THE DIFFICULTIES OF DATA MANAGEMENT - DATA GOVERNANCE.
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The document discusses the difficulties of data management including increased data quantities from multiple sources over time in various formats, as well as data degradation, security and legal issues, and redundancy. It then defines data governance as policies, procedures and people needed to establish data consistency and defines characteristics of governed data including being consistent, correct, timely and complete. Finally, it discusses data governance strategies like master data and qualities of governed data.
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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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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
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- 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 Truthfulness Integrations