What is data analytics and how does it work?
Data analytics is
defined as "the process of analysing raw data in order to discover trends
and answer questions that captures the field's broad scope." That does,
however, cover a wide range of approaches with a wide range of objectives.
Some aspects of the
data analytics process are applicable to a wide range of initiatives and
projects. A effective data analytics initiative would provide a clear picture
of where you're from, where you 've been, and where you'll be going by
combining these elements.
Typically,
descriptive analytics is the first step in this process. The process of
describing historical trends in data is known as historical trend analysis. The
goal of descriptive analytics is to provide an answer to the question,
"What happened?" Traditional indicators such as return on investment
are frequently used in this context. Each industry will have its own set of
indicators that it will use. Descriptive analytics does not make predictions or
provide decision-making information. The primary goal of this paper is to
interpret and describe the data.
The following step
is advanced analytics, which is a critical component of data analytics. This
subfield of data science makes use of sophisticated tools to extract
information from data, make predictions, and uncover trends. Among the
available tools are traditional statistics and deep learning. "Advanced
analytics are enabled by machine learning technologies such as neural networks,
natural language processing, sentiment analysis, and other similar techniques."
This data is derived from statistical data and provides new information. The
focus of advanced analytics is on the "what if?" questions.
Machine learning techniques, large data sets, and low-cost computing capabilities have facilitated such technics to be used in a variety of fields and industries. Large amounts of data must be collected in order to develop these techniques. When businesses have access to large amounts of cheap computing power, they can draw meaningful conclusions from a wide range of complex and diverse datasets. This is due to the increased use of parallel processing and the availability of inexpensive computing power in large quantities.
Methods and techniques for data
analytics are addressed.
When it comes to data analysis,
data analysts employ a variety of methods and techniques. The following are
seven of the most popular job titles,
Regression analysis: “When estimating the
relationships between variables, regression analysis is used to determine how
changes in one or more variables may affect the relationships between other
variables”. For example, what impact might social media advertising have on
sales?
Monte Carlo simulation: “When dealing with a process that
cannot be predicted easily due to the intervention of random variables Monte
Carlo simulations are used to model and predict the probability of different
outcomes”. It is frequently employed in the field of risk analysis.
Factor analysis “As a statistical method, factor
analysis can be used to take a large data set and condense it into a smaller,
more manageable one”. This has the additional benefit of frequently revealing
previously unseen patterns. In the business world, factor analysis is
frequently used to investigate issues such as customer loyalty.
Cohort analysis “Cohort analysis is a technique
for breaking down a dataset into groups of people who have similar
characteristics, known as cohorts, for the purpose of analysis”. This is
frequently employed in order to better understand customer segments.
Cluster analysis: “Cluster analysis is a
class of techniques that are used to classify objects or cases into relative
groups known as clusters For example insurance companies might use cluster
analysis to figure out why certain locations are associated with certain types
of insurance claims which could lead to a better understanding of how the data
is arranged”.
Time series analysis: “Time series analysis is a
statistical technique that deals with time series data, also known as trend
analysis”.Data in a time series refers to data that is organised into a series
of specific time periods or intervals. For example, weekly sales numbers can be
used to identify trends and cycles over time by using time series analysis (see
Figure 1). It is frequently employed in the economic and sales forecasting
industries.
Sentiment analysis “In order to understand the
feelings expressed in the data, sentiment analysis makes use of tools such as
natural language processing, text analysis, computational linguistics, and
other techniques”. “Sentiment analysis is a qualitative method that seeks to
interpret and classify qualitative data by organising it into themes, whereas
the previous six methods sought to analyse quantitative data (data that could
be measured). It is frequently employed in order to determine how customers
feel about a particular brand, product, or service”.
- Shailesh Maurya
- Mar, 25 2022