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What Is Data Analysis? (With Examples)
Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.
"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.
This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.
Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.
The World Economic Forum Future of Jobs Report 2020 listed data analysts and scientists as the top emerging job, followed immediately by AI and machine learning specialists, and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.
Read more: How to Become a Data Analyst (with or Without a Degree)
Data analysis process
As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.
Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?
Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).
Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.
Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.
Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?
Watch this video to hear what data analysis how Kevin, Director of Data Analytics at Google, defines data analysis.
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Learn more: What Does a Data Analyst Do? A Career Guide
Types of data analysis (with examples)
Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.
In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.
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Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.
Descriptive analysis answers the question, “what happened?”
If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.
Diagnostic analysis answers the question, “why did it happen?”
So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.
Predictive analysis answers the question, “what might happen in the future?”
Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.
Prescriptive analysis answers the question, “what should we do about it?”
This last type is where the concept of data-driven decision-making comes into play.
Read more : Advanced Analytics: Definition, Benefits, and Use Cases
What is data-driven decision-making (DDDM)?
Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.
This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].
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If you’re interested in a career in the high-growth field of data analytics, you can begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare yourself for an entry-level job as you learn from Google employees — no experience or degree required. Once you finish, you can apply directly with more than 130 US employers (including Google).
Frequently asked questions (FAQ)
Where is data analytics used .
Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance.
What are the top skills for a data analyst?
Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python), machine learning, and spreadsheets.
Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022
What is a data analyst job salary?
Data from Glassdoor indicates that the average salary for a data analyst in the United States is $95,867 as of July 2022 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location.
Do data analysts need to be good at math?
Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.
Learn more: Data Analyst vs. Data Scientist: What’s the Difference?
World Economic Forum. " The Future of Jobs Report 2020 , https://www.weforum.org/reports/the-future-of-jobs-report-2020." Accessed July 28, 2022.
McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed July 28, 2022.
Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed July 28, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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Sample Data Analysis Paper
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Data Analysis Essay
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Data Analysis Essay Examples
As there is massive growth in technology, the usage of data is also increasing day by day due to social networks and cloud computing. Lots of data was getting used and there was no back up for the data, so analytics of data was introduced....
Many sales executives talk of budget, needed timeframe and authority (i. e. BANT) to get leads. Some claim they need appointment with the right person and some businesses are using webinars, blog subscriptions, sign ups, emails etc. to reach buyers. The B2B sector is growing...
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Tesco are a large multinational organisation. Initially Tesco was founded in 1919 by Jack Cohen as a grocery stall in East London, with the first store opened in Edgware in 1929. The first self-service store was opened in 1948 in Hertfordshire, with “a mixed reaction...
This report aims to highlight how the retail stores are using big data and predictive analytics to know their customer’s need and do personalized advertising. The report focuses on the enhanced marketing operation of US retail giant Target through the use of big data. Target...
Our expanding reliance on data advances and self-ruling frameworks has heightened global worry for data and digital security even with politically, socially and religiously roused digital assaults. Data fighting strategies that meddle with the stream of data can challenge the survival of people and gatherings....
This report aims to highlight how the retail stores are using big data and predictive analytics to know their customer’s need and do personalized advertising. Report focuses on the enhanced marketing operation of US retail giant Target through the use of big data. Target was...
According to the paper, the panther species of North America is listed as endangered and is one of the last from that area. A panther population of Florida was rediscovered and efforts immediately went underway to conserve, support, and protect them. The authors gathered data...
The authors attempt to address the problem of joining tables when the datasets are collected from different sources and their key columns are formatted differently. Joins are very useful when it comes to combining records for data analysis and the present-day tools available allow to...
As indicated by Database (2002) underdeveloped nations allude to nations of Africa, Asia and Latin America, which were portrayed as nations with low wage, high neediness rate, joblessness, high populace and war. This exposition fundamentally show how monetary improvement tended to make the lives of...
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