The difficulties of data management & Data governance.
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This document discusses the difficulties of data management, including increased data quantities from multiple incompatible sources, data degradation over time, security and legal issues across countries, and data redundancy. It then defines data governance as policies, procedures and people needed to establish data consistency. Key attributes for efficient data use are outlined as consistent, correct, timely, complete and protected data. Data governance strategies like master data are discussed as a way to store, maintain, exchange and secure consistent, precise and timely data across systems and clouds.
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Data Ownership:
Most companies and organizations have this notion that data governance should be taken care of ,
by the Information Technology department, because IT owns the system which stores the data.
The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data.
The people answerable to these kinds of data are generally the ones involved in defining business rules,
data cleaning and consolidation.?
Data Stewardship:?
Data stewards should be favorably those people who are familiar with the data. It is often seen that
there is need to deploy several people, to handle and correct data,
whereas a single data steward could have done the same job. Since the data being handled involves
organizational level data, it is important that there are governance rules for this process.?
If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing.
So it is important to take care of the amount of clean data sent to the stewards,
since we are not aware of which rules might trigger what amount of data.?
Choice of data stewards is again a difficult selection.
Data Security:?
Although the master data is data on organization level, but there is some confidentiality level linked to it.?
Not every employee has the authorization to view its aspects.
Security rules can be applied to the data.
The various departments in the organization must set different rules to the data they own.
They need to grant permissions to these rules , so that the user can view the data.
A large company can have data sourced out of many regions.
It is to be ensured that they are responsible to correct only their own data.?
Data survivorship:
There are some guidelines which are set up by data governance.
These rules can often change over hthe time according to new data sources being added.
The changes made to the data , are communicated to the organization so that data stewards and users can understand the process.
So from a data steward's point of view, it is important to apply security rules to the people who are involved
in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.?
?
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Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
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Data governance isn't about data. It's about relationships. Who needs information and who has information? How does decision making relate to information, the systems that manage that information, and processes that create information? My wife doesn't spend her time thinking about databases, analytics, enterprise KPIs, and business performance, but she schools me whenever we talk about the daily challenges that confront me during data governance initiatives. These are some lessons from my marriage that are critical to effective data governance.
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This was presented on October 24, 2018 at the ASUG EIM Conference. One of the many challenges presented by an acquisition and divestiture event is unifying disparate data and integrating systems together. If you are leading an integration, you may have more questions than answers on how to approach this event. Learn how to best leverage the momentum and budgets that accompany these activities to jump start good governance practices up front, as well as how to measure the return on investment, ensuring data and EIM professionals' ongoing success.
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A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
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This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
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Similar to The difficulties of data management & Data governance.
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Master Data Management
Data Ownership:
Most companies and organizations have this notion that data governance should be taken care of ,
by the Information Technology department, because IT owns the system which stores the data.
The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data.
The people answerable to these kinds of data are generally the ones involved in defining business rules,
data cleaning and consolidation.?
Data Stewardship:?
Data stewards should be favorably those people who are familiar with the data. It is often seen that
there is need to deploy several people, to handle and correct data,
whereas a single data steward could have done the same job. Since the data being handled involves
organizational level data, it is important that there are governance rules for this process.?
If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing.
So it is important to take care of the amount of clean data sent to the stewards,
since we are not aware of which rules might trigger what amount of data.?
Choice of data stewards is again a difficult selection.
Data Security:?
Although the master data is data on organization level, but there is some confidentiality level linked to it.?
Not every employee has the authorization to view its aspects.
Security rules can be applied to the data.
The various departments in the organization must set different rules to the data they own.
They need to grant permissions to these rules , so that the user can view the data.
A large company can have data sourced out of many regions.
It is to be ensured that they are responsible to correct only their own data.?
Data survivorship:
There are some guidelines which are set up by data governance.
These rules can often change over hthe time according to new data sources being added.
The changes made to the data , are communicated to the organization so that data stewards and users can understand the process.
So from a data steward's point of view, it is important to apply security rules to the people who are involved
in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.?
?
Boosting Cybersecurity with Data Governance (peer reviewed)
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BD1.pptx
Big data is massive, complex datasets including huge quantities of data from sources like social media. Big data analytics examines large amounts of heterogeneous digital data to glean insights. It involves five characteristics: volume, variety, velocity, value, and veracity. The types of big data are structured, unstructured, and semi-structured. Data repositories like data warehouses and data lakes store organizational data to facilitate decision-making and analytics.
ACCOUNTING-IT-APP-MIdterm Topic-Bigdata.pdf
The document discusses big data, including its characteristics, sources, uses, and challenges. It defines big data as extremely large and complex data that cannot be managed with traditional software. Key points include: big data is characterized by volume, velocity, and variety; sources include business applications and social media; uses include marketing, healthcare, and manufacturing; and challenges include dealing with the volume, variety, velocity, data quality issues, and cost of infrastructure. It also covers data management concepts like data governance, data stewards, the data lifecycle, data warehouses, ETL processes, data marts, data lakes, NoSQL databases, Hadoop, HDFS, and MapReduce programs.
big data.pptx
Big data refers to large, complex datasets that are difficult to process using traditional methods. It is growing exponentially from sources like the internet, sensors, and social media. Big data has characteristics like volume, velocity, variety, and veracity. While it enables better decision making and customer insights, big data also poses challenges around privacy, security, complexity, and cost. Effective use of big data requires investment in tools, skills, and governance strategies.
Group 2 Handling and Processing of big data (1).pptx
This document outlines techniques for handling and processing big data. It begins by defining big data and explaining why it is important to handle large volumes of diverse data generated at high speeds. Some key points covered include:
- The importance of clearly defining goals and strategies before collecting big data to ensure only relevant data is gathered.
- Techniques for handling big data including securing data, keeping backups, linking data between systems, and adapting to new technologies.
- The need to preprocess big data by cleaning, integrating, reducing and discretizing data to improve quality before analysis.
- Examples of big data analysis techniques including market basket analysis and examples using tools like Pandas, Scikit-Learn, R and Apache OpenNLP
IT6701 Information Management - Unit III
The document discusses Master Data Management (MDM). It defines MDM as a framework for creating and maintaining authoritative, reliable, accurate and secure master data across an enterprise. The key points covered are:
- MDM is needed to resolve data uncertainty and have a single version of truth. It identifies master data items and manages them.
- MDM implementation involves identifying master data sources, appointing data stewards, developing a data model, choosing tools, and designing infrastructure to generate and test master data.
- MDM provides benefits like a single version of truth, increased consistency, data governance and facilitates multiple domains and data analysis across departments.
Data Warehousing.pdf
Real-Time Data Access: While historical data analysis is essential, real-time data access is equally important in today's fast-paced business environment. Data warehousing systems often incorporate real-time data integration capabilities, providing decision-makers with up-to-the-minute information to respond promptly to changing conditions.
Why data governance is the new buzz?
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data Mining
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
Group 2 Handling and Processing of big data.pptx
The document discusses handling and processing big data. It begins by defining big data and explaining why it is important for companies to analyze big data. It then discusses several techniques for handling big data, including establishing goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. The document also covers preprocessing big data by cleaning, integrating, reducing, and discretizing data. It provides a case study of preprocessing government agency data and discusses advanced tools and techniques for working with big data.
Handling and Processing Big Data
This document provides an overview of handling and processing big data. It begins with defining big data and its key characteristics of volume, velocity, and variety. It then discusses several ways to effectively handle big data, such as outlining goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. Metadata is also important for big data handling and processing. The document outlines the different types of metadata and closes by discussing technologies commonly used for big data processing like Hadoop, MapReduce, and Hive.
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IT for Management On-Demand Strategies for Performance, Growth,.docx
IT for Management: On-Demand Strategies for Performance, Growth, and Sustainability
Eleventh Edition
Turban, Pollard, Wood
Chapter 3
Data Management, Business Intelligence, and Data Analytics
Learning Objectives (1 of 5)
2
Copyright ©2018 John Wiley & Sons, Inc.
Database Technologies: Databases
Collections of data sets or records stored in a systematic way
Stores data generated by business apps, sensors, operations, and transaction-processing systems (TPS)
The data in databases are extremely volatile
Medium and large enterprises typically have many databases of various types
Volatile data changes frequently.
3
Copyright ©2018 John Wiley & Sons, Inc.
Database Technologies: Data Warehouses
Integrate data from multiple databases and data silos, and organize them for complex analysis, knowledge discovery, and to support decision making
May require formatting processing and/or standardization
Loaded at specific times making them non-volatile and ready for analysis
4
Copyright ©2018 John Wiley & Sons, Inc.
Database Technologies: Data Marts
Small-scale data warehouses that support a single function or one department
Enterprises that cannot afford to invest in data warehousing may start with one or more data marts
5
Copyright ©2018 John Wiley & Sons, Inc.
Database Technologies: BI
Business Intelligence (BI)
Tools and techniques that process data and conduct statistical analysis for insight and discovery
Used to discover meaningful relationships in the data, keep informed of real time, detect trends, and identify opportunities and risks
6
Copyright ©2018 John Wiley & Sons, Inc.
Database Management Systems (DBMS)
Integrate with data collection systems such as TPS and business applications
Organized way to store, access, and manage data
Stores data in tables consisting of columns and rows, similar to the format of a spreadsheet
Standard database model adopted by most enterprises
Functions include:
Data filtering and profiling
Data integrity and maintenance
Data synchronization
Data security
Data access
7
Copyright ©2018 John Wiley & Sons, Inc.
Database Technologies: SQL
Relational Database Management Systems (DBMS)
Provides access to data using a declarative language
Declarative language
Simplifies data access by requiring that users only specify what data they want to access without defining how they will be achieved
Structured Query Language (SQL) is an example of declarative language:
SELECT column_name(s)
FROM table_name
WHERE condition
8
Copyright ©2018 John Wiley & Sons, Inc.
OLTP and OLAP Systems
Online Transaction Processing and Online Analytics Processing
Online Transaction Processing (OLTP)
Designed to manage transaction data, which are volatile & break down complex information into simpler data tarbles and strike a balance between transaction-processing efficiency and query efficiency
Cannot be optimized for data mining
Online Analytics Processing (OLAP)
A means of organizing large business databases
...
Bad customer data?
The document discusses the challenges clients face with bad customer data, including inconsistent data between systems, lack of data standards and ownership, difficulty retrieving archived data, and high costs of data issues. It provides examples of data quality problems that have cost companies millions or billions of dollars. The document advocates implementing data management and architecture practices to address these challenges and ensure accurate, consistent and secure customer data.
Data lifecycle management services
5Data Inc provides essential data discovery, classification, and protection at all six phases of data lifecycle management. Learn more.
Data
Descripcion de los datos dentro de los sistemas de informacion y las estruccturas de almacenamientos warehouse y Data mart.
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The document discusses some disadvantages of Minitrex's current data management system and proposes solutions based on customer relationship management (CRM) theories. It finds that Minitrex's data is siloed across different departments, leading to issues like duplicate customer records and a lack of a holistic view of customers. It suggests integrating CRM across departments to get a unified view of customers. It also recommends utilizing CRM software to consolidate data to improve data quality, gain insights, and better manage customer relationships. Leadership support and an integrated, holistic approach are identified as important for effective use of CRM.
Data Governance_Notes.pptx
The document discusses the activities involved in establishing an effective data governance program, including defining data governance for the organization, performing readiness assessments, developing goals and policies, underwriting data management projects, and engaging change management. The goal of data governance is to manage data as a valuable asset and guide data management activities according to policies and best practices. Setting up an appropriate operating framework, developing a governance strategy, and establishing organizational touchpoints are important for implementing a sustainable data governance program.
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The difficulties of data management & Data governance.
- 1. Databases, data warehouses & data marts. The difficulties of data management. Data governance.
- 2. the difficulties of data management. 1. The data quantity increase grow with the time. 2. The data are dispersed in all organization and in the web.
- 3. the difficulties of data management. 3. The data are produce from multiples sources. Internal, external or personal sources. 4. The data degrade and change very much.
- 4. the difficulties of data management. 6. The security and the legal requrements of the different countries. 5. The data format of the different sources are incompatible with the data warehouse and data mart format.
- 5. the difficulties of data management. 7. Redundancy and outdate data. Redundancy
- 6. data governance. The Business Intelligence Institute (BI Institute), defines it as a series of policies, procedures and people needed to establish a framework of data consistency within organizations Data management Data quality managemen. Data Governance,
- 7. DATA GOVERNANCE The data must have the following attributes to guarantee an efficient use: Data ConsistentUnique Correcte & Unique Timely Complete Protect
- 8. data governance - MASTER DATA. One of the Data Governance Strategies is the Master Data. Supply the ability to: - Store. - Keep. - Exchange. - Put into the cloud. Include: - Process. - Organizational Apps. With the aim of achieving: - Consistency. - Precision. - Timely.
- 9. data governance-master data. Transactional data. Master data Describe the activities or business transactions. Master data involves multiple transactions. They are used for: Categorize Add Evaluate Transactional Data
- 10. data governance - qualities Data Qualities Integrity Quality Quality Integrations