1. Metadata is organized data that describes and gives information about other data. There are three main types of metadata that corporations use: descriptive metadata for discovery and identification, structural metadata for describing how complex objects are composed, and administrative metadata for management of resources.
2. Security of metadata is important as it can contain sensitive customer information. Access control, mobile device security policies, secure storage environments, and limiting what data is considered metadata can help secure it.
3. For a service division, important metadata would include customer service histories, technician information, and logs of troubleshooting activities to integrate into a new system. Master data is critical business data like customer, supplier, and organizational information.
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.
The volume of information your company creates and stores digitally is growing every day. Every business system in your organization creates or uses some type of data, making the job of records management challenging. If you had to manage records based only on the contents of the record itself, it would be very difficult. Likewise, searching for information across the organization would also be challenging. This is where metadata plays an important role in records management.
A Study on Big Data Privacy Protection Models using Data Masking Methods IJECEIAES
In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications.
Information and Integration Management VisionColin Bell
The vision of the Information and Integration Management team at the University of Waterloo captured on a single 'poster' page. Covers: Data Management Environment, Mission + Vision, Information Asset Base, Information Lifecycle, Document Management, Metadata/Meaning, Integration Platform, and Innovation Platform.
Boosting Cybersecurity with Data Governance (peer reviewed)Guy Pearce
Data Governance has a significant role to play in information security, with special data classes beyond the regular four cyber classes (public, confidential, classified and restricted) being useful in helping the organization identify whether sensitive data was exposed in a breach.
Are you prepared for eu gdpr indirect identifiers? what are indirect identifi...Steven Meister
What is your solution for GDPR’s Indirect Identifiers? Many aren’t sure what they are and will probably be unsuccessful when attempting to become GDPR compliant. Allow me to explain.
As a software development manager, I must confess that the Discovery & Remediation of Indirect Identifiers was the most complex project I have managed in my 33 years in the industry.
First, let me explain what an Indirect Identifier is. According to the “Privacy Technical Assistance Center of the U.S. Department of Education, it means “Indirect identifiers include information that can be combined with other information to identify specific individuals, including, for example, a combination of gender, birth date, geographic indicator and other descriptors.”
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.
The volume of information your company creates and stores digitally is growing every day. Every business system in your organization creates or uses some type of data, making the job of records management challenging. If you had to manage records based only on the contents of the record itself, it would be very difficult. Likewise, searching for information across the organization would also be challenging. This is where metadata plays an important role in records management.
A Study on Big Data Privacy Protection Models using Data Masking Methods IJECEIAES
In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications.
Information and Integration Management VisionColin Bell
The vision of the Information and Integration Management team at the University of Waterloo captured on a single 'poster' page. Covers: Data Management Environment, Mission + Vision, Information Asset Base, Information Lifecycle, Document Management, Metadata/Meaning, Integration Platform, and Innovation Platform.
Boosting Cybersecurity with Data Governance (peer reviewed)Guy Pearce
Data Governance has a significant role to play in information security, with special data classes beyond the regular four cyber classes (public, confidential, classified and restricted) being useful in helping the organization identify whether sensitive data was exposed in a breach.
Are you prepared for eu gdpr indirect identifiers? what are indirect identifi...Steven Meister
What is your solution for GDPR’s Indirect Identifiers? Many aren’t sure what they are and will probably be unsuccessful when attempting to become GDPR compliant. Allow me to explain.
As a software development manager, I must confess that the Discovery & Remediation of Indirect Identifiers was the most complex project I have managed in my 33 years in the industry.
First, let me explain what an Indirect Identifier is. According to the “Privacy Technical Assistance Center of the U.S. Department of Education, it means “Indirect identifiers include information that can be combined with other information to identify specific individuals, including, for example, a combination of gender, birth date, geographic indicator and other descriptors.”
Organizations looking to benefit from the scalability, agility, and capital cost savings of cloud computing inevitably
encounter the issues of data privacy and security. In the corporate data center, data security and privacy are mostly
about protection from hackers and insiders. In the cloud, however—public, community, hybrid, and sometimes even
private-- they are also affected by where data resides and the impact of local, regional, and national regulations on
the privacy of that data--an issue known as data sovereignty.
Extending Information Security to Non-Production EnvironmentsLindaWatson19
This paper discusses the threats that non-production environments pose to database security and provides practical advice and multiple options for ensuring data assets remain secure against unauthorized access.
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESijcsit
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
A presentation for researcher, majorly scientists, on how to prepare proposal with well structured and documented data management plan. it presentation also covered key aspect of data management planning as well as the importance of data management planning. What are donors or funders looking for in a research proposal?
In this PPT, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
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.
Various cloud computing models are used to increase the profit of an organization. Cloud
provides a convenient environment and more advantages to business organizations to run their
business. But, it has some issues related to the privacy of data. User’s data are stored and
maintained out of user’s premises. The failure of data protection causes many issues like data
theft which affects the individual organization. The cloud users may be satisfied, if their data
are protected properly from unauthorized access. This paper presents a survey on different
privacy issues involved in the cloud service. It also provides some suggestions to the cloud users to select their suitable cloud services by knowing their privacy policies.
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
Running head MANAGEMENT INFORMATION SYSTEM1MANAGEMENT INFORM.docxcowinhelen
Running head: MANAGEMENT INFORMATION SYSTEM 1
MANAGEMENT INFORMATION SYSTEM 6
Management Information System
Vijay chilakala
Wilmington university
5/20/18
Introduction
A management information system can be described as a computerized system that accepts data and organizes data in a systematic way so that the data can be used for other purposes like analysis, decision making and also problem solving. Most of the business organizations use these kinds of system to store and organize their data for decision making purposes. Management information systems are applied in various departments like banking institutions, military use, hospitals, meteorological institutions, National Space Aeronautics (NASA) and many more. An example of Management information system (MIS) is Automated Teller Machines (ATM) used for cash transactions (Li, Xie, & Zhang, 2015)Risks associated with Management Information System
Management of information systems in relation to risk is a wide area to g by. There are a few important security concepts that can help in the management of these risks. Therefore security for data and information is important in these sectors. Information security entails a number of factors; integrity refers to when the management of data and information is dealt with by transparent and authorized ways. Confidentiality refers to limiting the data and information so that the authorized parties get to view it while writing off the unauthorized parties (Batini & Scannapieco, 2016). Availability of data and information is also information and therefore data and information should be available for the right people at a given time. There are a number of risks or vulnerabilities in the information systems department.
Impersonation is an example of a risk. You find that one user takes the identity of another person to accomplish certain hidden agendas. Cybercrimes like hacking or cracking to gain access into the system. Theft is also a risk which usually results into loss of computer hardware (Li, Xie, & Zhang, 2015)Management of Information systems related to use
Information system has a number of uses. They play an important role in managing data and information. A management information system can be used in processing transactions like the Automated Teller Machines. This function is mostly used by financing institutions like Banks to store, organize and process data. Management information systems can be used in libraries to organize and keep record of books as they are issued to the parties that need them. They are also used by organizations to compile data used for analysis; the data can later be used for decision making policies.Management of Information systems related to data storage
Data storage provides a platform where information that is used by any information system can be kept and backed up for future usage. It also means that the data stored can be accessed at any given whenever the authorized parties feel like using them. ...
Organizations looking to benefit from the scalability, agility, and capital cost savings of cloud computing inevitably
encounter the issues of data privacy and security. In the corporate data center, data security and privacy are mostly
about protection from hackers and insiders. In the cloud, however—public, community, hybrid, and sometimes even
private-- they are also affected by where data resides and the impact of local, regional, and national regulations on
the privacy of that data--an issue known as data sovereignty.
Extending Information Security to Non-Production EnvironmentsLindaWatson19
This paper discusses the threats that non-production environments pose to database security and provides practical advice and multiple options for ensuring data assets remain secure against unauthorized access.
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESijcsit
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
A presentation for researcher, majorly scientists, on how to prepare proposal with well structured and documented data management plan. it presentation also covered key aspect of data management planning as well as the importance of data management planning. What are donors or funders looking for in a research proposal?
In this PPT, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
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.
Various cloud computing models are used to increase the profit of an organization. Cloud
provides a convenient environment and more advantages to business organizations to run their
business. But, it has some issues related to the privacy of data. User’s data are stored and
maintained out of user’s premises. The failure of data protection causes many issues like data
theft which affects the individual organization. The cloud users may be satisfied, if their data
are protected properly from unauthorized access. This paper presents a survey on different
privacy issues involved in the cloud service. It also provides some suggestions to the cloud users to select their suitable cloud services by knowing their privacy policies.
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
Running head MANAGEMENT INFORMATION SYSTEM1MANAGEMENT INFORM.docxcowinhelen
Running head: MANAGEMENT INFORMATION SYSTEM 1
MANAGEMENT INFORMATION SYSTEM 6
Management Information System
Vijay chilakala
Wilmington university
5/20/18
Introduction
A management information system can be described as a computerized system that accepts data and organizes data in a systematic way so that the data can be used for other purposes like analysis, decision making and also problem solving. Most of the business organizations use these kinds of system to store and organize their data for decision making purposes. Management information systems are applied in various departments like banking institutions, military use, hospitals, meteorological institutions, National Space Aeronautics (NASA) and many more. An example of Management information system (MIS) is Automated Teller Machines (ATM) used for cash transactions (Li, Xie, & Zhang, 2015)Risks associated with Management Information System
Management of information systems in relation to risk is a wide area to g by. There are a few important security concepts that can help in the management of these risks. Therefore security for data and information is important in these sectors. Information security entails a number of factors; integrity refers to when the management of data and information is dealt with by transparent and authorized ways. Confidentiality refers to limiting the data and information so that the authorized parties get to view it while writing off the unauthorized parties (Batini & Scannapieco, 2016). Availability of data and information is also information and therefore data and information should be available for the right people at a given time. There are a number of risks or vulnerabilities in the information systems department.
Impersonation is an example of a risk. You find that one user takes the identity of another person to accomplish certain hidden agendas. Cybercrimes like hacking or cracking to gain access into the system. Theft is also a risk which usually results into loss of computer hardware (Li, Xie, & Zhang, 2015)Management of Information systems related to use
Information system has a number of uses. They play an important role in managing data and information. A management information system can be used in processing transactions like the Automated Teller Machines. This function is mostly used by financing institutions like Banks to store, organize and process data. Management information systems can be used in libraries to organize and keep record of books as they are issued to the parties that need them. They are also used by organizations to compile data used for analysis; the data can later be used for decision making policies.Management of Information systems related to data storage
Data storage provides a platform where information that is used by any information system can be kept and backed up for future usage. It also means that the data stored can be accessed at any given whenever the authorized parties feel like using them. ...
Week 4 Lecture 1 - Databases and Data WarehousesManagement of .docxjessiehampson
Week 4 Lecture 1 - Databases and Data Warehouses
Management of Information Systems
Databases and Data Warehouses
The impact of database technology on how business is conducted today cannot be overemphasized. This technology has enabled an information industry with comprehensive influences on businesses and individuals. Databases store data that populate web pages and other interactive networked technologies. Search engines, e-commerce, and social media would not exist without databases. With database support, larger tasks can be accomplished by fewer people.
Effective data management is the principal benefit of IT. Database management systems (DBMSs) enable the fast creation of databases and manipulation of data on an aggregate basis or down to the smallest detail for business purposes. Databases support most web pages and other interactive networked technology. DBMSs support target marketing, financial management, decision-making, distribution of goods and services, customer service, and other activities. It is imperative, in the age of data mining, and “big data,” for knowledge workers to understand how databases work and how data are used operationally and strategically in business management.
Database analysis and management skills are mandatory in the marketplace. IT professionals develop and implement databases. However, data is essential to the non-technical professional who uses the data for decision making regarding accounting, marketing, logistics, senior management, and other functional areas.
The relational database model is common. However, data can be organized in other ways. “Big Data” prompted the use of other database models. “NoSQL” database models are non-relational and do not require SQL to retrieve data. NoSQL databases can be structured by object, document, key-value, graph, column, and other possibilities
In relational databases, a primary key is a field in a table that contains a unique value used to differentiate between rows of data. The primary key is usually a number, or a computer generated globally unique identifier (GUID). Sometimes a composite key is used differentiate between table rows. A composite key is a combination of the values in two or more fields in a table that when combined are unique in the table and serve as a primary key. A foreign key is used to link data between two tables. A foreign key in a table is the primary key of a related table.
Databases contain different types of fields. Some types are, number, text, image, video, audio, geographical coordinates, and others. If a number is not used for mathematical calculations, it is best to assign a text type to it in a database to avoid the need to convert it from a number to a string after retrieval.
SQL is a popular query language used to retrieve data from relational databases. SQL can be used to retrieve data from more than one table by use of a “join.” A join query retrieves data from rows in two or more tables, where the value of the foreign ...
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.
Color Blind 1.pdfColor Blind 2.pdfColor Blind 3.pdfC.docxdrandy1
Color Blind 1.pdfColor Blind 2.pdfColor Blind 3.pdfColor Blind 4.pdf
100
C h a p t e r
8 A Management Framework for IT Sourcing1
1 This chapter is based on the authors’ previously published article, McKeen, J. D., and H. A. Smith.
“Delivering IT Functions: A Decision Framework.” Communications of the Association for Information Systems 19,
no. 35 (June 2007): 725–39. Reproduced by permission of the Association for Information Systems.
Every five years starting in 1995, the focus group has taken stock of the responsibilities for which IT is held accountable (Smith and McKeen 2006; Smith and McKeen 2012). To no one’s surprise, the list of IT responsibilities has grown
dramatically. To the standard list of “operations management,” “systems development,”
and “network management” have now been added responsibilities such as business
transformation, regulatory compliance, enterprise and security architecture manage-
ment, information and content management, mobile and social computing, business
intelligence and analytics, risk management, innovation, demand management, and
business continuity management (Smith and McKeen 2012). Never before has IT man-
agement been challenged to assume such diversity of responsibility and to deliver on
so many different fronts. As a result, IT managers have begun to critically examine how
they source and deliver their various services to the organization.
In the past, organizations met additional demands for IT functionality by simply
adding more staff. Today, increasing permanent IT staff is less viable than in the past
and this has led IT organizations to explore other options. Fortunately, several sourcing
alternatives are at hand for delivering IT functionality. Software can be purchased or
rented from the cloud, customized systems can be developed by third parties, whole
business processes can be outsourced, technical expertise can be contracted, data center
facilities can be managed, networking solutions (e.g., data, voice) are obtainable, data
storage is available on demand, and companies will manage your desktop environment
as well as all of your support/maintenance functions. Faced with this smorgasbord of
sourcing options, organizations are experimenting as never before. As with other forms
of experimentation, however, there have been failures as well as successes, and most
decisions have been made on a “one-off” basis. What is still lacking is a unified decision
framework to guide IT managers through this maze of sourcing options.
Chapter 8 • A Management Framework for IT Sourcing 101
This chapter explores how organizations are choosing to source and deliver IT
“functions.” The first section defines what we mean by an IT function and proposes a
maturity model for IT functions. Following this, we take a conceptual look at IT sourc-
ing options, and then we analyze actual company experiences with four different IT
sourcing options—(1) in-house, (2) insource, (3) .
Color Blind 1.pdfColor Blind 2.pdfColor Blind 3.pdfC.docxcargillfilberto
Color Blind 1.pdfColor Blind 2.pdfColor Blind 3.pdfColor Blind 4.pdf
100
C h a p t e r
8 A Management Framework for IT Sourcing1
1 This chapter is based on the authors’ previously published article, McKeen, J. D., and H. A. Smith.
“Delivering IT Functions: A Decision Framework.” Communications of the Association for Information Systems 19,
no. 35 (June 2007): 725–39. Reproduced by permission of the Association for Information Systems.
Every five years starting in 1995, the focus group has taken stock of the responsibilities for which IT is held accountable (Smith and McKeen 2006; Smith and McKeen 2012). To no one’s surprise, the list of IT responsibilities has grown
dramatically. To the standard list of “operations management,” “systems development,”
and “network management” have now been added responsibilities such as business
transformation, regulatory compliance, enterprise and security architecture manage-
ment, information and content management, mobile and social computing, business
intelligence and analytics, risk management, innovation, demand management, and
business continuity management (Smith and McKeen 2012). Never before has IT man-
agement been challenged to assume such diversity of responsibility and to deliver on
so many different fronts. As a result, IT managers have begun to critically examine how
they source and deliver their various services to the organization.
In the past, organizations met additional demands for IT functionality by simply
adding more staff. Today, increasing permanent IT staff is less viable than in the past
and this has led IT organizations to explore other options. Fortunately, several sourcing
alternatives are at hand for delivering IT functionality. Software can be purchased or
rented from the cloud, customized systems can be developed by third parties, whole
business processes can be outsourced, technical expertise can be contracted, data center
facilities can be managed, networking solutions (e.g., data, voice) are obtainable, data
storage is available on demand, and companies will manage your desktop environment
as well as all of your support/maintenance functions. Faced with this smorgasbord of
sourcing options, organizations are experimenting as never before. As with other forms
of experimentation, however, there have been failures as well as successes, and most
decisions have been made on a “one-off” basis. What is still lacking is a unified decision
framework to guide IT managers through this maze of sourcing options.
Chapter 8 • A Management Framework for IT Sourcing 101
This chapter explores how organizations are choosing to source and deliver IT
“functions.” The first section defines what we mean by an IT function and proposes a
maturity model for IT functions. Following this, we take a conceptual look at IT sourc-
ing options, and then we analyze actual company experiences with four different IT
sourcing options—(1) in-house, (2) insource, (3) .
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
leewayhertz.com-AI in Master Data Management MDM Pioneering next-generation d...KristiLBurns
Master data refers to the critical, core data within an enterprise that is essential for conducting business operations and making informed decisions. This data encompasses vital information about the primary entities around which business transactions revolve and generally changes infrequently. Master data is not transactional but rather plays a key role in defining and guiding transactions.
NFRASTRUCTURE MODERNIZATION REVIEW
Analyze the issues
Hardware
Over-running volume of data is a problem that should be addressed by data management and storage management. Data is being constantly collected but poorly analyzed which leads to excessive amounts of data occupying storage and delay in operations which inevitably affect production, sales and profits. If this remains unresolved, current data may have to be moved to external storage and recovered if needed. There is also the risk of data not being encoded into computers and thus will remain in manual state. This can be a case of redundant or extraneous data that is not yet cleaned and normalized by operations managers with the guidance of IT. This situation is known as data overload where companies actually use only a fraction of the data they capture and store. Many companies simply hoard data to make sure that they are readily available when they are needed. This negatively impacts the Corporation when assessing data relevance, accuracies and timeliness (Marr, 2016).
Software
The Largo Corporation (LC) seems to running on an enterprise resource planning system that is probably as long as 20 years old. Initially, LC has had success with the old system because they were able to establish themselves in various industries such as healthcare, media, government, etc. But due to various concerns, the Corporation is currently running on an outdated system because it is unable to provide services that keeps the Corporation a float. The LC is losing revenue and customers. Complete data without analysis is invaluable because, no information and insights can be produced that will support decisions. Customer data should lead to the best marketing and sales campaigns. The Corporation needs to recognize its weaknesses and implement changes to their software by incorporating funding for a new system that is reliable, secure, and has the ability to run on integrated systems; all of which will streamline data organization and analysis for the enterprise. (Rouse, n.d).
Network/Telecommunications
The network that was built in the 1980’s has become slow and unreliable affecting business operations. The problems caused by the old network are; lack of integration and communication between departments affecting the work flow, supply vs. demand, and inability to analyze data to carry out these operations. The Corporation should have taken into consideration the growth of the company by expanding and upgrading their networks along with their services. They should also take into consideration the number of departments, the number of users and their skill level, storage and bandwidth, and budget (Rasmussen, 2011). The current network does not allow employees to connect on their mobile devices which restricts flexibility and places limitations on productivity and portability.
Management
The responses of both IT and the business group are both juxtaposed against e ...
Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
https://www.dasca.org/
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
ANALYSIS ON IDENTITY MANAGEMENT SYSTEMS WITH EXTENDED STATE-OF-THE-ART IDM TA...ijasuc
Every person has his/her own identity. It’s important to manage a digital identity in a computer network,
with high priority. In spite of different applications we use in organization, resources need to be managed
and allotted to the appropriate user with proper access rights. Identity management or IdM refers to how
humans are identified, authorized and managed across computer networks. It covers issues such as how
users are given an identity, the protection of that identity and the technologies supporting that protection.
This paper attempts to provide an analysis to various identity management systems based on the state-ofthe-art identity taxonomy factors.
DOCUMENT SELECTION USING MAPREDUCE Yenumula B Reddy and Desmond HillClaraZara1
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The research discusses the MapReduce issues, framework for MapReduce programming model and implementation. The paper includes the analysis of Big Data using MapReduce techniques and identifying a required document from a stream of documents. Identifying a required document is part of the security in a stream of documents in the cyber world. The document may be significant in business, medical, social, or terrorism.
Similar to Company Metadata and Master Data Management Unit 9 Assigment 1 Jessica Graf (20)
DOCUMENT SELECTION USING MAPREDUCE Yenumula B Reddy and Desmond Hill
Company Metadata and Master Data Management Unit 9 Assigment 1 Jessica Graf
1. Company Metadata and MasterData Management Page 1
Company Metadata and Master Data Management
ISTM5020 - Lead Global IT Enterprise Sys
Jessica Graf
Capella University
Dr. Vincent Tran
December 14, 2014
2. Company Metadata and MasterData Management Page 2
Metadata is organized data that defines, clarifies, pinpoints, or otherwise makes it easier
to recover, use, or handle an information source. Metadata is frequently termed data about data
or information about information. The expression metadata is used otherwise in diverse
communities. Some use it to denote to machine comprehensible information, while others use it
only for archives that define electronic resources. In the library environment, metadata is
commonly used for any formal scheme of resource description, applying to any type of object,
digital or non-digital. (Godinez M., 2010)
There are three main types of metadata that corporations use. The first is called
Descriptive metadata. This type of metadata is a source for processes such as discovery and
identification. It can include elements such as title, abstract, author, and keywords. Another type
of metadata is Structural metadata and designates how complex objects are composed, for
example, how pages are ordered to form chapters. Another important type of metadata is
Administrative Metadata. Administrative metadata delivers information to aid management of a
resource, such as when and how it was produced, file category and other technical information,
and who can open it. There are several subsets of administrative data. There is two that are
important, the first one is Rights management metadata, which used with intellectual property
rights and then there is Preservation metadata, which encompasses information needed to
document and preserve a source. Metadata can describe resources at any level of aggregation. It
can describe a collection, a single resource, or a component part of a larger resource. (Godinez
M., 2010)
Security issues around metadata are something that a corporation should take seriously.
Metadata can contain anything from DNS information about an environment to customers’
3. Company Metadata and MasterData Management Page 3
ordering history and payment method. While this metadata is useful to company, it is a gold
mine to the hacker as they can use tools such as Exit to find to extract this useful information.
Access control is the first security issue that you would need to address. By keeping who
can access metadata to a very granular level, the safer your metadata will be. The second security
concern revolves around the use of mobile devices. As workforces become more mobile, there
are more security risks such as lost or stolen devices, unsecure connection to the metadata and
shoulder surfing for passwords and other information. While these are concerns, security
policies that surround metadata can be a security issue as well. If the policies are not flexible
enough to allow the users to do their jobs, they will find ways around the policies and that can
lead to your metadata being even more vulnerable. What you keep as metadata and where you
keep it is also a security risk. If you allow users to keep metadata locally on their computers or is
your metadata kept in a secure environment. A secure environment is not only technically secure,
but physically secure. (Yee, 2004)
When it comes to Dell’s Service division, the metadata that would have to be integrated
into a new system would include customer technical issue history and dispatch history. This
would cover service request numbers, part numbers and information about what they have
purchased from Dell. It would also have to include information about technicians that have
worked on their environment, TAMs and Sales personal that have worked with a customer.
Another important piece of metadata would be the logs from any troubleshooting done and
solutions to any technical issues. This needs to be kept not only for internal use, but for legal
reasons as well. Any metadata that pertains to the customer interaction with the service division
would need to be integrated into a new system.
4. Company Metadata and MasterData Management Page 4
Master Data is defined as data that is critical for your business. It can also be defined as
data that is the dependable and undeviating set of identifiers and protracted attributes that
designates the fundamental entities of the enterprise including customers, prospects, citizens,
suppliers, sites, hierarchies and chart of accounts.
Master Data for the Service division would definitely include customer information,
however it would also include organizational data such as organizational charts, team charts and
workflows for escalations. These are important as the teams within the service division need to
have cross functionality in order to troubleshoot and build data centers and complex issues.
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References
Chen, M., Jin, H., Wen, Y., & Leung, V. (2013, August). Enabling technologies for future data
center networking: a primer. Network, IEEE, pp. 8-15.
Conroy, P. (2012). CPG and the Cloud: Building Better Consumer Relationships for Less. CIO
Journal.
Godinez M., H. E. (2010). The Art of Enterprise Information Architecture. Crawfordsville, IN:
IBM Press.
Sahar, F. (2013). Tradeoffs between Usability and Security. International Journal of
Engineering and Technology, 434.
Yee, K.-P. (2004). Aligning security and usability. Security & Privacy, IEEE, 2(5), 48-55.
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