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Overview of data analysis - Training Microsoft Learn
Overview of data analysis - Training _ Microsoft Learn
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Data Analytics - Trainity
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Visvesvaraya Technological University
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Ｒ Previous Unit 2 of 6 Ｓ Next Ｔ
Overview of data analysis
6 minutes
Before data can be used to tell a story, it must be run through a process that makes it usable in
the story. Data analysis is the process of identifying, cleaning, transforming, and modeling data to
discover meaningful and useful information. The data is then crafted into a story through reports
for analysis to support the critical decision-making process.
As the world becomes more data-driven, storytelling through data analysis is becoming a vital
component and aspect of large and small businesses. It is the reason that organizations continue
to hire data analysts.
Data-driven businesses make decisions based on the story that their data tells, and in today's
data-driven world, data is not being used to its full potential, a challenge that most businesses
face. Data analysis is, and should be, a critical aspect of all organizations to help determine the
impact to their business, including evaluating customer sentiment, performing market and
product research, and identifying trends or other data insights.
While the process of data analysis focuses on the tasks of cleaning, modeling, and visualizing
data, the concept of data analysis and its importance to business should not be understated. To
100 XP
＂
analyze data, core components of analytics are divided into the following categories:
Descriptive
Diagnostic
Predictive
Prescriptive
Cognitive
Descriptive analytics
Descriptive analytics help answer questions about what has happened based on historical data.
Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders.
By developing key performance indicators (KPIs), these strategies can help track the success or
failure of key objectives. Metrics such as return on investment (ROI) are used in many industries,
and specialized metrics are developed to track performance in specific industries.
An example of descriptive analytics is generating reports to provide a view of an organization's
sales and financial data.
Diagnostic analytics
Diagnostic analytics help answer questions about why events happened. Diagnostic analytics
techniques supplement basic descriptive analytics, and they use the findings from descriptive
analytics to discover the cause of these events. Then, performance indicators are further
investigated to discover why these events improved or became worse. Generally, this process
occurs in three steps:
1. Identify anomalies in the data. These anomalies might be unexpected changes in a metric or
a particular market.
2. Collect data that's related to these anomalies.
3. Use statistical techniques to discover relationships and trends that explain these anomalies.
Predictive analytics
Predictive analytics help answer questions about what will happen in the future. Predictive
analytics techniques use historical data to identify trends and determine if they're likely to recur.
An underlying facet of data analysis is that a business needs to trust its data. As a practice, the
data analysis process will capture data from trusted sources and shape it into something that is
consumable, meaningful, and easily understood to help with the decision-making process. Data
analysis enables businesses to fully understand their data through data-driven processes and
decisions, allowing them to be confident in their decisions.
As the amount of data grows, so does the need for data analysts. A data analyst knows how to
organize information and distill it into something relevant and comprehensible. A data analyst
knows how to gather the right data and what to do with it, in other words, making sense of the
data in your data overload.
Next unit: Roles in data
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Overview of data analysis - Training Microsoft Learn
Course: Data Analytics - Trainity
28 Documents
Students shared 28 documents in this course
University: Visvesvaraya Technological University
Was this document helpful?
7/21/23, 11:23 AM
Overview of data analysis - Training | Microsoft Learn
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ＲPrevious Unit 2 of 6 ＳNext Ｔ
Overview of data analysis
6 minutes
Before data can be used to tell a story, it must be run through a process that makes it usable in
the story. Data analysis is the process of identifying, cleaning, transforming, and modeling data to
discover meaningful and useful information. The data is then crafted into a story through reports
for analysis to support the critical decision-making process.
As the world becomes more data-driven, storytelling through data analysis is becoming a vital
component and aspect of large and small businesses. It is the reason that organizations continue
to hire data analysts.
Data-driven businesses make decisions based on the story that their data tells, and in today's
data-driven world, data is not being used to its full potential, a challenge that most businesses
face. Data analysis is, and should be, a critical aspect of all organizations to help determine the
impact to their business, including evaluating customer sentiment, performing market and
product research, and identifying trends or other data insights.
While the process of data analysis focuses on the tasks of cleaning, modeling, and visualizing
data, the concept of data analysis and its importance to business should not be understated. To
100 XP
＂
