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Chapter 1: : OVERVIEW OF DATA TECHNOLOGY, image, image, image, image -…
Chapter 1: : OVERVIEW OF DATA
TECHNOLOGY
Data Technology
Technology connected to martech and adtech
solution for data management and product or services based on data generated by human and machine
used to manage big data set
integrate data from various sources
discover new business or analytical insight from collected information
for build solution for data management
Evolution
Traditional DBMS
Relational DBMS
AI
Object-oriented &
Object-Relational Database
Digital technologies
NoSQL-Big Data
Inteliligent DBMS
Big Data
dedicated to
Analysis
of large collections of data that frequently
originate from disparate sources.
Processing
Storage
Processing large amounts of unstructured data from various sources to extract hidden insights efficiently and quickly.
Characteristic
Volume
Huge amount of data
Velocity
hig speed of accumulation data and continuos flow of data
Variety
nature of data
structured
semi-structured
heterogeneous sources
unstructured
Veracity
inconsistency and uncertainty data
variable because of the multitude of data dimensions
resulting from multiple disparate data types and sources
Value
need to converted for valuable to extract information
can lead to a wide range of insights and benefits
Operational optimization
Actionable intelligence
Identification of new markets
Accurate predictions
Fault and fraud detection
More detailed records
Improved decision-making
Scientific discoveries
Terminology
Datasets
Collections or groups of related data.
Tweets stored in a flat file
A collection of image files in a directory
An extract of rows from a database table stored in a CSV
formatted file
Historical weather observations that are stored as XML files
Data analysis
Process of examining data to find facts, relationships,
patterns, insights and/or trends
to support better decision making
Carrying out data analysis helps establish patterns and
relationships among the data being analyzed.
Data analytics
Data analytics involves handling data throughout its entire lifecycle, including collecting, cleaning, organizing, storing, analyzing, and governing it.
Descriptive
Carried out to answer questions about events that have already occurred.
This form of analytics contextualizes data to generate information.
Diagnostic
to determine cause phenomena from past using question that focus on the reason
goal to dtermine what information is relatedto phenomena
can result in data that suitable for performing drill down and roll-up analysis
Predictive
carry attempt to determine the outcome of event in future
enhanced with meaning to generate knowledge that conveys how that information is related.
have implicit dependencies on condition
Predictive analytics tools can provide user-friendly
front-end interfaces
Prescriptive
prescribing actions that should be taken after predictive analysis
provide result that can be reasoned about because they embed element of situational understanding
Type of data
Human generated data
machine-generated data
Structured data
Conforms to a data model or schema and is
often stored in tabular form.
Used to capture relationship between different entities and is therefore most stored in relational database
generated ERP and CRP system
Unstructured data
Data that does not conform to a data model or data
schema.
faster growth rate than
structured data.
textual or binary
image, audio, or video data
Semi-structured data
defined level of structure and consistency, but not relational in nature
hierarchical or graph based
XML and JSON files are common forms
more easily processed than unstructured data
