Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Data Management PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Management Powerpoint Presentation Slides. We bring to you to the point topic specific slides with apt research and understanding. Putting forth our PPT deck comprises of twenty-seven slides. Our tailor-made Data Management Powerpoint Presentation Slides editable presentation deck assists planners to segment and expound the topic with brevity. The advantageous slides on Data Management Powerpoint Presentation Slides are braced with multiple charts and graphs, overviews, analysis templates agenda slides etc. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
What is Data? What are data types? Tools for data collection & data management
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. ... Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices.
Data Privacy in the DMBOK - No Need to Reinvent the WheelDATAVERSITY
World wide, Data Privacy laws are increasing. Customers are increasingly aware, and concerned, about how data is processed. The Chief Privacy Officer is (or should be) a key stakeholder for many Data Governance initiatives, and new terms like “Privacy by Design” and “Privacy Engineering” are entering our conversations with peers. Non-EU organizations selling into the EU will soon have to comply with EU Data Privacy laws. However, data professionals who take a structured, principles based approach, to building their Data Privacy capabilities stand a better chance of sustainable success than those who don’t. Rather than reinventing the wheel, organizations should look at how the DMBOK framework, in conjunction with other approaches and methods, can provide a robust platform for Data Privacy initiatives in their organizations.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Data Management PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Management Powerpoint Presentation Slides. We bring to you to the point topic specific slides with apt research and understanding. Putting forth our PPT deck comprises of twenty-seven slides. Our tailor-made Data Management Powerpoint Presentation Slides editable presentation deck assists planners to segment and expound the topic with brevity. The advantageous slides on Data Management Powerpoint Presentation Slides are braced with multiple charts and graphs, overviews, analysis templates agenda slides etc. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
What is Data? What are data types? Tools for data collection & data management
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. ... Managing digital data in an organization involves a broad range of tasks, policies, procedures, and practices.
Data Privacy in the DMBOK - No Need to Reinvent the WheelDATAVERSITY
World wide, Data Privacy laws are increasing. Customers are increasingly aware, and concerned, about how data is processed. The Chief Privacy Officer is (or should be) a key stakeholder for many Data Governance initiatives, and new terms like “Privacy by Design” and “Privacy Engineering” are entering our conversations with peers. Non-EU organizations selling into the EU will soon have to comply with EU Data Privacy laws. However, data professionals who take a structured, principles based approach, to building their Data Privacy capabilities stand a better chance of sustainable success than those who don’t. Rather than reinventing the wheel, organizations should look at how the DMBOK framework, in conjunction with other approaches and methods, can provide a robust platform for Data Privacy initiatives in their organizations.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house the metadata that builds organizational confidence in your data. First and foremost, the people in your organization need to be engaged in leveraging the tools, understanding the data that is available and who is responsible for the data, and knowing how to get their hands on the data they need to perform their job function. This metadata will not govern itself.
Join Bob Seiner for the April RWDG webinar, where he will discuss how to govern the metadata in a data catalog, business glossary, and data dictionary. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must be governed. Learn how to govern that metadata in this webinar.
Bob will discuss the following subjects in this webinar:
• Successful Data Governance relies on value from very important tools
• What it means to govern your data catalog, business glossary, and data dictionary
• Why governing the metadata in these tools is so important
• The roles necessary to govern these tools
• Value expected from governing the catalog, glossary, and dictionary
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
<!-- wp:paragraph -->
<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This program's learning objectives include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
<!-- /wp:list -->
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Melding the planets of Java and JavaScript, JavaPoly.js expands local Java Virtual Machine assistance to internet explorer via a collection offering as a polyfill.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house the metadata that builds organizational confidence in your data. First and foremost, the people in your organization need to be engaged in leveraging the tools, understanding the data that is available and who is responsible for the data, and knowing how to get their hands on the data they need to perform their job function. This metadata will not govern itself.
Join Bob Seiner for the April RWDG webinar, where he will discuss how to govern the metadata in a data catalog, business glossary, and data dictionary. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must be governed. Learn how to govern that metadata in this webinar.
Bob will discuss the following subjects in this webinar:
• Successful Data Governance relies on value from very important tools
• What it means to govern your data catalog, business glossary, and data dictionary
• Why governing the metadata in these tools is so important
• The roles necessary to govern these tools
• Value expected from governing the catalog, glossary, and dictionary
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
<!-- wp:paragraph -->
<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>This program's learning objectives include:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
<!-- /wp:list -->
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Melding the planets of Java and JavaScript, JavaPoly.js expands local Java Virtual Machine assistance to internet explorer via a collection offering as a polyfill.
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
DISCUSSION 15 4All students must review one (1) Group PowerP.docxcuddietheresa
DISCUSSION 15 4
All students must review one (1) Group PowerPoint Presentation from another group and complete the follow activities:
1. First each student (individually) must summarize the content of the PowerPoint of another group in 200 words or more.
2. Additionally each student must present a detailed discussion of what they learned from the presentation they summarized and discuss the ways in which they would you use this information in their current or future profession.
PowerPoint is attached separately
Homework
Create a new product that will serve two business (organizational) markets.
Write a 750-1,000-word paper that describes your product, explains your strategy for entering the markets, and analyzes the potential barriers you may encounter. Explain how you plan to ensure your product will be successful, given your market strategy.
Include an introduction and conclusion that make relevant connections to course objectives.
Prepare this assignment according to the APA guidelines found in the APA Style Guide
Management Information Systems
Campbellsville University
Week 15: PowerPoint Presentation
Topic: Data
Group: E
GROUP MEMBERS FULL NAME
Data
Data can be defined as a specific piece of information or a basic building block of information.
Data is stored in files or in databases.
Data can be presented into tables, graphs or charts, so that legitimate and analytical results can be derived from the gathered information.
An authentic data is very important for the smooth running of any business organizations. It helps IT managers to make effective decisions. Data helps to interpret and enhance overall business processes (Cai & Zhu, 2015).
Uses of Data
The main purpose of data is to keep the records of several activities and situations.
Gathering data helps to better understand the interest of customers which can enhance the sales of organization (Haug & Liempd, 2011).
Relevant data assists in creating strong business strategies.
Use of big data helps to promote service support to the customers. It also helps organizations to find new markets and new business opportunities.
After all, data plays a great role in running the company more effectively and efficiently.
Data Management
Data management is the implementation of policies and procedures that put organizations in control of their business data regardless of where it resides. Data management is concerned with the end-to-end lifecycle of data, from creation to retirement, and the controlled progression of data to and from each stage within its lifecycle (Dunie, M. 2017).
Data Management
Information technology has evolved to deal with the most important data management computer science which helps the computer leads to the advantage of a navigable and transparent communication space.
Large volumes of data can be processed and managed with the help of management systems through the methods of algebra with applications in economic engineering especially in ...
CHAPTER5Database Systemsand Big DataRafal OlechowsJinElias52
CHAPTER
5
Database Systems
and Big Data
Rafal Olechowski/Shutterstock.com
Copyright 2018 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203
Know?Did Yo
u
• The amount of data in the digital universe is expected
to increase to 44 zettabytes (44 trillion gigabytes) by
2020. This is 60 times the amount of all the grains of
sand on all the beaches on Earth. The majority of
data generated between now and 2020 will not be
produced by humans, but rather by machines as they
talk to each other over data networks.
• Most major U.S. wireless service providers have
implemented a stolen-phone database to report and
track stolen phones. So if your smartphone or tablet
goes missing, report it to your carrier. If someone else
tries to use it, he or she will be denied service on the
carrier’s network.
• You know those banner and tile ads that pop up on
your browser screen (usually for products and
services you’ve recently viewed)? Criteo, one of
many digital advertising organizations, automates the
recommendation of ads up to 30 billion times each day,
with each recommendation requiring a calculation
involving some 100 variables.
Principles Learning Objectives
• The database approach to data management has
become broadly accepted.
• Data modeling is a key aspect of organizing data and
information.
• A well-designed and well-managed database is an
extremely valuable tool in supporting decision making.
• We have entered an era where organizations are
grappling with a tremendous growth in the amount of
data available and struggling to understand how to
manage and make use of it.
• A number of available tools and technologies allow
organizations to take advantage of the opportunities
offered by big data.
• Identify and briefly describe the members of the hier-
archy of data.
• Identify the advantages of the database approach to
data management.
• Identify the key factors that must be considered when
designing a database.
• Identify the various types of data models and explain
how they are useful in planning a database.
• Describe the relational database model and its funda-
mental characteristics.
• Define the role of the database schema, data definition
language, and data manipulation language.
• Discuss the role of a database administrator and data
administrator.
• Identify the common functions performed by all data-
base management systems.
• Define the term big data and identify its basic
characteristics.
• Explain why big data represents both a challenge and
an opportunity.
• Define the term data management and state its overall
goal.
• Define the terms data warehouse, data mart, and data
lakes and explain how they are different.
• Outline the extract, transform, load process.
• Explain how a NoSQL database is different from an
SQL database.
• Discuss the whole Hadoop computing environment and
its various components.
• Define the term in-memory database and ex ...
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
This presentation will cover the definition of Master Data Management, describe potential MDM hub architectures, outline 5 essential elements of MDM, and describe 11 real-world best practices for MDM and data governance, based on years of experience in the field.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Running head DATABASE AND DATA WAREHOUSING DESIGNDATABASE AND.docxtodd271
Running head: DATABASE AND DATA WAREHOUSING DESIGN
DATABASE AND DATA WAREHOUSING DESIGN 10
Database and Data Warehousing Design
Necosa Hollie
Dr. Ford
Information Systems Capstone CIS499
May 5, 2019
Introduction
Somar and Co. Data Collection Company collects and analyzes data by using operational systems and web analytics. The data used in the analysis is collected from diverse operating systems such as ERP software. Various applications such as payrolls, human resources, and insurance claims are used in, modern-day enterprises and data from them keep on increasing day by day (Schoenherr, & Speier‐Pero, 2015). The ever-increasing data has been overwhelming organizations’ ability to analyze it due to its complex nature. This challenge has forced Somar and Co. Data Collection Company to seek a solution to it to deliver quality results to their clients. As the chief information officer (CIO) at the company, will be in charge of designing the solution that will incorporate data warehousing. This will make it possible to be consolidating large amounts of data quickly and be creating quality analytical reports within the shortest time possible.
Need for Data Warehousing
Data warehouses are central storage systems in companies where vital information from other applications such as ERP system is deposited. The data is periodically extracted from these applications. Data is sent to the data warehouse in different formats as different applications have distinct ways of keeping information. Then the data warehouse by having a uniform operational system will process and analyze discrete data into a more straightforward form. Somar and Co. Data Collection Company manages data from various clients with the information having been collected from multiple departments such as marketing, sales, and finance. To develop an active data warehouse, data consistency from different applications plays a crucial part (Waller, & Fawcett, 2013). This enables establishing of a constant process for all types of data. The information is analyzed for analytical reports, market research and decision report. The processed data also gives insight about the direction of the company to the management. The data is considered by the management during decision making and strategic planning.
Due to the importance of the data reposted in the data warehouse to the management, it should be analyzed in such a way that it is easy to comprehend and interpret (Schoenherr, & Speier‐Pero, 2015). As the processed data originates from different departments of the organization, this makes it be a reliable source of information to the management. If every department were to analyze its data, this would result in different information in different formats hence tricky for the administration to interpret it accurately. The data warehouse helps to resolve this problem by offering a centralized syste.
A deck on the basics of data, for those who did not know that data was actually the plural of datum :) just kidding, hopefully an interesting quick read into a simple breakdown of how data works and what jobs there may be in data.
Do you think you have tour Enterprise Content Management Right? then think again because if your staff are using Microsoft and Google products, drop box or box then I think you no longer have an efficient data retrieval process.
http://www.embarcadero.com
Data yields information when its definition is understood or readily available and it is presented in a meaningful context. Yet even the information that may be gleaned from data is incomplete because data is created to drive applications, not to inform users. Metadata is the data that holds application
data definitions as well as their operational and business context, and so plays a critical role in data and application design and development, as well as in providing an intelligent operational environment that's driven by business meaning.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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UiPath Test Automation using UiPath Test Suite series, part 5
Data Management
1. Data Management and Emergence of Data
Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and
enhance the value of data and information assets.
Data is one of your organization’s most valuable resources. When fully leveraged, it will help your organization control costs, understand
your customers and the market and, ultimately, improve your bottom line. This takes your data beyond basic integration and turning it into
insightful and actionable information
Data collection and processing features are managed by the DMS Service – a Windows service that runs unattended.
DMS Service performs the following:
Communications (telemetry) management ,configuration and management
Data collection and storage to a database management system (DBMS)
Data dissemination (DBMS, serial, TCP/IP, email, SMS)
DMS Plug-ins enable clients to customize their software package based on the sensors used in their system and the type of information they
need to view from the acquired data.
DMS includes two software applications for the presentation of acquired data: desktop application, a web application.
Types
Content Management Software
Content management software (CM) is used to collaboratively create, edit, review, index, search, translate, publish and archive various
types of digital media and electronic text.
Education Management Software
Education management software is used by teachers, students, and school administrators for organization and collaboration, and to
facilitate learning. Learn More about Education Management Software
Learning Management Systems (LMS)
Learning management systems (LMS) are software applications for delivering, tracking and managing training. They are used mainly by
educational institutions and corporate training departments
Career Management and Placement Services
Career management, development and placement services include consultants, businesses, organizations and employment agencies that
provide information and resources related to employment and career direction.
Thermal Management Design and Analysis Services
Thermal management design and analysis services perform tests and redesigns around thermal dissipation issues.
2. Facility Management Services
Facility management services perform building operations and maintenance, project management, subcontractor management,
energy management, budget planning, commissioning and de-commissioning services for buildings and facilities.
Marketing Resource Management Software
Marketing Resource Management Software automates the process of completing marketing work.
Document Management Software
Document management software (DM) enables organizations to create, capture, store, index, and retrieve information digitally.
Knowledge Management Software
Knowledge management software (KM) is used to manage the way that information is collected, stored, and retrieved.
Performance Management Software
Performance Management Software is used for reporting and analysis of tracking your Key Performance Indicators (KPIs), incident data and
other variables or a project, employee or enterprise.
Approaches to Data Management
Master data management (MDM), for example, is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called
a master file, that provides a common point of reference. The effective management of corporate data has grown in importance as businesses
are subject to an increasing number of compliance regulations. Furthermore, the sheer volume of data that must be managed by organizations
has increased so markedly that it is sometimes referred to as big data.
Data Management - Book of Knowledge (DMBoK)
A team of data management professionals produced "The DAMA Guide to the Data Management Body of Knowledge" (DAMA-
DMBOK Guide), under the guidance of a DAMA-DMBOK Editorial Board. The publication was made available in April, 2009.
The “body of knowledge” about data management is quite large and constantly growing. It provides a “definitive introduction” to
data management and defines a standard industry view of data management functions, terminology and best practices, without
detailing specific methods and techniques. The DAMA-DMBOK is not a complete authority on any specific topic, but is on
source of information from widely recognized publications, articles and websites for further reading.
The figure below provides an overview of the major areas (bold) with some of the basics functions that described.
3. Information Management
Data Resource Management or Information Resources Management are terms that have been synonymous with organizations who manage data. But the
implications of the following questions within an organization are critical for the growth, stability, and delivery of business results: who gets what data and
who converts data into information; who balances the competing interests of leaders and followers; and who benefits from the stewardship (not the
ownership) of the data; and how does the choice of implementation of information technologies affect organizational survival. So, without a sound set of
principles, practices, tools, techniques, and decision criteria, the organization can be severely constrained in meeting its targeted goals. Data Management
provides the foundation to organization survival and information security.
Having an organization who focuses on information and data management helps to catalog, assess, validate, and determine the viability of the data
resource. Along with decision-making, managing of data is essentially for making good, reliable business decisions.
Increase in the Growth of Data
Changes in solid state electronics, communication infrastructure, miniaturization of computing devices will dynamically influence the growth of data. In the
data management world, there is discussion of structured (housed in files, databases, etc., where it is organized using an explicit structure ) compared to
unstructured data, such as: email, bitmap images/objects, or text which is not part of a database. Actually, the common nomenclature being used is
"unstructured" but really it has a very complex structure.
By analogy, data is like a book in the library. It’s great when you can go into a library, search the catalog to locate the book, go to the shelf, open the book
and find the information for which you were looking. Data in many forms is like the thousands of books in a library. Like a library book, data needs to be
cataloged so it can be properly accessed. This cataloguing function results in data about the data or data resource data (some call it metadata). Without
such data (the library card catalog), we won’t easily find our book and its content.
We have a similar example in the business environment. We create a spreadsheet that provides information about our products and their prices. We name
the spreadsheet abc.xls on our personal computer. We created it today (when) but, we do not provide any additional information about where the data
came from (it's source), the purpose for which we need it (reasons why), who else needs this information (either internally or externally), or how we actually
created the information (if calculations or special programs were used to complete the request for the data). The data has significant meaning since it is the
means by which we search, access, and provide data meaning to others. It helps to provide the overall context for the use of abc.xls.
Within the spreadsheet, we have captured other data. For each column, we have created a column name that describes the content of the column. For
example, customer name, customer number, order date, product name, product number, description, quantity that was sold and the price the customer paid
for it on that date. We also include the cost of the product to calculate the net profit made on the sale. Down the rows, we have listed each customer who
purchased the products.
4. Now, most of us can relate to this spreadsheet since it is a typical example of business sales information. But it does raise some interesting questions.
What is a sale? Is it the day that the customer ordered it? Is it the day that we delivered it? Is it the day that the customer paid for it? So, when is a sale a
“sale”?
As we can see from this spreadsheet example, various interpretations and implications are made based upon the understanding of what the data
represents. If definitions of the data are not available, commonly understood terms may be misinterpreted by your employees and customers. Your
organization now has a data integrity problem, which is called "data chaos".
Stages of Data management
Without some framework for data and information quality, it is difficult (if not impossible) to manage and change your business. The following
framework defines stages of development of your data management activities. Six (6) measurement categories span the five (5) stages of
maturity.
MeasurementCategoryorStage:
Leadershipunderstandingandattitude
Uncertain: No leadership understanding of the issue
Awakening: Willing to invest time and money to investigate.
Defined: Become knowledgeable and supportive of effort
Managed: Take on a participative role
Certainty: Information quality becomes a key company strategy
QualityOrganizationstatus
Uncertain: Quality is built into software application and tools
Awakening: Emphasis to correct bad data and metadata
Defined: Formalize data quality organization
Managed: Participates with CIO in management
Certainty: Information and Data Quality is foremost concern
Dataqualityproblemhandling
Uncertain: No formal process defined
Awakening: Short-term team handle major problem
Defined: Problems faced openly
Managed: Proactive problem recognition of data quality issues
Certainty: Most data quality problems prevented
Costofinformationquality
Uncertain: Unknown
Awakening: Reporting of some items
Defined: Open Reporting of all items
Managed: Improved savings drives new opportunities
Certainty: Significant data quality cost savings achieved
QualityImprovement
Uncertain: No data quality process
Awakening: Short-term data quality effects observed
5. Defined: Development as a key program/initiative
Managed: Data Quality process becomes effective and efficient
Certainty: Normal and continued process improvement
Companyposture
Uncertain: Don't know why there is a Data Quality problem occurring
Awakening: Some recognition of data quality problem
Defined: Start to resolve major data quality problems
Managed: Recognize that Data Error prevention is a key business operation
Certainty: Know reasons for data quality problems
Remember data is the source of the enterprise knowledge. Measuring it has value -- just as valuable as measuring your business’ financial worth
because it creates value either by design or by default. By default is not acceptable in today’s marketplace in light of the changes in solid state
electronics, communication infrastructure, and the miniaturization of computing devices that will dynamically influence the exponential growth of
data!
Reason For Emergence of Data
Increase in computational power as described by Moore’s law
Number of internet enabled data generating devices; majorly known as M2M
Falling cost of data storage devices. i.e. data is available to everybody virtually free or no cost
What is the Future of Data Management
The data management profession will definitely be impacted by current and future trends. Factors that are related to changing various
communications and computer technologies, the use of social media, and an organization's need to obtain and use quality information and data.
These factors will be manifested in the following:
an exponential growth in data (i.e., big data).
the mobile delivery of information (i.e., phone and tablet applications, etc.).
the quality of the data for required informational needs (i.e., real-time access anywhere).
various technology changes in mobile, storage, computing, and communications affecting data needs.
organizational and personal needs to access and use high-quality data for decision-making.
There are other factors that will influence the need for organizations to organize, structure, relate, monitor, assess, deliver, and dispose of data as
needed. Let's examine some areas now.
The computer industry evolution will require tools and techniques to manage data and it will drive a cultural transition as well. The business
culture will change since business executives and professionals will make demands for the management of data. The current environment is full
of redundant, low-quality, disparate data affecting the information required for decision-making. The cultural transformation that will occur is that
business professionals will team up with data management professionals to focus on high-quality, non-redundant, business decision-making
data. The transformation will focus on the discipline of data management.
The discipline of data management will continue to demand expertise. Various roles and responsibilities include: Chief Data Manager or
Architect, Data Architects, Data Modelers, Data Stewards, Database Architects, and various data technicians. Each of these roles demand a
particular set of skills that may include: mathematics (like set theory), statistics, linguistics, logic, philosophy, inductive and deductive reasoning,
inter-personnel communications, writing, presentation skills, and a solid foundation in business fundamentals.
6. Summary of Trends
The availability of data from so many difference sources drives today's organizations to constantly pursue the latest data from reliable and
accurate sources. The implications of having data at our fingertips at anytime and anywhere is our reality. Data is captured from many sources:
databases, files, blogs, email, images, satellite, cameras, video, and other related sources. Mobile technology is changing the landscape for most
businesses because the speed of the delivery of data to these devices makes fact-based informed decisions much more suspect. Why?
As the current century unfolds, business professionals and data management professionals will partner to organize, structure, relate, monitor,
assess, deliver, and dispose data as needed by organizations as a matter of survival. The partnering efforts will drive the data management
profession to support a business asset management approach.