Data-Ed Webinar: Design & Manage Data Structures
 

Data-Ed Webinar: Design & Manage Data Structures

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Data structures enable you to store and organize data so that it can be used efficiently. But how do you know to apply the correct one? There is a difference between structuring master data, reference ...

Data structures enable you to store and organize data so that it can be used efficiently. But how do you know to apply the correct one? There is a difference between structuring master data, reference data and analytics data. This webinar will discuss the various data structures available and when to use each one. We will show how data structures should support your organizational data strategy and how each method can contribute to business value.

Takeaways:

Application of correct data structures to fit business needs
How different structures create different business value

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Data-Ed Webinar: Design & Manage Data Structures Data-Ed Webinar: Design & Manage Data Structures Presentation Transcript

  • Data structures enable you to store and organize
 data so that it can be used efficiently. But how do
 you know to apply the correct one? There is a
 difference between structuring master data,
 reference data and analytics data. This webinar 
 will discuss the various data structures available 
 and when to use each one. We will show how 
 data structures should support your organizational
 strategy and how each method can contribute to
 business value. Learning Objectives: • Application of correct data structures to fit business needs • How different structures create different business value 
 
 Date: July 8, 2014
 Time: 2:00 PM ET
 Presented by: Dave Marsh & Peter Aiken Copyright 2013 by Data Blueprint Welcome: Design/Manage Data Structures 1
  • Copyright 2013 by Data Blueprint Get Social With Us! Like Us on Facebook www.facebook.com/ datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed 3
  • Presented by Dave Marsh & Peter Aiken, Ph.D. Design & Manage Data Structures Marco Level
  • Copyright 2013 by Data Blueprint Your Presenters Dave Marsh • Lead Data 
 Consultant, 
 Data Blueprint • 30+ Years experience designing and building solutions for the private and public sectors. • Architecture/Design experience in: - Transactional processing - Shop floor automation - Data Warehousing - Identity Management - Mobile Peter Aiken • 30+ years DM 
 experience • 9 books/many articles • Experienced with 500+ data management practices • Multi-year immersions: US DoD, Nokia, Deutsche Bank, Wells Fargo, & Commonwealth of VA 4
  • Copyright 2013 by Data Blueprint • 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? Outline: Design/Manage Data Structures 6 • 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?
  • Maslow's Hierarchy of Needs Copyright 2013 by Data Blueprint 7
  • You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present 
 greater
 risk Data Management Practices Hierarchy Basic Data Management Practices Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Data Program Management Data Stewardship Data Development Data Support Operations Organizational Data Integration Copyright 2013 by Data Blueprint 8
  • Data Program 
 Coordination Feedback Data
 Development Copyright 2013 by Data Blueprint Standard
 Data Data Management is an Integrated System of Five Practice Areas Organizational Strategies Goals Business
 Data Business Value Application 
 Models & Designs Implementation Direction Guidance 9 Organizational
 Data Integration Data
 Stewardship Data Support
 Operations Data 
 Asset Use Integrated
 Models Leverage data in organizational activities Data management processes and infrastructure Combining multiple assets to produce extra value Organizational-entity subject area data integration Provide reliable data access Achieve sharing of data within a business area
  • Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas 10 Manage data coherently. Share data across boundaries. Assign responsibilities for data. Engineer data delivery systems. Maintain data availability. Data Program 
 Coordination Data
 Development Organizational Data Integration Data
 Stewardship Data Support
 Operations
  • Copyright 2013 by Data Blueprint DAMA DM BoK & CDMP Data Management Functions • Published by DAMA International – The professional association for Data Managers (40 chapters worldwide) – DMBoK organized around – Primary data management functions focused around data delivery to the organization (more at dama.org) – Organized around several environmental elements • CDMP – Certified Data Management Professional – DAMA International and ICCP – Membership in a distinct group made up of your fellow professionals – Recognition for your specialized knowledge in a choice of 17 specialty areas – Series of 3 exams – For more information, please visit: • http://www.dama.org/i4a/pages/index.cfm?pageid=3399 • http://iccp.org/certification/designations/cdmp 11
  • Copyright 2013 by Data Blueprint • 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? Outline 13
  • Copyright 2013 by Data Blueprint 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 for data objects • Grammar is the principles 
 or rules of an art, science, 
 or technique "a grammar 
 of the theater" – Constraints for data 
 objects – Sequential order – Uniqueness – Arrangement • Hierarchical, relational, 
 network, other – Balance – Optimality http://www.nist.gov/dads/HTML/datastructur.html 14
  • Copyright 2013 by Data Blueprint 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 15
  • Copyright 2013 by Data Blueprint 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 – Examples • 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 – Examples • 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 – Why no examples? 16
  • Copyright 2013 by Data Blueprint Data Data Data Information Fact Meaning Request A Model Specifying Relationships Among Important Terms [Built on definition by Dan Appleton 1983] Intelligence Strategic Use 1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. 5. INTELLIGENCE is INFORMATION associated with its USES. Wisdom & knowledge are 
 often used synonymously Data Data Data Data 17
  • Copyright 2013 by Data Blueprint • 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? Outline 19
  • Copyright 2013 by Data Blueprint 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 20
  • Copyright 2013 by Data Blueprint Chief Information Officer 21
  • Copyright 2013 by Data Blueprint CFO Necessary Prerequisites/Qualifications • CPA • CMA • Masters of Accountancy • Other recognized degrees/certifications • These are necessary but insufficient prerequisites/ qualifications 22
  • Copyright 2013 by Data Blueprint 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. 23
  • Copyright 2013 by Data Blueprint What do we teach knowledge workers about data? What percentage of the deal with it daily? 24
  • Copyright 2013 by Data Blueprint • 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? Outline 26
  • Copyright 2013 by Data Blueprint 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 27
  • Copyright 2013 by Data Blueprint 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? 28
  • Copyright 2013 by Data Blueprint Running Query 29
  • Copyright 2013 by Data Blueprint Optimized Query 30
  • Copyright 2013 by Data Blueprint Repeat 100s, thousands, millions of times ... 31
  • Death by 1000 Cuts Copyright 2013 by Data Blueprint 32
  • Copyright 2013 by Data Blueprint 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 33
  • Copyright 2013 by Data Blueprint 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 34
  • Copyright 2013 by Data Blueprint Single Data Store No Single Data Store • 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 35
  • Copyright 2013 by Data Blueprint Conclusions • 1 is not enough • Most organizations have far to many different data structures and they become barriers to progress and integration • Not much expertise to figure out these challenges 36
  • Copyright 2013 by Data Blueprint • 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? Outline 38
  • Copyright 2013 by Data Blueprint 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 39
  • • 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. Copyright 2013 by Data Blueprint Persona Data Interest Operational Interest Analytic Interest Interest Time 40
  • Copyright 2013 by Data Blueprint • 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? Outline 42
  • Copyright 2013 by Data Blueprint Data Topology Today 43
  • Copyright 2013 by Data Blueprint 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 44
  • Copyright 2013 by Data Blueprint 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) 45
  • Copyright 2013 by Data Blueprint 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 46
  • Copyright 2013 by Data Blueprint 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 47
  • Operational i
 n 
 
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 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 viewVolatile 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 Copyright 2013 by Data Blueprint Data Store Characteristics 48
  • Copyright 2013 by Data Blueprint • 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? Outline 50
  • • 3rd Normal Form (3NF) – Inmon • Dimensional – Kimball • Data Vault – Lindstad Copyright 2013 by Data Blueprint Data Structure Design Styles 51
  • • 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. Copyright 2013 by Data Blueprint Design Styles – 3NF 52
  • Copyright 2013 by Data Blueprint Design Styles – Dimensional Modeling • A data design approach create and refined by Ralph Kimball in the 80s • Organizes data in Facts and Dimensions – Fact tables record the events (what) within the 
 business domain – Dimension tables describing who, when, how and where • Created to exploit the capabilities of the relational database to retrieve and report against large volumes of data. • There are 2 variations to Dimensional Modeling: – Star Schema – Snowflake 53
  • Copyright 2013 by Data Blueprint Design Styles – Data Vault • Newest of the relational database modeling techniques. • Conceived in the 1990s by Dan Linstedt • Focuses on linking the data from multiple disparate locations without forcing the data to be semantically aligned NOTE: There is a Data Ed presentation schedule for 14 October 2014 to cover the details of Data Vault designs! 54
  • 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 Copyright 2013 by Data Blueprint 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 55
  • Copyright 2013 by Data Blueprint • 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? Outline: Design/Manage Data Structures 56
  • Copyright 2013 by Data Blueprint Questions to Ask • Are you ready for a data warehouse? • Foundational Practices • Is the business environment constantly evolving? • Will you get it right the first time? • Do you have an agreed upon enterprise-wide vocabulary • Is your data warehouse intended to be the enterprise audit-able system of record? • Extract, Transform and Load • Data Transformations • How fast do you need results? • Performance of inserts vs reads • Project deliverables 57
  • Copyright 2013 by Data Blueprint Upcoming Events August Webinar: Data Management Maturity August 12, 2014 @ 2:00 PM ET/11:00 AM PT ! Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net ! ! ! ! ! ! ! ! ! ! ! Brought to you by: 58
  • Copyright 2013 by Data Blueprint Questions? + = 59
  • Copyright 2013 by Data Blueprint Why Architectural Models? • Would you build a house without an architecture sketch? • Would you like to have an estimate how much your new house is going to cost? • If you hired a set of constructors from all over the world to build your house, would you like them to have a common language? • Would you like to verify the proposals of the construction team before the work gets started? • If it was a great house, would you like to build something rather similar again, in another place? • Would you drill into a wall of your house without a map of the plumbing and electric lines? • Model is the sketch of the system to be built in a project. • Your model gives you a very good idea of how demanding the implementation work is going to be! • Model is the common language for the project team. • Models can be reviewed before thousands of hours of implementation work will be done. • It is possible to implement the system to various platforms using the same model. • Models document the system built in a project. This makes life easier for the support and maintenance! Would you build a house without an architecture sketch? Model is the sketch of the system to be built in a project. Would you like to have an estimate how much your new house is going to cost? Your model gives you a very good idea of how demanding the implementation work is going to be! If you hired a set of constructors from all over the world to build your house, would you like them to have a common language? Model is the common language for the project team. Would you like to verify the proposals of the construction team before the work gets started? Models can be reviewed before thousands of hours of implementation work will be done. If it was a great house, would you like to build something rather similar again, in another place? It is possible to implement the system to various platforms using the same model. Would you drill into a wall of your house without a map of the plumbing and electric lines? Models document the system built in a project. This makes life easier for the support and maintenance! 60
  • Copyright 2013 by Data Blueprint Inmon Implementation 61
  • Copyright 2013 by Data Blueprint Kimball Implementation 62
  • Copyright 2013 by Data Blueprint Data Vault Implementation 63
  • Copyright 2013 by Data Blueprint Hybrid Approach • (http://www.kimballgroup.com/2004/03/03/differences-of-opinion/) • Learn Data Vault – “dv-in-kimball-bus-architecture” 64
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