide # DATA STORE AUDIENCE SERVED BUILD CHARACTERISTICS DESIGN STYLE O
 P
 E
 R
 A
 T
 I
 O
 N
 A
 L Master Data OLTP ODS Event A
 N
 A
 L
 Y
 T
 I
 C Data Warehouse Data Mart Summary/Take Aways DATA STORE AUDIENCE SERVED BUILD CHARACTERISTICS DESIGN STYLE O
 P
 E
 R
 A
 T
 I
 O
 N
 A
 L Master Data Operations Manager Operational Analyst Subject Oriented Volatile Atomic Current Valued 3NF OLTP Operational Performer Operations Manager Subject Oriented Volatile Atomic Current Valued 3NF ODS Operational Manager Operational Analyst Executive Consumer Integrated Volatile Atomic Current Valued 3NF Event All Personas Integrated Volatile Atomic Current Valued 3NF A
 N
 A
 L
 Y
 T
 I
 C Data Warehouse Data Miner/Scientist Integrated Non-volatile Atomic Time Variant 3NF trending to Data Vault Data Mart Operational Analyst Data Analyst Executive Consumer Subject Oriented Non-volatile Atomic -or- Aggregated Time Variant Dimensional 62Copyright 2017 by Data Blueprint Slide #
  32. 32. Outline: Design/Manage Data Structures 63Copyright 2017 by Data Blueprint Slide # • Context: Data Management/DAMA/DM BoK/CDMP? • What is a data structure? • Structured data storage, a bit of history and context • Why are data structures important? • Data Personas/Usage (interest over time) • Data Topology and alignment to the data audience • Internal data structures to fit the needs • Q & A? Upcoming Events September Webinar: Implementing Big Data, NOSQL, & HADOOP – Bigger is (Usually) Better September 12, 2017 @ 2:00 PM ET/11:00 AM PT Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by: 64Copyright 2017 by Data Blueprint Slide #
  33. 33. Questions? + = 65Copyright 2017 by Data Blueprint Slide # 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 Copyright 2017 by Data Blueprint Slide # 66

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