data analytics.pptx

WHAT IS DATA ?
Data is a distinct piece of information
that is gathered and translated for
some purpose. Data can be available in
terms of different forms, such as bits
and bytes stored in electronic memory,
numbers or text on pieces of paper, or
facts stored in a person's mind..

STRUCTURED DATA V/S UNSTRUCTURED DATA
STRUCTURED DATA
Structured data means that the data
follows the model or schema that
defines how the data is represented or
organized, meaning it fits well with the
traditional Relational Database System
(RDBMS).
We can find structured data in Tabular
Form.
Ex: A spreadsheet where data occupies
a specific cell and can be explicitly
defined and referenced.
UNSTRUCTURED DATA
Unstructured data lacks a logical schema for
understanding and decoding the data
through traditional programming means. As
a general rule, any data that does not fit
neatly into a predefined data model is
classified as unstructured data.
In unstructured data datasets are isn’t stored
in any structured format.
Ex: Text, Speech, Video, Images etc.

WHAT IS DATA ANALYTICS ?
Data analytics is the process
of examining data sets in
order to find trends and draw
conclusions about the
information they contain.

INTRODUCTION TO DATA ANALYTICS FOR IOT
 In the world of IOT, the creations of
massive amounts of data from sensor is
common and one of the biggest
challenges-not only from the transport
perspective but also from the data
management standpoint.
 Modern jet engines are fitted with
thousand of sensor that generate of
whopping of 10GB of data per second.
 Analyzing this amount of data in the

categorized and thus analyzed in
different ways.
 Depending on how data is
categorized, various data analytics
tools and processing methods can
be applied.
 Two important categorization
from an IOT perspective are
whether the data is structured or

IOT DATA ANALYTICS OVERVIEW
oThe true importance of IOT data from
smart object is realized only when the
analysis of data leads to actionable
business intelligence and insights.
oData Analytics are typically
broken down by the types of
results that are produced

TYPES OF DATA ANALYSIS RESULTS
descriptive data analysis tells you what is
happening either now or in the past. for example ,a
thermometer in a truck engine reports temperature
values every second. From a descriptive analysis
perspective , you can full this data at any moment to
gain insight into current operating condition of the
truck engine. If the temperature value is too high, then
there may be a cooling problem or the engine may be
experiencing too much load.
Diagnostic :
when you are interested in the “why ,”
diagnostic data analysis can provide the answer .
continuing with the example of the temperature sensor
in truck engine ,you might wonder why the truck engine
failed diagnostic analysis might show at the temperature

the historical value of a temperature for the truck engine,
predictive analysis could provide an estimate on the
remaining life of certain components in the engine. These
components could then be proactively replaced before failures
failures occurs. Or perhaps if temperature values of the truck
truck engine start to rise slowly overtime, this could indicate
indicate the need for an oil change or some other sort of engine
engine cooling maintenance.
 Prescriptive:
prescriptive analysis goes a step beyond
predictive and recommends solutions for upcoming problems. A
problems. A prescriptive analysis of the temperature data from
from truck engine might calcite various alternatives to cost-
cost-effectively maintain our trucks. These calculations could
could range from the cost necessary for more frequent oil
changes and cooling maintenance to installing new cooling
equipment on the engine or upgrading to a lease on a model

IOT DATA ANALYTICS CHALLENGES
IOT data places two specific
challenges on a relational database
1. Scaling Problems
2. Volatility of Data

Due to the large number of smart objects
objects in most IOT networks that continually send data,
data, relational databases can grow incredibly large very
very quickly. This can result in performance issue that
can be costly to resolve, often requiring more hardware
and architecture changes.
2. Volatility of Data:
With relational databases, it is critical that the
schema be designed correctly from the beginning. Changing it
Changing it later can slow or stop the database from
operating. Due to the lack of flexibility, revisions to the
schema must be kept at a minimum. Iot data, however, is
volatile is the sense that the data model is likely to change
and evolve over time. A dynamic schema is often required so

NoSQL, is being used. Structured Query
Language(SQL) is the computer language used to
communicate with an RDBMS. As the name
implies, a NOSQL database is a database that
does not use SQL. It is not set up in the
traditional tabular form of a relational database.
NOSQL databases do not enforce a strict schema,
and they support a complex, evolving data model.
These databases are also inherently much more
scalable.
In addition to the relational database
challenge that IOT imposes, with its high volume
of smart object data that frequently changes, IOT

data analytics.pptx

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