SlideShare une entreprise Scribd logo
1  sur  10
Télécharger pour lire hors ligne
White Paper



Building an Enterprise Metadata
Repository
Ron Lewis, CDO Technologies

December 2009




Corporate Headquarters              EMEA Headquarters         Asia-Pacific Headquarters

100 California Street, 12th Floor   York House                L7. 313 La Trobe Street

San Francisco, California 94111     18 York Road              Melbourne VIC 3000

                                    Maidenhead, Berkshire     Australia
                                    SL6 1SF, United Kingdom
Building an Enterprise Metadata Repository




CONTENTS
Introduction ............................................................................................................................. - 2 -

First Step: Decide What Needs to be Collected ..................................................................... - 3 -
Second Step: Collecting the Metadata .................................................................................. - 4 -

Third Step: Populating the Metadata Repository .................................................................. - 6 -

Selecting the Right Tools......................................................................................................... - 6 -

Summary .................................................................................................................................. - 7 -
About the Author .................................................................................................................... - 8 -




Embarcadero Technologies                                                                                                                  -1-
Building an Enterprise Metadata Repository




INTRODUCTION
The importance of capturing metadata has been a topic of many webinars, teleconferences,
and white papers over the last several years. There’s has also been an increasing emphasis on
“building metadata repositories”. To avoid being just another white paper describing the
“metadata trend” or promoting a particular metadata repository solution – the intent of this
whitepaper is to provide basic definitions for the concepts of metadata and metadata
repositories, as well as to provide a basic methodology for collecting metadata and populating
an enterprise metadata repository.

IT ALL BOILS DOWN TO THE DATA
A good starting point is defining the difference between data and information. Data is nothing
more than collections of raw facts. Data is transformed into information as result of the
manipulating or processing of these “raw facts” by putting them into meaningful contexts. Data
is often referred to as the “crown jewels” of an organization. This is a valid analogy; an
organization’s data is typically priceless to that organization. The financial health and
profitability is often tied to how well a business entity utilizes its data resources.

As technology has evolved, executive leaders have become increasingly more aware of the
importance of data to the efficiency of an organization. A lot of emphasis is being placed on
methodologies and frameworks—a couple good examples would be LEAN Engineering
Principles and the Zachman Framework—to provide additional insight into how to streamline a
company or business’ effectiveness. There’s a heavy focus on mapping business processes and
to the corporate data upon which the processes rely. This focus introduced the need for
metadata management.

METADATA PROVIDES CONTEXT
Metadata describes data and is used to enhance the effectiveness of data use. Metadata is
often categorized as either business or technical metadata. Business metadata describes
taxonomies, articulates business rules, and establishes common vocabularies. This helps align
data with its business context. Conversely, technical metadata describes data sources,
attributes, domains, nomenclature, movement, and consumption rules. The bottom line:
Metadata is used to identify the context in which data becomes meaningful.

METADATA REPOSITORIES STORE ALL YOUR PROVERBIAL EGGS
IN ONE BASKET
When it comes to metadata, having all your proverbial eggs in one basket is a good thing.
Metadata, especially when aggregated properly, can expose new ways of exploiting data to
significantly increase efficiency and profitability. In order to glean the full value from collected
metadata it’s important to store metadata in a manner where it can be easily indexed,
cataloged, and searched. A metadata repository facilitates this. A metadata repository is a
system for aggregating, indexing, cataloging, protecting and providing access to corporate
metadata.

This is no small task. Storing metadata collectively is challenging because there are many
different types of metadata, and many different means by which metadata can be expressed.


Embarcadero Technologies                                                                           -2-
Building an Enterprise Metadata Repository


As stated above, there are two basic categories of metadata: business and technical. Below are
a few examples of crucial metadata and a few of the forms by which it is normally expressed.
This is by no means an exhaustive list, but is provided to help scope the metadata collection
task.

Crucial business metadata:

           o   Business Vocabularies: describe terms common to the organization and are built
               of Business Definitions
           o   Business Definitions: provide a common meaning to a common term
           o   Business Processes: groups of business activities that center around a business
               purpose and governed by business rules
           o   Business Rules: define the organization and how it achieves it business goals

Critical technical metadata:

           o   Data Models
           o   System Catalogs
           o   Extract, Transform, and Load Scripts
           o   Data Lineage
           o   Data Consumption Rules

A few common means by which metadata is articulated:

           o   Use Cases: Often used to describe business processes in software development
               terms
           o   Business Models: Used to facilitate communication between business and
               systems analysts
           o   Data Models: Used to describe relationships between data elements

A metadata repository solution should be capable of collecting all of these bits of data in a
readily searchable, protected form. Quick rule of thumb concerning metadata repository
security: The value of the metadata is proportionate to the perceived quality and reliability of
the metadata repository contents. The metadata is incredibly valuable and should be
adequately protected.


FIRST STEP: DECIDE WHAT NEEDS TO BE
COLLECTED
There is obviously lots of metadata that can be collected and housed in a repository. Knowing
what needs to be collected is definitely a huge challenge. Deciding what needs to be collected
is viewed as an enterprise engineering task. Enterprise architects typically describe an
organization using a formal, structured framework, such as Zachman, to define the “why, how,
what, who, where, and when” associated with an enterprise. This is an expensive and time
consuming endeavor but is also well worth the investment.




Embarcadero Technologies                                                                       -3-
Building an Enterprise Metadata Repository



REVERSE ENGINEERING DATA COLLECTION NEEDS FROM KEY
REPORTING REQUIREMENTS
A simple method for getting started for diverse, well-structured organization is to iteratively
collect data associated with the business processes that define the enterprise reverse
engineering from the reports that the enterprise relies upon. For example, a medical practice
can be decomposed into several large building blocks—such as doctors, patients, visits, and
perhaps treatments. A medical practice requires several different types of key reports, such as
billing, treatment record, medical record requests, and HIPAA release forms-- to name a few
(disclaimer: this is only for illustrative purposes). Start with the most frequently used “reports”
and identify the information necessary to complete the tasks associated with the large building
blocks. Reports encapsulate critical business rules, and key business attributes. A lesson
learned from the security industry is reports often put business data in its most meaningful
context and are prime targets of cyber-attackers. This method for determining metadata needs
leverages this security knowledge. Since reports provide easy-to-understand, readily accessible
mappings between business needs and the supporting data--the answer to these two
questions: “What information do the reports describe” and “what is necessary to build them”
provide a good starting point for identifying the most important metadata to be collected.


SECOND STEP: COLLECTING THE METADATA
THE TRADITIONAL METHOD
Many enterprise architects start with building business process models, and then map the
business process models to conceptual data models. This requires business architects to
interview key business analysts to derive and validate the business models. It also requires data
architects to interview key technical personnel to build and validate the conceptual data
models. Once the models and mappings have been built—enterprise architects then map
these to systems and data repositories identifying key dependencies between systems. The
correlating metadata defining the AS-IS is then collected, cataloged, indexed, and housed in a
metadata repository.

KEY CHALLENGES
Managing Cost: Initial metadata collection activities associated with the manual effort for
describing the existing environment (e.g., the “AS-IS”) are expensive, both in terms of cost and
time. Manual interview processes are disruptive. Interview processes have a hidden cost in lost
production; they pull key personnel away from revenue generating tasks. In rapidly evolving,
high-demand environments—this loss can be devastating.

Managing Scope: There are two common scope-related tendencies during initial data
collection efforts: to focus on collecting everything, or to get buried in the details. Both of
these cause a loss of momentum and end up increasing cost significantly.

Staying focused on the data collection: It’s tempting, especially when glaring inefficiencies are
discovered, to stop the data collection activities and focus on addressing and correcting the
discrepancies. This can have huge negative impact due to the ripple effect changes can cause
across an organization.



Embarcadero Technologies                                                                          -4-
Building an Enterprise Metadata Repository


SOLUTIONS
Summing up what seems to work best in most environments to counter these challenges:

           o   Focus on what’s important. Start with the organization’s main focus and then
               quantify each activity based on the percentage of revenue for the organization.
           o   Leverage automation to the greatest degree. Eliminate as many of the manual
               interviews as possible and glean as much business information by decomposing
               reports, reverse engineering applications, and current software development
               documentation. Caution: Not all software development artifacts will match
               what’s been deployed into production.
           o   Do not perform data re-engineering or process improvement during data
               collection. Focus the effort on data collection. When all the metadata has been
               collected, cataloged, and indexed, business analysts will often get a clear
               picture of inefficiencies across the enterprise.  


A BETTER WAY – USE TOOLS
A prime solution to reduce the time and cost while increasing the overall effectiveness of the
data collection activities is to leverage technology. It’s best to start with the reports and work
backwards towards deriving processes. The reports and associated metadata collected provide
a good means for validating the processes, provide a common frame of reference for all
involved in the interview process, and minimizes number of interviews necessary to fully capture
the enterprise view.

Perform the following actions using tools:

           o   Identify as many of the database servers within the enterprise and reverse
               engineer the physical schemas contained within each database server.
           o   Build a single enterprise data model and include each reverse engineered
               physical schema in the master model.
           o   Export the schema metadata to a spreadsheet and have the appropriate data
               architects annotate what is known about each entity and associated attributes.
           o   Instrument and profile each application with an application protocol monitor to
               capture business rules captured as programmatic logic-thereby capturing the
               data consumption rules.
           o   Extract business logic from application source code
           o   Capture transformation rules and data lineage metadata by profiling extract,
               transform, and load (ETL) scripts. This is accomplished by instrumenting each
               database server and capturing use with a database profiling tool.
           o   Import any existing business process models (BPM) into a standard BPM tool.
           o   Profile data use, focusing on identification of dormant data as well as high-
               utilization data—databases, tables, and elements.
           o   Evaluate the metadata captured for consistency. Identify and annotate any
               discrepancies discovered.
                


Embarcadero Technologies                                                                       -5-
Building an Enterprise Metadata Repository



THIRD STEP: POPULATING THE METADATA
REPOSITORY
Once the above steps have been completed, there will be a significant amount of metadata. To
glean the value from the metadata, it needs to be imported in a common repository, cataloged,
indexed, and made available to the appropriate business, system, and data analysts.

There are plenty of pre-built metadata repositories. The metadata repository solution selected
must have the following critical capabilities:

           o   Manages unstructured content.
           o   Provides appropriate access controls and auditing.
           o   Imports myriad formats and exports in a standard format such as XML.
           o   Allow the creation and associations of descriptions to each metadata asset.
           o   Provides a searchable and intuitive means for finding and evaluating the
               appropriate metadata.

The steps for populating a metadata repository will vary based on the particular solution;
however, the methodology is pretty standard. Each solution should provide import and export
mechanisms that allow data bridging between metadata collection tools. For collection tools
not natively supported by the repository solution, a commonly understood format, such as XML,
can be used as an intermediary bridge. For example--if metadata describing data
nomenclature has been captured in a loosely structured tool, such as Microsoft (MS) Excel, the
metadata can be exported as a comma-separated-value (CSV) file and imported back into a
supported data modeling tool. Most data modeling tools allow users to export metadata in
XML format. While an MS Excel file would most likely normally be captured in the repository as
“unstructured content”—it can be converted to structured content by importing the
appropriate metadata into the data modeling tool and exporting as schema metadata. This
example, of course, assumes that the metadata articulated in the spreadsheet is associated with
either logical or physical data models.


SELECTING THE RIGHT TOOLS
Selecting the right tools for collecting metadata is critical. The tools should be intuitive, and
easy-to-use. More importantly, the tools need to integrate well with one another to minimize
the number of tool bridging required. (Note: each time data is exported from one tool and
imported to another there is a risk that metadata will be lost or altered.) The tools should also
be accepted as an industry standard; this ensures that whatever metadata repository solution
selected, the tools will be supported.

THIS AUTHOR’S TOOL CHOICE
It’s important to have tools that are comfortable, reliable, and easy-to-demonstrate. I really like
the Embarcadero solution set. The integration provided by the new Embarcadero All Access
solution further solidified my choice. I’ve supplemented the toolset with NitroSecurity’s
NitroView Application Protocol Monitor (APM) to allow the visibility necessary to profile legacy
applications to extract business logic articulated as .NET and Java Code.


Embarcadero Technologies                                                                        -6-
Building an Enterprise Metadata Repository


METADATA COLLECTION TOOLS
Embarcadero® ER/Studio® Data Architect for reverse engineering physical schemas, building
the associated conceptual and logical models, and for managing schema related metadata. It is
well suited for capturing and managing enterprise transformation, consumption, and data
lineage metadata. Each reverse-engineered schema can be added to a master data model as a
sub-model to the master and the master can be auto-generate a master data catalog.
ER/Studio Data Architect supports creation of meta-data tags which are useful for meeting
regulatory requirements, such as identify PHI and PII for HIPAA or FISMA respectively. It also
supports creation and management of aliases, and provides a good foundation for data
governance.

Embarcadero® ER/Studio® Business Architect for importing existing business process models
and for building new ones.

Embarcadero ® ER/Studio® Enterprise. The latest version of ER Studio allows Data Architect
and Business architect to integrate into the ER Studio Repository. This allows business process
metadata and data metadata to coexist in a web-accessible repository.

DATABASE POLLING TOOLS
Embarcadero® DB Optimizer™. This provides the ability to capture sessions and filter them by
database and table. It is well suited for capturing key performance parameters that are critical
for data warehousing construction. It also provides a means of identifying which ETL scripts are
being utilized and for validating data lineage.

APPLICATION PROFILING TOOLS
Embarcadero® JBuilder™ for evaluating legacy J2EE applications and mapping to business
cases

NitroView APM for mapping application roles to business functions and for exposing business
rules in near-real time. The output from APM can be correlated with the DBOptimizer output to
provide a very accurate picture of which users are accessing which data, even when legacy
applications use connection pooling or shared application user accounts.

Most of the metadata is stored in the ER/Studio Repository and made available via the ER
Studio Portal included in the ER/Studio Enterprise. The integration of Data Architect and
Business Architect into a single repository, as well as the functionality provided in the All Access
Toolkit makes All Access a good fit for metadata collection and management.


SUMMARY
The intent of this whitepaper was to provide basic definitions for the concepts of metadata and
metadata repositories, as well as to provide a basic methodology for collecting metadata and
populating an enterprise metadata repository. Three key points hopefully gleaned:

In order to reap the full benefit from enterprise data assets, an organization must properly
collect and manage enterprise metadata.

Collecting the metadata across an enterprise can be expensive, although worthwhile endeavor;
the cost can be significantly reduced through proper use of the right tools; and the tangible


Embarcadero Technologies                                                                        -7-
Building an Enterprise Metadata Repository


benefits associated with the construction of an enterprise view should yield a high Return on
Investment.
Metadata Repositories are crucial tools to facilitate metadata management; metadata
repository selection is important and the repository should provide key functionality such as
management of unstructured content, import/export for a well-known set of tools, and the
ability to import and export standard formats such as XML.


ABOUT THE AUTHOR
Ron Lewis is an analyst who specializes in application security for CDO Technologies, a systems
integrator that delivers technology-based solutions to government agencies and customers in
the private sector. He has worked in the government and commercial security arena for more
than 15 years identifying and providing guidance for remediating application vulnerabilities.
Ron is considered an industry authority, having authored numerous articles on hardening
applications and the hacker mindset. He is also actively involved in industry organizations and
efforts such as the Open Web Application Security Project (OWASP) and the Oracle
Development Tools User Group (ODTUG).




Embarcadero Technologies                                                                        -8-
Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application
developers and database professionals so they can design systems right, build them faster and
run them better, regardless of their platform or programming language. Ninety of the Fortune
100 and an active community of more than three million users worldwide rely on Embarcadero
products to increase productivity, reduce costs, simplify change management and compliance
and accelerate innovation. The company’s flagship tools include: Embarcadero® Change
Manager™, Embarcadero® RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid
SQL®. Founded in 1993, Embarcadero is headquartered in San Francisco, with offices located
around the world. Embarcadero is online at www.embarcadero.com.

Contenu connexe

Tendances

Metadata in data warehouse
Metadata in data warehouseMetadata in data warehouse
Metadata in data warehouseSiddique Ibrahim
 
Data warehousing
Data warehousingData warehousing
Data warehousingkeeyre
 
Taxonomies and Metadata in Information Architecture
Taxonomies and Metadata in Information ArchitectureTaxonomies and Metadata in Information Architecture
Taxonomies and Metadata in Information ArchitectureAccess Innovations, Inc.
 
Six Ways to Simplify Metadata Management
Six Ways to Simplify Metadata ManagementSix Ways to Simplify Metadata Management
Six Ways to Simplify Metadata ManagementEnterprise Knowledge
 
Webinar: Business Solutions and Metadata Design
Webinar:  Business Solutions and Metadata DesignWebinar:  Business Solutions and Metadata Design
Webinar: Business Solutions and Metadata Designmartingarland
 
Information and Integration Management Vision
Information and Integration Management VisionInformation and Integration Management Vision
Information and Integration Management VisionColin Bell
 
Role of metadata in transportation agency data programs
Role of metadata in transportation agency data programsRole of metadata in transportation agency data programs
Role of metadata in transportation agency data programsJoseph Busch
 
Enterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsEnterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsBoris Otto
 
Metadata strategies for transportation agencies: An information management pe...
Metadata strategies for transportation agencies: An information management pe...Metadata strategies for transportation agencies: An information management pe...
Metadata strategies for transportation agencies: An information management pe...Joseph Busch
 
#SPSVancouver 2016 - The importance of metadata
#SPSVancouver 2016 - The importance of metadata#SPSVancouver 2016 - The importance of metadata
#SPSVancouver 2016 - The importance of metadataVincent Biret
 
Data Mining and Data Warehousing
Data Mining and Data WarehousingData Mining and Data Warehousing
Data Mining and Data WarehousingAswathy S Nair
 
Enterprise Master Data Architecture
Enterprise Master Data ArchitectureEnterprise Master Data Architecture
Enterprise Master Data ArchitectureBoris Otto
 

Tendances (20)

Metadata in data warehouse
Metadata in data warehouseMetadata in data warehouse
Metadata in data warehouse
 
Data warehousing
Data warehousingData warehousing
Data warehousing
 
Taxonomies and Metadata in Information Architecture
Taxonomies and Metadata in Information ArchitectureTaxonomies and Metadata in Information Architecture
Taxonomies and Metadata in Information Architecture
 
Taxonomy And Metadata
Taxonomy And MetadataTaxonomy And Metadata
Taxonomy And Metadata
 
Six Ways to Simplify Metadata Management
Six Ways to Simplify Metadata ManagementSix Ways to Simplify Metadata Management
Six Ways to Simplify Metadata Management
 
Open data quality
Open data qualityOpen data quality
Open data quality
 
Metadata Standards
Metadata StandardsMetadata Standards
Metadata Standards
 
Webinar: Business Solutions and Metadata Design
Webinar:  Business Solutions and Metadata DesignWebinar:  Business Solutions and Metadata Design
Webinar: Business Solutions and Metadata Design
 
Information and Integration Management Vision
Information and Integration Management VisionInformation and Integration Management Vision
Information and Integration Management Vision
 
Role of metadata in transportation agency data programs
Role of metadata in transportation agency data programsRole of metadata in transportation agency data programs
Role of metadata in transportation agency data programs
 
Enterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsEnterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and Options
 
Metadata strategies for transportation agencies: An information management pe...
Metadata strategies for transportation agencies: An information management pe...Metadata strategies for transportation agencies: An information management pe...
Metadata strategies for transportation agencies: An information management pe...
 
#SPSVancouver 2016 - The importance of metadata
#SPSVancouver 2016 - The importance of metadata#SPSVancouver 2016 - The importance of metadata
#SPSVancouver 2016 - The importance of metadata
 
Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010
 
Managing data resources
Managing  data resourcesManaging  data resources
Managing data resources
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
DTL Partners Event - FAIR Data Tech overview - Day 1
DTL Partners Event - FAIR Data Tech overview - Day 1DTL Partners Event - FAIR Data Tech overview - Day 1
DTL Partners Event - FAIR Data Tech overview - Day 1
 
Mendeley Data FAIR hackathon
Mendeley Data FAIR hackathonMendeley Data FAIR hackathon
Mendeley Data FAIR hackathon
 
Data Mining and Data Warehousing
Data Mining and Data WarehousingData Mining and Data Warehousing
Data Mining and Data Warehousing
 
Enterprise Master Data Architecture
Enterprise Master Data ArchitectureEnterprise Master Data Architecture
Enterprise Master Data Architecture
 

En vedette

Jena based implementation of a iso 11179 meta data registry
Jena based implementation of a iso 11179 meta data registryJena based implementation of a iso 11179 meta data registry
Jena based implementation of a iso 11179 meta data registryA. Anil Sinaci
 

En vedette (11)

Jena based implementation of a iso 11179 meta data registry
Jena based implementation of a iso 11179 meta data registryJena based implementation of a iso 11179 meta data registry
Jena based implementation of a iso 11179 meta data registry
 
NISO DCMI Webinar bibframe-20130123
NISO DCMI Webinar bibframe-20130123NISO DCMI Webinar bibframe-20130123
NISO DCMI Webinar bibframe-20130123
 
April 24, 2013 NISO/DCMI Webinar: Deployment of RDA (Resource Description and...
April 24, 2013 NISO/DCMI Webinar: Deployment of RDA (Resource Description and...April 24, 2013 NISO/DCMI Webinar: Deployment of RDA (Resource Description and...
April 24, 2013 NISO/DCMI Webinar: Deployment of RDA (Resource Description and...
 
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
 
NISO/DCMI May 22 Webinar: Semantic Mashups Across Large, Heterogeneous Insti...
 NISO/DCMI May 22 Webinar: Semantic Mashups Across Large, Heterogeneous Insti... NISO/DCMI May 22 Webinar: Semantic Mashups Across Large, Heterogeneous Insti...
NISO/DCMI May 22 Webinar: Semantic Mashups Across Large, Heterogeneous Insti...
 
NISO/DCMI Webinar: Cooperative Authority Control: The Virtual International A...
NISO/DCMI Webinar: Cooperative Authority Control: The Virtual International A...NISO/DCMI Webinar: Cooperative Authority Control: The Virtual International A...
NISO/DCMI Webinar: Cooperative Authority Control: The Virtual International A...
 
NISO/DCMI Webinar: Metadata for Public Sector Administration
NISO/DCMI Webinar: Metadata for Public Sector AdministrationNISO/DCMI Webinar: Metadata for Public Sector Administration
NISO/DCMI Webinar: Metadata for Public Sector Administration
 
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
 
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
Embedding Linked Data Invisibly into Web Pages: Strategies and Workflows for ...
 
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
NISO/DCMI Webinar: Schema.org and Linked Data: Complementary Approaches to Pu...
 
NISO/DCMI Webinar: Metadata for Managing Scientific Research Data
NISO/DCMI Webinar: Metadata for Managing Scientific Research DataNISO/DCMI Webinar: Metadata for Managing Scientific Research Data
NISO/DCMI Webinar: Metadata for Managing Scientific Research Data
 

Similaire à Building an Enterprise Metadata Repository

Solving data discovery in the enterprise
Solving data discovery in the enterpriseSolving data discovery in the enterprise
Solving data discovery in the enterpriseJesus Rodriguez
 
Running head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxRunning head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxhealdkathaleen
 
Running head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxRunning head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxtodd271
 
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...Alan D. Duncan
 
Governance and Architecture in Data Integration
Governance and Architecture in Data IntegrationGovernance and Architecture in Data Integration
Governance and Architecture in Data IntegrationAnalytiX DS
 
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...AnalytixDataServices
 
Data warehouse-dimensional-modeling-and-design
Data warehouse-dimensional-modeling-and-designData warehouse-dimensional-modeling-and-design
Data warehouse-dimensional-modeling-and-designSarita Kataria
 
Metadata Strategies
Metadata StrategiesMetadata Strategies
Metadata StrategiesDATAVERSITY
 
White Paper - How Data Works
White Paper - How Data WorksWhite Paper - How Data Works
White Paper - How Data WorksDavid Walker
 
White Paper - The Business Case For Business Intelligence
White Paper -  The Business Case For Business IntelligenceWhite Paper -  The Business Case For Business Intelligence
White Paper - The Business Case For Business IntelligenceDavid Walker
 
3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.ppt3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.pptBsMath3rdsem
 
Information architecture overview
Information architecture overviewInformation architecture overview
Information architecture overviewJames M. Dey
 
Data Governance challenges in a major Energy Company
Data Governance challenges in a major Energy CompanyData Governance challenges in a major Energy Company
Data Governance challenges in a major Energy CompanyChristopher Bradley
 
Mi0036 business intelligence tools
Mi0036  business intelligence toolsMi0036  business intelligence tools
Mi0036 business intelligence toolssmumbahelp
 
Next generation Data Governance
Next generation Data GovernanceNext generation Data Governance
Next generation Data GovernanceVladimiro Borsi
 
IT_RFO10-14-ITS_AppendixA_20100513
IT_RFO10-14-ITS_AppendixA_20100513IT_RFO10-14-ITS_AppendixA_20100513
IT_RFO10-14-ITS_AppendixA_20100513Alexander Doré
 

Similaire à Building an Enterprise Metadata Repository (20)

Solving data discovery in the enterprise
Solving data discovery in the enterpriseSolving data discovery in the enterprise
Solving data discovery in the enterprise
 
Jn2516891694
Jn2516891694Jn2516891694
Jn2516891694
 
Jn2516891694
Jn2516891694Jn2516891694
Jn2516891694
 
Running head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxRunning head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docx
 
Running head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docxRunning head Database and Data Warehousing design1Database and.docx
Running head Database and Data Warehousing design1Database and.docx
 
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
 
Governance and Architecture in Data Integration
Governance and Architecture in Data IntegrationGovernance and Architecture in Data Integration
Governance and Architecture in Data Integration
 
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...
White Paper-1-AnalytiX Mapping Manager-Governance And Architecture In Data In...
 
Data warehouse-dimensional-modeling-and-design
Data warehouse-dimensional-modeling-and-designData warehouse-dimensional-modeling-and-design
Data warehouse-dimensional-modeling-and-design
 
Metadata Strategies
Metadata StrategiesMetadata Strategies
Metadata Strategies
 
Abstract
AbstractAbstract
Abstract
 
White Paper - How Data Works
White Paper - How Data WorksWhite Paper - How Data Works
White Paper - How Data Works
 
White Paper - The Business Case For Business Intelligence
White Paper -  The Business Case For Business IntelligenceWhite Paper -  The Business Case For Business Intelligence
White Paper - The Business Case For Business Intelligence
 
BI_StrategyDM2
BI_StrategyDM2BI_StrategyDM2
BI_StrategyDM2
 
3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.ppt3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.ppt
 
Information architecture overview
Information architecture overviewInformation architecture overview
Information architecture overview
 
Data Governance challenges in a major Energy Company
Data Governance challenges in a major Energy CompanyData Governance challenges in a major Energy Company
Data Governance challenges in a major Energy Company
 
Mi0036 business intelligence tools
Mi0036  business intelligence toolsMi0036  business intelligence tools
Mi0036 business intelligence tools
 
Next generation Data Governance
Next generation Data GovernanceNext generation Data Governance
Next generation Data Governance
 
IT_RFO10-14-ITS_AppendixA_20100513
IT_RFO10-14-ITS_AppendixA_20100513IT_RFO10-14-ITS_AppendixA_20100513
IT_RFO10-14-ITS_AppendixA_20100513
 

Plus de Embarcadero Technologies

PyTorch for Delphi - Python Data Sciences Libraries.pdf
PyTorch for Delphi - Python Data Sciences Libraries.pdfPyTorch for Delphi - Python Data Sciences Libraries.pdf
PyTorch for Delphi - Python Data Sciences Libraries.pdfEmbarcadero Technologies
 
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...Embarcadero Technologies
 
Linux GUI Applications on Windows Subsystem for Linux
Linux GUI Applications on Windows Subsystem for LinuxLinux GUI Applications on Windows Subsystem for Linux
Linux GUI Applications on Windows Subsystem for LinuxEmbarcadero Technologies
 
Python on Android with Delphi FMX - The Cross Platform GUI Framework
Python on Android with Delphi FMX - The Cross Platform GUI Framework Python on Android with Delphi FMX - The Cross Platform GUI Framework
Python on Android with Delphi FMX - The Cross Platform GUI Framework Embarcadero Technologies
 
Introduction to Python GUI development with Delphi for Python - Part 1: Del...
Introduction to Python GUI development with Delphi for Python - Part 1:   Del...Introduction to Python GUI development with Delphi for Python - Part 1:   Del...
Introduction to Python GUI development with Delphi for Python - Part 1: Del...Embarcadero Technologies
 
FMXLinux Introduction - Delphi's FireMonkey for Linux
FMXLinux Introduction - Delphi's FireMonkey for LinuxFMXLinux Introduction - Delphi's FireMonkey for Linux
FMXLinux Introduction - Delphi's FireMonkey for LinuxEmbarcadero Technologies
 
Python for Delphi Developers - Part 1 Introduction
Python for Delphi Developers - Part 1 IntroductionPython for Delphi Developers - Part 1 Introduction
Python for Delphi Developers - Part 1 IntroductionEmbarcadero Technologies
 
RAD Industrial Automation, Labs, and Instrumentation
RAD Industrial Automation, Labs, and InstrumentationRAD Industrial Automation, Labs, and Instrumentation
RAD Industrial Automation, Labs, and InstrumentationEmbarcadero Technologies
 
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBase
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBaseEmbeddable Databases for Mobile Apps: Stress-Free Solutions with InterBase
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBaseEmbarcadero Technologies
 
Rad Server Industry Template - Connected Nurses Station - Setup Document
Rad Server Industry Template - Connected Nurses Station - Setup DocumentRad Server Industry Template - Connected Nurses Station - Setup Document
Rad Server Industry Template - Connected Nurses Station - Setup DocumentEmbarcadero Technologies
 
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinar
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinarMove Desktop Apps to the Cloud - RollApp & Embarcadero webinar
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinarEmbarcadero Technologies
 
Getting Started Building Mobile Applications for iOS and Android
Getting Started Building Mobile Applications for iOS and AndroidGetting Started Building Mobile Applications for iOS and Android
Getting Started Building Mobile Applications for iOS and AndroidEmbarcadero Technologies
 
ER/Studio 2016: Build a Business-Driven Data Architecture
ER/Studio 2016: Build a Business-Driven Data ArchitectureER/Studio 2016: Build a Business-Driven Data Architecture
ER/Studio 2016: Build a Business-Driven Data ArchitectureEmbarcadero Technologies
 
The Secrets of SQL Server: Database Worst Practices
The Secrets of SQL Server: Database Worst PracticesThe Secrets of SQL Server: Database Worst Practices
The Secrets of SQL Server: Database Worst PracticesEmbarcadero Technologies
 
Driving Business Value Through Agile Data Assets
Driving Business Value Through Agile Data AssetsDriving Business Value Through Agile Data Assets
Driving Business Value Through Agile Data AssetsEmbarcadero Technologies
 
Troubleshooting Plan Changes with Query Store in SQL Server 2016
Troubleshooting Plan Changes with Query Store in SQL Server 2016Troubleshooting Plan Changes with Query Store in SQL Server 2016
Troubleshooting Plan Changes with Query Store in SQL Server 2016Embarcadero Technologies
 

Plus de Embarcadero Technologies (20)

PyTorch for Delphi - Python Data Sciences Libraries.pdf
PyTorch for Delphi - Python Data Sciences Libraries.pdfPyTorch for Delphi - Python Data Sciences Libraries.pdf
PyTorch for Delphi - Python Data Sciences Libraries.pdf
 
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...
Android on Windows 11 - A Developer's Perspective (Windows Subsystem For Andr...
 
Linux GUI Applications on Windows Subsystem for Linux
Linux GUI Applications on Windows Subsystem for LinuxLinux GUI Applications on Windows Subsystem for Linux
Linux GUI Applications on Windows Subsystem for Linux
 
Python on Android with Delphi FMX - The Cross Platform GUI Framework
Python on Android with Delphi FMX - The Cross Platform GUI Framework Python on Android with Delphi FMX - The Cross Platform GUI Framework
Python on Android with Delphi FMX - The Cross Platform GUI Framework
 
Introduction to Python GUI development with Delphi for Python - Part 1: Del...
Introduction to Python GUI development with Delphi for Python - Part 1:   Del...Introduction to Python GUI development with Delphi for Python - Part 1:   Del...
Introduction to Python GUI development with Delphi for Python - Part 1: Del...
 
FMXLinux Introduction - Delphi's FireMonkey for Linux
FMXLinux Introduction - Delphi's FireMonkey for LinuxFMXLinux Introduction - Delphi's FireMonkey for Linux
FMXLinux Introduction - Delphi's FireMonkey for Linux
 
Python for Delphi Developers - Part 2
Python for Delphi Developers - Part 2Python for Delphi Developers - Part 2
Python for Delphi Developers - Part 2
 
Python for Delphi Developers - Part 1 Introduction
Python for Delphi Developers - Part 1 IntroductionPython for Delphi Developers - Part 1 Introduction
Python for Delphi Developers - Part 1 Introduction
 
RAD Industrial Automation, Labs, and Instrumentation
RAD Industrial Automation, Labs, and InstrumentationRAD Industrial Automation, Labs, and Instrumentation
RAD Industrial Automation, Labs, and Instrumentation
 
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBase
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBaseEmbeddable Databases for Mobile Apps: Stress-Free Solutions with InterBase
Embeddable Databases for Mobile Apps: Stress-Free Solutions with InterBase
 
Rad Server Industry Template - Connected Nurses Station - Setup Document
Rad Server Industry Template - Connected Nurses Station - Setup DocumentRad Server Industry Template - Connected Nurses Station - Setup Document
Rad Server Industry Template - Connected Nurses Station - Setup Document
 
TMS Google Mapping Components
TMS Google Mapping ComponentsTMS Google Mapping Components
TMS Google Mapping Components
 
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinar
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinarMove Desktop Apps to the Cloud - RollApp & Embarcadero webinar
Move Desktop Apps to the Cloud - RollApp & Embarcadero webinar
 
Useful C++ Features You Should be Using
Useful C++ Features You Should be UsingUseful C++ Features You Should be Using
Useful C++ Features You Should be Using
 
Getting Started Building Mobile Applications for iOS and Android
Getting Started Building Mobile Applications for iOS and AndroidGetting Started Building Mobile Applications for iOS and Android
Getting Started Building Mobile Applications for iOS and Android
 
Embarcadero RAD server Launch Webinar
Embarcadero RAD server Launch WebinarEmbarcadero RAD server Launch Webinar
Embarcadero RAD server Launch Webinar
 
ER/Studio 2016: Build a Business-Driven Data Architecture
ER/Studio 2016: Build a Business-Driven Data ArchitectureER/Studio 2016: Build a Business-Driven Data Architecture
ER/Studio 2016: Build a Business-Driven Data Architecture
 
The Secrets of SQL Server: Database Worst Practices
The Secrets of SQL Server: Database Worst PracticesThe Secrets of SQL Server: Database Worst Practices
The Secrets of SQL Server: Database Worst Practices
 
Driving Business Value Through Agile Data Assets
Driving Business Value Through Agile Data AssetsDriving Business Value Through Agile Data Assets
Driving Business Value Through Agile Data Assets
 
Troubleshooting Plan Changes with Query Store in SQL Server 2016
Troubleshooting Plan Changes with Query Store in SQL Server 2016Troubleshooting Plan Changes with Query Store in SQL Server 2016
Troubleshooting Plan Changes with Query Store in SQL Server 2016
 

Dernier

2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilV3cube
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 

Dernier (20)

2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 

Building an Enterprise Metadata Repository

  • 1. White Paper Building an Enterprise Metadata Repository Ron Lewis, CDO Technologies December 2009 Corporate Headquarters EMEA Headquarters Asia-Pacific Headquarters 100 California Street, 12th Floor York House L7. 313 La Trobe Street San Francisco, California 94111 18 York Road Melbourne VIC 3000 Maidenhead, Berkshire Australia SL6 1SF, United Kingdom
  • 2. Building an Enterprise Metadata Repository CONTENTS Introduction ............................................................................................................................. - 2 - First Step: Decide What Needs to be Collected ..................................................................... - 3 - Second Step: Collecting the Metadata .................................................................................. - 4 - Third Step: Populating the Metadata Repository .................................................................. - 6 - Selecting the Right Tools......................................................................................................... - 6 - Summary .................................................................................................................................. - 7 - About the Author .................................................................................................................... - 8 - Embarcadero Technologies -1-
  • 3. Building an Enterprise Metadata Repository INTRODUCTION The importance of capturing metadata has been a topic of many webinars, teleconferences, and white papers over the last several years. There’s has also been an increasing emphasis on “building metadata repositories”. To avoid being just another white paper describing the “metadata trend” or promoting a particular metadata repository solution – the intent of this whitepaper is to provide basic definitions for the concepts of metadata and metadata repositories, as well as to provide a basic methodology for collecting metadata and populating an enterprise metadata repository. IT ALL BOILS DOWN TO THE DATA A good starting point is defining the difference between data and information. Data is nothing more than collections of raw facts. Data is transformed into information as result of the manipulating or processing of these “raw facts” by putting them into meaningful contexts. Data is often referred to as the “crown jewels” of an organization. This is a valid analogy; an organization’s data is typically priceless to that organization. The financial health and profitability is often tied to how well a business entity utilizes its data resources. As technology has evolved, executive leaders have become increasingly more aware of the importance of data to the efficiency of an organization. A lot of emphasis is being placed on methodologies and frameworks—a couple good examples would be LEAN Engineering Principles and the Zachman Framework—to provide additional insight into how to streamline a company or business’ effectiveness. There’s a heavy focus on mapping business processes and to the corporate data upon which the processes rely. This focus introduced the need for metadata management. METADATA PROVIDES CONTEXT Metadata describes data and is used to enhance the effectiveness of data use. Metadata is often categorized as either business or technical metadata. Business metadata describes taxonomies, articulates business rules, and establishes common vocabularies. This helps align data with its business context. Conversely, technical metadata describes data sources, attributes, domains, nomenclature, movement, and consumption rules. The bottom line: Metadata is used to identify the context in which data becomes meaningful. METADATA REPOSITORIES STORE ALL YOUR PROVERBIAL EGGS IN ONE BASKET When it comes to metadata, having all your proverbial eggs in one basket is a good thing. Metadata, especially when aggregated properly, can expose new ways of exploiting data to significantly increase efficiency and profitability. In order to glean the full value from collected metadata it’s important to store metadata in a manner where it can be easily indexed, cataloged, and searched. A metadata repository facilitates this. A metadata repository is a system for aggregating, indexing, cataloging, protecting and providing access to corporate metadata. This is no small task. Storing metadata collectively is challenging because there are many different types of metadata, and many different means by which metadata can be expressed. Embarcadero Technologies -2-
  • 4. Building an Enterprise Metadata Repository As stated above, there are two basic categories of metadata: business and technical. Below are a few examples of crucial metadata and a few of the forms by which it is normally expressed. This is by no means an exhaustive list, but is provided to help scope the metadata collection task. Crucial business metadata: o Business Vocabularies: describe terms common to the organization and are built of Business Definitions o Business Definitions: provide a common meaning to a common term o Business Processes: groups of business activities that center around a business purpose and governed by business rules o Business Rules: define the organization and how it achieves it business goals Critical technical metadata: o Data Models o System Catalogs o Extract, Transform, and Load Scripts o Data Lineage o Data Consumption Rules A few common means by which metadata is articulated: o Use Cases: Often used to describe business processes in software development terms o Business Models: Used to facilitate communication between business and systems analysts o Data Models: Used to describe relationships between data elements A metadata repository solution should be capable of collecting all of these bits of data in a readily searchable, protected form. Quick rule of thumb concerning metadata repository security: The value of the metadata is proportionate to the perceived quality and reliability of the metadata repository contents. The metadata is incredibly valuable and should be adequately protected. FIRST STEP: DECIDE WHAT NEEDS TO BE COLLECTED There is obviously lots of metadata that can be collected and housed in a repository. Knowing what needs to be collected is definitely a huge challenge. Deciding what needs to be collected is viewed as an enterprise engineering task. Enterprise architects typically describe an organization using a formal, structured framework, such as Zachman, to define the “why, how, what, who, where, and when” associated with an enterprise. This is an expensive and time consuming endeavor but is also well worth the investment. Embarcadero Technologies -3-
  • 5. Building an Enterprise Metadata Repository REVERSE ENGINEERING DATA COLLECTION NEEDS FROM KEY REPORTING REQUIREMENTS A simple method for getting started for diverse, well-structured organization is to iteratively collect data associated with the business processes that define the enterprise reverse engineering from the reports that the enterprise relies upon. For example, a medical practice can be decomposed into several large building blocks—such as doctors, patients, visits, and perhaps treatments. A medical practice requires several different types of key reports, such as billing, treatment record, medical record requests, and HIPAA release forms-- to name a few (disclaimer: this is only for illustrative purposes). Start with the most frequently used “reports” and identify the information necessary to complete the tasks associated with the large building blocks. Reports encapsulate critical business rules, and key business attributes. A lesson learned from the security industry is reports often put business data in its most meaningful context and are prime targets of cyber-attackers. This method for determining metadata needs leverages this security knowledge. Since reports provide easy-to-understand, readily accessible mappings between business needs and the supporting data--the answer to these two questions: “What information do the reports describe” and “what is necessary to build them” provide a good starting point for identifying the most important metadata to be collected. SECOND STEP: COLLECTING THE METADATA THE TRADITIONAL METHOD Many enterprise architects start with building business process models, and then map the business process models to conceptual data models. This requires business architects to interview key business analysts to derive and validate the business models. It also requires data architects to interview key technical personnel to build and validate the conceptual data models. Once the models and mappings have been built—enterprise architects then map these to systems and data repositories identifying key dependencies between systems. The correlating metadata defining the AS-IS is then collected, cataloged, indexed, and housed in a metadata repository. KEY CHALLENGES Managing Cost: Initial metadata collection activities associated with the manual effort for describing the existing environment (e.g., the “AS-IS”) are expensive, both in terms of cost and time. Manual interview processes are disruptive. Interview processes have a hidden cost in lost production; they pull key personnel away from revenue generating tasks. In rapidly evolving, high-demand environments—this loss can be devastating. Managing Scope: There are two common scope-related tendencies during initial data collection efforts: to focus on collecting everything, or to get buried in the details. Both of these cause a loss of momentum and end up increasing cost significantly. Staying focused on the data collection: It’s tempting, especially when glaring inefficiencies are discovered, to stop the data collection activities and focus on addressing and correcting the discrepancies. This can have huge negative impact due to the ripple effect changes can cause across an organization. Embarcadero Technologies -4-
  • 6. Building an Enterprise Metadata Repository SOLUTIONS Summing up what seems to work best in most environments to counter these challenges: o Focus on what’s important. Start with the organization’s main focus and then quantify each activity based on the percentage of revenue for the organization. o Leverage automation to the greatest degree. Eliminate as many of the manual interviews as possible and glean as much business information by decomposing reports, reverse engineering applications, and current software development documentation. Caution: Not all software development artifacts will match what’s been deployed into production. o Do not perform data re-engineering or process improvement during data collection. Focus the effort on data collection. When all the metadata has been collected, cataloged, and indexed, business analysts will often get a clear picture of inefficiencies across the enterprise.   A BETTER WAY – USE TOOLS A prime solution to reduce the time and cost while increasing the overall effectiveness of the data collection activities is to leverage technology. It’s best to start with the reports and work backwards towards deriving processes. The reports and associated metadata collected provide a good means for validating the processes, provide a common frame of reference for all involved in the interview process, and minimizes number of interviews necessary to fully capture the enterprise view. Perform the following actions using tools: o Identify as many of the database servers within the enterprise and reverse engineer the physical schemas contained within each database server. o Build a single enterprise data model and include each reverse engineered physical schema in the master model. o Export the schema metadata to a spreadsheet and have the appropriate data architects annotate what is known about each entity and associated attributes. o Instrument and profile each application with an application protocol monitor to capture business rules captured as programmatic logic-thereby capturing the data consumption rules. o Extract business logic from application source code o Capture transformation rules and data lineage metadata by profiling extract, transform, and load (ETL) scripts. This is accomplished by instrumenting each database server and capturing use with a database profiling tool. o Import any existing business process models (BPM) into a standard BPM tool. o Profile data use, focusing on identification of dormant data as well as high- utilization data—databases, tables, and elements. o Evaluate the metadata captured for consistency. Identify and annotate any discrepancies discovered.   Embarcadero Technologies -5-
  • 7. Building an Enterprise Metadata Repository THIRD STEP: POPULATING THE METADATA REPOSITORY Once the above steps have been completed, there will be a significant amount of metadata. To glean the value from the metadata, it needs to be imported in a common repository, cataloged, indexed, and made available to the appropriate business, system, and data analysts. There are plenty of pre-built metadata repositories. The metadata repository solution selected must have the following critical capabilities: o Manages unstructured content. o Provides appropriate access controls and auditing. o Imports myriad formats and exports in a standard format such as XML. o Allow the creation and associations of descriptions to each metadata asset. o Provides a searchable and intuitive means for finding and evaluating the appropriate metadata. The steps for populating a metadata repository will vary based on the particular solution; however, the methodology is pretty standard. Each solution should provide import and export mechanisms that allow data bridging between metadata collection tools. For collection tools not natively supported by the repository solution, a commonly understood format, such as XML, can be used as an intermediary bridge. For example--if metadata describing data nomenclature has been captured in a loosely structured tool, such as Microsoft (MS) Excel, the metadata can be exported as a comma-separated-value (CSV) file and imported back into a supported data modeling tool. Most data modeling tools allow users to export metadata in XML format. While an MS Excel file would most likely normally be captured in the repository as “unstructured content”—it can be converted to structured content by importing the appropriate metadata into the data modeling tool and exporting as schema metadata. This example, of course, assumes that the metadata articulated in the spreadsheet is associated with either logical or physical data models. SELECTING THE RIGHT TOOLS Selecting the right tools for collecting metadata is critical. The tools should be intuitive, and easy-to-use. More importantly, the tools need to integrate well with one another to minimize the number of tool bridging required. (Note: each time data is exported from one tool and imported to another there is a risk that metadata will be lost or altered.) The tools should also be accepted as an industry standard; this ensures that whatever metadata repository solution selected, the tools will be supported. THIS AUTHOR’S TOOL CHOICE It’s important to have tools that are comfortable, reliable, and easy-to-demonstrate. I really like the Embarcadero solution set. The integration provided by the new Embarcadero All Access solution further solidified my choice. I’ve supplemented the toolset with NitroSecurity’s NitroView Application Protocol Monitor (APM) to allow the visibility necessary to profile legacy applications to extract business logic articulated as .NET and Java Code. Embarcadero Technologies -6-
  • 8. Building an Enterprise Metadata Repository METADATA COLLECTION TOOLS Embarcadero® ER/Studio® Data Architect for reverse engineering physical schemas, building the associated conceptual and logical models, and for managing schema related metadata. It is well suited for capturing and managing enterprise transformation, consumption, and data lineage metadata. Each reverse-engineered schema can be added to a master data model as a sub-model to the master and the master can be auto-generate a master data catalog. ER/Studio Data Architect supports creation of meta-data tags which are useful for meeting regulatory requirements, such as identify PHI and PII for HIPAA or FISMA respectively. It also supports creation and management of aliases, and provides a good foundation for data governance. Embarcadero® ER/Studio® Business Architect for importing existing business process models and for building new ones. Embarcadero ® ER/Studio® Enterprise. The latest version of ER Studio allows Data Architect and Business architect to integrate into the ER Studio Repository. This allows business process metadata and data metadata to coexist in a web-accessible repository. DATABASE POLLING TOOLS Embarcadero® DB Optimizer™. This provides the ability to capture sessions and filter them by database and table. It is well suited for capturing key performance parameters that are critical for data warehousing construction. It also provides a means of identifying which ETL scripts are being utilized and for validating data lineage. APPLICATION PROFILING TOOLS Embarcadero® JBuilder™ for evaluating legacy J2EE applications and mapping to business cases NitroView APM for mapping application roles to business functions and for exposing business rules in near-real time. The output from APM can be correlated with the DBOptimizer output to provide a very accurate picture of which users are accessing which data, even when legacy applications use connection pooling or shared application user accounts. Most of the metadata is stored in the ER/Studio Repository and made available via the ER Studio Portal included in the ER/Studio Enterprise. The integration of Data Architect and Business Architect into a single repository, as well as the functionality provided in the All Access Toolkit makes All Access a good fit for metadata collection and management. SUMMARY The intent of this whitepaper was to provide basic definitions for the concepts of metadata and metadata repositories, as well as to provide a basic methodology for collecting metadata and populating an enterprise metadata repository. Three key points hopefully gleaned: In order to reap the full benefit from enterprise data assets, an organization must properly collect and manage enterprise metadata. Collecting the metadata across an enterprise can be expensive, although worthwhile endeavor; the cost can be significantly reduced through proper use of the right tools; and the tangible Embarcadero Technologies -7-
  • 9. Building an Enterprise Metadata Repository benefits associated with the construction of an enterprise view should yield a high Return on Investment. Metadata Repositories are crucial tools to facilitate metadata management; metadata repository selection is important and the repository should provide key functionality such as management of unstructured content, import/export for a well-known set of tools, and the ability to import and export standard formats such as XML. ABOUT THE AUTHOR Ron Lewis is an analyst who specializes in application security for CDO Technologies, a systems integrator that delivers technology-based solutions to government agencies and customers in the private sector. He has worked in the government and commercial security arena for more than 15 years identifying and providing guidance for remediating application vulnerabilities. Ron is considered an industry authority, having authored numerous articles on hardening applications and the hacker mindset. He is also actively involved in industry organizations and efforts such as the Open Web Application Security Project (OWASP) and the Oracle Development Tools User Group (ODTUG). Embarcadero Technologies -8-
  • 10. Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application developers and database professionals so they can design systems right, build them faster and run them better, regardless of their platform or programming language. Ninety of the Fortune 100 and an active community of more than three million users worldwide rely on Embarcadero products to increase productivity, reduce costs, simplify change management and compliance and accelerate innovation. The company’s flagship tools include: Embarcadero® Change Manager™, Embarcadero® RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid SQL®. Founded in 1993, Embarcadero is headquartered in San Francisco, with offices located around the world. Embarcadero is online at www.embarcadero.com.