WHAT IS DATA ANALYTICS?
Data analytics is a science in which a set of the data is analyzed and results are
given based on the analysis. Many people wouldn't & get it in the first time and
many would think that how is it possible to predict the results based on data? But
data analytics is indeed the most overwhelming technology at present. Data
analytics has its application in every sector of the market.
Data analytics is a science in which raw data sets are collected and then analyzed.
Data analytics is majorly used for decision making, predictions, etc. What is data
which is analyzed and how do we get it? People who analyze the data are known
as data scientists. The data which is analyzed by them is the data of the people.
This data can be in any form. For example, videos, audios, texts, etc.
WHY SHOULD WE USE DATA ANALYTICS?
As said above, data analytics is majorly used for decision making, predictions, etc.
How can we predict results from the data? The best example of this would be
weather forecasting. At present, weather forecasting is based on data analytics.
The temperature and other physical parameters of different cities are recorded
and then analyzed. Based on the data of physical parameters, it is predicted that
how much chance of the rainfall is there. Suppose that the chances of rainfall of
those cities would be more where the temperature is more. If that state would
have a good rainfall then there are some chances that nearby states would also
have rainfall. In this way, weather forecasting is done. However, it is not accurate,
but it improves with more data and experience.
Just like weather forecasting, data analytics is used in other sectors too. For
example, consider the healthcare sector. In the healthcare sector, the records of
the patients are analyzed. They analyze which diseases does a patient have and
which diseases he/she can have in the future.
HOW ANALYSIS OF THE DATA IS DONE?
Here are some steps which are followed for analyzing the data. They are listed
below
- The first step is the grouping of the data. The data which we have collected should be sorted. We can sort them by making groups of them. For example, we group them based on name, gender, age, etc.
- The second step is the gathering of the data. The data can be collected through various resources like laptops, computers, smartphones, environmental sources, cameras, etc.
- After the accumulation of the data, the data is organized and stored properly. Here, organization of the data means that the data is stored in the spreadsheets, etc.
- After the organization, the data gets cleaned. Cleaning the data means unwanted data is removed from the set of data. All the data which we collect is not of use. We have to extract useful data from it.
TYPES OF DATA ANALYTICS
Analysis of the data is not an easy task. Though, analysis too has its types. There
are different types of data analytics. They are listed below
- Descriptive analytics
- Prescriptive analytics
- Predictive analytics
- Diagnostic analytics
DESCRIPTIVE ANALYTICS
In descriptive analytics, the data of the past is analyzed. It is analyzed that why
things have occurred in the way they occurred. The past data is analyzed and the
reasons for past happenings are observed.
PRESCRIPTIVE ANALYTICS
In prescriptive analytics, the data of the past is analyzed. Based on that analysis,
future decisions are prescribed. In the prescriptive analysis, we think what we
should do now.
PREDICTIVE ANALYTICS
In the predictive analytics, the past data as well as present data, is analyzed and it
is predicted that what could happen now. In predictive analytics, the problems
that can occur in the future are predicted.
PHASES OF THE DATA ANALYTICS
As said above, we cannot use the raw data that we have collected as it is. We have
to extract useful information from it. For that, some operations are performed on
data. During those processes, the data goes through some phases. They are listed
below
Data requirements specifications
In this phase, the data which is required is collected. We do not collect all the
data of a person. If we do so, then it would become very difficult to store and
process such a huge amount of the data. That’s why it is identified that which
data is required and then it is collected.
Data collection
In this phase, the data is collected from the people. It is made sure that the
data which is collected should be accurate. The data can be collected from
different sources. Moreover, it is not mandatory that the data which is
collected would be structured. It could be unstructured as well.
Data processing
After the data collection, the data is sent for processing. In this phase, the data
is organized or arranged in a structured form. The organization of the data is
important. The data is arranged in columns and rows in the table,
spreadsheets, etc. Along with this arrangement, different data models are also
created. These data models are systems based on which outputs are generated.
Data cleaning
After processing, the cleaning of the data is done. The data which is collected
may contain errors, duplicate values, etc. In this phase, these errors are
removed. The method of the data cleaning depends on the type of data which
we have collected.
Data analysis
After all these processes, the data becomes ready for the analysis. For the data
analysis, different techniques are used which interpret, understand and give
conclusions according to the requirements. Moreover, data analysis is also
done in the visual form by representing them in the charts, graphs, etc.
Different models are used for the analysis of the data. For example,
regression, correlation, etc.
Communication
After the analysis of the data, the results are given in that format which the
user has requested. Then, the users are asked for the feedback. Based on the
feedback, additional analysis is done if required. As said above, the data is also
analyzed in the visual form. This is so because the visualization makes
communication easier. The data is better understood by the users with the
help of visualization.
However, all these processes are iterative. They can be repeated throughout
the process. For example, in the data analysis phase, additional data cleaning
may be required.
ADVANTAGES OF THE DATA ANALYTICS
Here are some advantages of data analytics. They are listed below
- The data analytics is beneficial in removing mistakes and errors from the data which we have collected with the help of data cleaning methods. This activity helps in improving the quality of the data. The output generated from the good quality of the data would be more efficient and beneficial.
- Data analytics saves space by saving memory. In the data cleaning method, errors, duplicate values and mistakes are removed from the data. The duplicate values won't occupy memory because they would not exist. The less use of memory reduces the cost to the company.
- Data analytics helps in displaying the relevant advertisements on the shopping websites. The relevant advertisements are displayed to each user based on their shopping behavior, historic data and the products they are looking for.
- Data analytics is also beneficial in the banking sector. Data analytics helps in detecting the fraudulent people based on their historic data.
- Data analytics is used by security companies. They use data analytics for monitoring and surveillance purposes. This is done by analyzing the data which is collected from the sensors.
- Data analytics helps in weather forecasting as well.
DISADVANTAGES OF THE DATA ANALYTICS
Here are some disadvantages of data analytics. They are listed below
- Many people may have problems with the data collection because their data is going to someone else. This may disturb their privacy.
- The cost of the different data analytics tools varies based on the features and services they provide to the users.
- The results which will be generated by the data analytics can be misused by some people.
CONCLUSION
Data analytics has growing importance. It is also increasing its importance in
education. Many people are willing to study the data analytics courses. Many
courses are available which offers complete data analytics training.