A Guide to Data Analysis (With a Definition and FAQs)

By Indeed Editorial Team

Published 2 August 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data analysis is the process of collecting, reviewing and analysing raw data to make informed decisions. There are several methods of analysing data that serve different purposes, such as identifying relationships between data or determining anomalies in statistical patterns. In this article, we define data analysis, discuss why it can be important, detail its process and list the types of data analysis.

What's data analysis?

Data analysis is the extraction, collection, ordering and review of raw data, such as statistics and texts. Raw data refers to collected information that has not been processed in terms of management, ordering, cleaning or analysis. An example of raw data might be a collection of words commonly used in job descriptions for a specific profession. It may also refer to statistics, such as the number of sales from retail stores. Raw data by itself rarely provides effective information for making business decisions and forecasting outcomes. Analysts typically review and organise the raw data to make sense of the information.

Analysts may use specialised software to review this raw data and determine patterns, relationships and anomalies. For example, analysts may review the raw data of retail store sales and implement processes to group the sales by location categories. They may further categorise the sales statistics into brackets that differentiate large sale numbers from small sale numbers. By conducting data analysis processes, analysts can begin identifying relationships between raw data. For example, by ordering retail store sale statistics, analysts can identify the average number of sales for retail stores of a specific size and location.

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Why is data analysis important?

Data analysis is important because it allows business executives and professionals to make informed business decisions. When businesses collect analysed data, they can predict the outcome of potential operations and activities. For example, an investment firm may collect analysed data to determine the likelihood of a specific company's stock price rising or falling. This information allows businesses to make decisions that can reduce risk, clarify uncertainty and improve business modelling.

Data analysis can also be important because of current methods of data collection. Most analysts use data collection tools that incorporate artificial intelligence (AI). These data AIs collect large quantities of raw data, which can require specific processes to make useful for businesses. While these systems can collect a large amount of data, the data can sometimes be unreliable or irrelevant. For example, an AI scanning job descriptions for typical skills might collect data that doesn't relate to skills. Data analysts may manually 'clean' this data, which is essentially the process of eliminating raw data that's irrelevant.

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What is the process of analysing data?

Below, you can explore the typical process of analysing data:

Identifying data requirements

The first step in analysing data is usually to determine the data requirements. This typically refers to the reason you want to collect the data, the type of data you want to collect and the purpose of this data. For example, you may collect data to identify potential property for investments. The type of data you're collecting is property prices and the data's purpose is to identify the average property price of different suburbs and states. By identifying these requirements, you can begin collecting data that are relevant and useful.

Collecting data

With your requirements identified, you can begin collecting data that's useful for making beneficial decisions for the business. The type of data you collect can depend on your data collection method. The most common and efficient method is usually using AI to scan databases and determine relevant information. Alternatively, you may manually research documents and records to gather data, but this can be relatively time-consuming. By using AI, you can calibrate automated systems that scan selected sources for data. These sources are usually online sources, such as government and public databases.

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Cleaning data

Not all the data collected may relate to your needs, so you may 'clean' the data to make it more accurate. While data AI platforms are smart, there are still some inefficiencies. For example, an AI scanning job descriptions for typical accountant skills might scan a description that has the business's location in the text. If the location includes a word similar to a skill, such as botany, the AI may register botany as an accountant skill. You may then 'clean' this data by removing illogical and irrelevant skills, such as botany skills for an accountant.

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Organising data

After you collect and clean the data, you may organise it into categories. The most effective method of organising data can be to use data analytics software. By using software, you can assign predetermined categories that automate the assignment of data sets to relevant categories. For example, if you're collecting financial statistics for business performances, the software may register different statistics, such as sales, profit and expenses. This can help you analyse the data efficiently, as you can easily identify the statistics by viewing groups of data.

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Interpreting data

Once you've collected, cleaned and organised the data, you can effectively interpret it to determine relationships and anomalies. When interpreting data, it's usually helpful to have knowledge of the relevant industry. If you're a financial analyst, you may understand fundamental business and economical processes. This can help you determine patterns and make conclusions. For example, by analysing stock price fluctuations, you might develop an economic forecast that suggests the potential movements of stock prices in a given period.

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Visualising data

After interpreting the data, you typically visualise it in a graphic that helps clients and stakeholders understand. You can use data visualisation tools to assign data to a specific visualisation. The visual you use can depend on the purpose of the data. If you want to show executives the performance of the business, you might assign data to a line graph. This can illustrate the rate of increased business performance over a period. Visualising this data can be crucial for allowing executives to understand the data, which can allow them to make decisions with reduced risk and improved clarity.

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What are the types of data analysis?

There are typically four fundamental types of data analysis that serve unique purposes. A helpful way to differentiate between these methods can be to identify the questions they answer. Below, you can explore the four fundamental data analysis methods:

1. Descriptive analysis

This type of analysis helps businesses answer the question 'What happened?' It involves the collection, cleaning and organising of data sets to analyse past statistics. For example, a company might conduct a descriptive analysis to identify the number of new customers gained in a specific period. They might conclude that they increased their client base by 10% over four months. The answer to the business' question 'What happened?' is that they increased their customer base by 10%.

2. Diagnostic analysis

This analysis method helps businesses answer the question 'Why did this happen?' For example, if a business's descriptive analysis outlined a 10% increase in customer base, they might conduct a diagnostic analysis to determine why they increased their customer base by this amount. Analysts may review several business process performances, such as marketing and sales strategies. They may discover that their marketing strategies were 30% more effective over a given period, leading to an increase in their customer base.

3. Predictive analysis

Predictive analysis focuses on answering the question 'What can happen in the future?' This analysis method includes analysing current and past statistics to develop forecasts for future business outcomes. For example, a company might conduct a predictive analysis to determine how much their customer base might increase in the next financial year. By reviewing their current and past sales performance, they might forecast a 30% increase in their customer base over the next 12-month period.

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4. Prescriptive analysis

Prescriptive analysis helps businesses answer the question 'What can we do?' An analyst might use the results from descriptive, diagnostic and predictive analysis methods to determine the next steps for the business. For example, the business understands its customer base may increase by 30% over the next financial year. The business now wants to know what it can do to increase this percentage. They decide to improve their sales and marketing strategies to increase their customer base by a further 10%. The business is making an informed business decision based on the analysed data.

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