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Transcript of Database_Management.pdf
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Managing Data to Improve Business
Performance
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Learning Objectives
Recognize the importance of data, managerial issues and life cycle
Describe sources of data, collection, and quality
DBMS
Describe Data Warehousing and Analytical Processing
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Data
What is Data ?
Importance of Data ?
Data Quality ?
Result of Dirty Data ?
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Difficulties of Managing Data
Amount of data increases exponentially.
Data are scattered and collected by many individuals using various methods and devices.
Data comes from many sources including internal sources, personal sources and external sources.
Data security, quality and integrity are critical.
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Types of Data in Organizations
Transaction Records
Documents
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Document Management System (DMS)
Consists of hardware and software that manage and archive electronic documents and also convert paper documents into e-documents.
DMS, besides capturing and storing the documents takes care of indexing which facilitates searching of documents from the repository.
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Data(transaction records) Management
Conventional file system vs DBMS:
Disadvantages of Conventional file system
Redundancy Inconsistency Security issues etc.
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Database Management System (DBMS)
DBMS Storage of data and program tomanage it
Examples of DBMS software Oracle, MSSQLServer, DB2, MySQL, PostGRESQL
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Database
Collection of storage objects like tables.
EMPNO NAME SALARY DEPTNO
10 ARUN 30000 10
20 KIRAN 40000 20
TABLE
RECORD
FIELD
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DBMS Key Concepts
Data Models Hierarchical
Network
Relational
etc
Data Views
KeysPrimary, Foreign, Candidate, Alternate
Indexes
SQL(Structured Query Language)
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Data Life Cycle
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Transactional vs. Analytical Data Processing Transactional processing takes place in operational systems(TPS) that provide the organization with the capability toperform business transactions and produce transaction reports.The data are organized mainly in a hierarchical structure andare centrally processed. This is done primarily for fast andefficient processing of routine, repetitive data.
Analytical processing involves the analysis of accumulateddata. Analytical processing, sometimes referred to as businessintelligence, includes data mining, decision support systems(DSS), querying, and other analysis activities. These analysesplace strategic information in the hands of decision makers toenhance productivity and make better decisions, leading togreater competitive advantage.
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Data WarehousingData warehouse is a repository of historical dataorganized by subject to support decision makersin the organization and include:
Online analytical processing(OLAP) which involvesthe analysis of accumulated data by end users.
Multidimensional data structure which allows datato be represented in a three-dimensional matrix(or data cube).
Unlike the data tables in the database which aredesigned to optimize storage, the data tables in awarehouse are designed to respond to analysisquery.
Data warehousing entails an ETL process:
Extracting data from various sources
Transforming it to fit operational needs
Loading it into the end target (Data mart)
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BIG DATA
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Background :
For decades companies have beenmaking business decisions based ontransactional data stored in relationaldatabases.
In the recent years, companies haverealized that the non traditional, lessstructured data in the form ofweblogs, social media, email, sensorsis trove of potential treasure as thiscan be mined for useful insights.
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How big is big data ?
Misconception of big data :If it is data and it is big, it is big data
What is big today may not be big tomorrow
Big data has attributes that challenges constraints of a system or business needs.
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4 Vs of big data
Volume
Velocity
Variety
Value
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Volume
Machine generated data is produced in much larger quantities than non-traditional data.
For example, a single jet engine can generate 10 TB of data in 30 minutes
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Velocity
Social media data streams while notas massive as machine-generated dataproduce a large influx of opinions andrelationships valuable to customerrelationship management. Even at 140characters per tweet, the high velocity(or frequency) of Twitter data ensureslarge volumes.
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Variety
Traditional data formats tend to be relatively well described and change slowly. In contrast, non-traditional data formats exhibit a dazzling rate of change.
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Value
The economic value of different datavaries significantly. Typically there isgood information hidden amongst alarger body of non-traditional data;the challenge is identifying what isvaluable and then transforming andextracting that data for analysis.
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Why Big Data ?
When big data is distilled and analyzed in combination with traditional enterprise data, enterprises can develop a more thorough and insightful understanding of their business.
It leads to enhanced productivity, a stronger competitive position and greater innovation.
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Examples The proliferation of smart phones and other GPS devices offers
advertisers an opportunity to target consumers when they are in
close proximity to a store, a coffee shop or a restaurant. This opens
up new revenue for service providers and offers many businesses a
chance to target new customers.
Retailers usually know who buys their products. Use of social media
and web log files from their ecommerce sites can help them
understand who didnt buy and why they chose not to.. This can
enable much more effective micro customer segmentation and
targeted marketing campaigns, as well as improve supply chain
efficiencies.
Social media sites like Facebook and LinkedIn simply wouldnt exist
without big data. Their business model requires a personalized
experience on the web, which can only be delivered by capturing
and using all the available data about a user or member.