10 Major Differences Between Data Analytics,Data Analysis and Data Mining

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Welcome To Loginworks Softwares
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10 Major Differences Between Data Analytics,Data
Analysis and Data Mining

• People are generating tons of data every second.
  Every post on social media, each heartbeat, every
  link clicked on the internet is data. The world
  generated more than 1ZB of data in 2010. The
  massive data are often stored in data warehouses.
  These warehouses collect data from all possible
  sources. However, these data are often
  unstructured and meaningless, therefore,
  professionals need to make sense of them. Experts
  in this field use certain tools to make sense of
  these data in order to help businesses make an
  informed decision. Hence, those tools include
  data analytics, data analysis and data mining.
• The terms data analytics, data analysis and data
  mining are used interchangeably by people.
  However, there are small differences between the
  three terms. In simplest terms, data mining is a
  proper subset of data analytics and data
  analytics is a proper subset of data analysis and
  they are all proper subset of data science. It is
  easy to get confused, read on to get a better
  understanding of the three terms.

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Data Mining

• We are starting with data mining because it is
  the smallest in the set were considering. Every
  tool, method or process used in data mining is
  also used in data analytics. Data analytics is
  data mining plus more. Wikipedia defines data
  mining as the process of discovering patterns in
  large data sets involving methods at the
  intersection of machine learning, statistics, and
  database systems. The Economic Times defines it
  as process used to extract usable data from a
  larger set of any raw data. These definitions
  give an overview of what data mining is about.
  Lets delve deeper.

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Data Analytics is the superset of data mining and
a proper subset of data analysis. Data analytics
involves using tools to analyze data in making a
business decision. For instance, your business
offers massage services to people using electric
massage chairs to help relieve stress and
backache. If youre interested in knowing who
patronizes you, then you can create a table of
your customers. You can further group your data
by occupation, age, home address, etc using the
data analytics tool.

Quantitative techniques use mathematical and
statistical tools and theories to manipulate
numbers to obtain a result or pattern. On the
other hand, qualitative analytics is
interpretive, it is the use of non-numerical data
such as images, audio, video, point of view,
interviews or texts. More advanced data analytics
tool include data mining, machine learning, text
mining and big data analytics. Data analytics can
also refer to software ranging from business
intelligence (BI), online analytical processing
(OLAP).

Data analytics starts with defining the business
objective, collecting data, checking for data
quality, building an analytical model and then a
decision based on the outcome.

1. Business objective Data analytics starts with
understanding the final goal. The team needs to
know what is required of them. This is part the
team plans, select the possible dataset and
establish project plans in line with company
goals.

2. Collecting data The team selects the data
that is required to carry out the analysis they
want. Since data comes from different sources.
The team has to check and collect data that are
most relevant to the information they are trying
to find out.

3. Data quality This is where the team ensures
the raw information is as clean as possible.
Dirty data can influence results negatively and
may cause the management to make wrong decisions.
This is a very crucial step in data analytics.
The data team must verify the data quality to
ensure it is what is required.

4. Building analytical models Once the team
ensures the data is clean, the team gathers the
data for analysis and they build analytical
models. This is done with analytics software and
programming languages such as Python, SQL, R and
Scala. In most cases, a test run is done on the
data to check if the outcome is close to or in
line with the predicted outcome. If this turns
out okay, the team then runs full analysis.

5. Outcome and decision The next stage is the
outcome, the result is evaluated. The team checks
for accuracy of the results and degree of error
generated. The result is then deployed, a report
is written and the team performs a final check on
the project as a whole. This is termed project
review. Once, this is done, observations and
results are passed to the management to make an
informed decision.
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Data Analysis

• EDUCBA defines data analysis as extracting,
  cleaning, transforming, modeling and
  visualization of data with an intention to
  uncover meaningful and useful information that
  can help in deriving conclusion and take
  decisions. This definition is comprehensive and
  it covers every aspect of data analysis. However,
  John Turkey, a world-renowned statistician, added
  that data analysis includes making the results
  more precise or accurate over time.
• Data analysis often used interchangeably with
  data analytics, however, there are slight
  differences between them. In the definition of
  analytics, we saw that it involved the use of
  specialized software and tools. Data analysis is
  a broader term and it fully engulfs data
  analytics, in other words, data analytics is a
  subcomponent of data analysis.

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Data analysis involves both technical and
non-technical tools. There are several stages in
data analysis and the phases can be iterative to
improve accuracy and get better results. Data
analysis is very wide and teams work on different
aspects. However, we state the most common steps
used by data analysis teams. This is putting a
team together, understanding business objective,
data collection, data cleaning, data
manipulation, communication, optimise and repeat.

1. Put a team together In testing any
hypothesis, the first step is to put a team
together that would carry out the analysis.

2. Business objective The problem bugging the
business is put across to the team. This serves
as the background of the analysis the team hopes
to get a hypothesis on.

3. Data collection Once the team understands the
business objective, it set out to collect data
needed.

4. Data cleaning This is a very important and
crucial step. This is identifying inaccurate or
incomplete data and deleting or modifying them.
Dirty data can lead to wrong conclusions which
can be fatal for a business. The team has to
ensure the data is as clean as possible. This is
the stage the data is inspected.
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5. Data manipulation In this stage, the data is
subjected to mathematical and statistical
methods, algorithms modelling of data. The data
is transformed from one structure to another.

6. Optimise, communicate and repeat Before
communicating results and reports to the
management, the team has to optimise the data by
checking and accounting for error due to
calculation or mathematical method. Once, the
results are ready, the team presents their
findings to the management in form of images,
graphs or video. If results require the new
perspective, then the team would repeat the
process from the beginning.
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