Data Analysis Methods Every Business Analyst Needs To Know

Posted on Jan 18, 2023 at 10:01 PM

Data analysis methods are one of the essential skills for any business analyst. It's more than just analysing data and reporting it to stakeholders. Data analytics can be used to make decisions, improve processes and create value for the business.

In this article, you'll find an introduction to data analysis, the primary classification of data science methods, and the main techniques used in each one.

What is data analysis?

Data analysis inspects, cleans, transforms, and models data to discover useful information, suggesting conclusions, and support decision-making. Data analysis often involves statistical methods but may also be based on complex mathematical models and other approaches. Data analysis is distinguished from data manipulation (see below) in that data manipulation is not driven by a desire to reveal underlying structures or systematics in the data set.

Data analysis skills have multiple applications in business intelligence systems such as CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and BI (Business Intelligence). It can also be used for scientific investigation into economics and psychology.

Why do companies use data analysis methods?

Companies use data analysis methods for a variety of reasons, including:

• Business intelligence and machine learning- to help managers make decisions about their business based on a scientific methodology. These types of analyses are used to provide information about the company's operations, customers, and suppliers.
• Data mining and analysing - to find patterns in large amounts of data that can be used to predict future activity. Data mining tools can look for ways that may not be apparent in an organisation's current data and summarise and interpret them using data algorithms to get better business results.
• Business forecasting - to help predict future demand for products or services; this type of analysis is also known as predictive modelling. Businesses use business forecasting tools to plan and make informed decisions about their businesses.

What are data analysis methods?

Several data analysis methods can be used to analyse different data types. Each method has its strengths and weaknesses, but they have proven helpful in analysing datasets.

1- Quantitative Analysis method

Quantitative data analysis involves using statistical methods to analyse numerical data. It gives insight into relationships between variables, such as how one variable affects another variable. It can also determine the probability of specific outcomes or predict future events based on past performance. Examples of quantitative analytical techniques include regression analysis, hypothesis testing, and time series forecasting.

Quantitative analysis is commonly used by scientists and in statistics because it can be applied to almost any data type. However, it is only sometimes appropriate for analysing qualitative data (e.g., opinions), which cannot be measured numerically.

2- Qualitative Analysis method

Qualitative analytics involves studying text-based data to understand patterns and trends. This type of analysis has been applied to social media networks, where it’s used to identify topics that people discuss online and their reactions to those topics over time. It’s also used in marketing research, predictive research, customer service operations management, and political campaigns.

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Data analysis techniques:

Data analysis techniques are tools that business analysts use to make sense of their data and make decisions based on it. There are many different types of data analysis methods, some are quantitative, and others are qualitative; here are a few:

Quantitative data analysis techniques include:

1- Statistical inference

 The statistical technique is used to determine whether an event has occurred, such as in a clinical trial. Statistical inference can also be used to describe how likely an event is to have taken place and at what probability level (also known as a p-value), for example, determining whether a new drug has been effective in reducing people's cancer rates.

2- Visualisation

 Visualising your data can be an effective way to understand it. This might include creating charts and graphs that show how or where your data fits into a larger picture using various tools.

3- Descriptive analysis method

 Descriptive analyses focus on the characteristics of your data rather than its values. These studies include descriptive statistics like mean, median, standard deviation, and range. Descriptive statistics can be used to describe a remarkable correlation between variables or types within that population. 

4- Inferential statistics

Inferential statistics use your sample or cluster (the small part of your population) as evidence for making predictions or insights about large amounts of the population according to your collected finding. Inferential statistics are used to make meaningful predictions about how likely a particular event will happen in the future based on your sample data. 

Qualitative Data Analysis Techniques include:

1- Content analysis

 Content analysis involves analysing text (usually from documents) to identify themes in the text (generally using software). The most common type of content analysis is content coding, which assigns codes based on the content of your text.

2- Thematic analysis

The thematic analysis involves organising your data into categories or themes. It is useful when you have a large number of variables that are difficult to manage, such as text data or qualitative data.

3- Grounded theory

Grounded theory is a qualitative approach that aims to develop a theory from data. It involves generating categories based on data characteristics and then using those categories to extract a theory or explanation.

Data analysis methods are helpful for any business analyst. They can help solve a problem, improve an existing solution, or identify new product and service opportunities. They’re a great tool that every business must have.