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Beginner's guide to data analysis methods

By Team Multiverse

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  1. What is data analysis?
  2. Qualitative vs. quantitative data analytics
  3. The data analysis process
  4. Data analysis tools
  5. Learning data analytics with Multiverse

Data analytics is one of the most promising fields of work. Most businesses today are drowning in raw data. Companies have learned how to collect data, but most of them still don’t know how to make the most of it. That’s where data analysis methods come in.

Not sure what data analysis is or how it works? Keep reading for everything you need to know about analytics and data analysis methods. We’ll cover:

  • What is data analysis?
  • Qualitative vs. quantitative data analytics
  • The data analysis process
  • Quantitative data analysis methods

What is data analysis?

Data analysis turns raw data into actionable insights. Data Analysts collect and make sense of information so companies can improve efficiency, profit and more. Ultimately, data analysis helps companies make better decisions that contribute to success.

Qualitative vs. quantitative data analytics

The two main forms of data analysis are qualitative and quantitative.

Quantitative analytics

Quantitative analytics focuses on data that you can measure or quantify with numbers. Some examples of quantitative data include:

  • Costs and revenue numbers
  • Weight and measurements
  • Projections and forecasts
  • Anything you can count or quantify

You usually use quantitative data analytics to answer questions about how much, how often or how many.

For example, Google Analytics is a rich source of quantitative data, especially for your website. Analysing web traffic shows how your customers find your website and what they engage with when they’re there.

Qualitative analytics

Qualitative analytics looks at those harder-to-define areas, such as customer satisfaction levels or user experience.

Qualitative analysis can answer questions about how customers interact with a company. For example, qualitative analysis might provide insights into how to attract loyal customers or convert new leads. It also reveals the ‘why’ behind behaviours. You could use it to understand why:

  • Customers buy from you
  • People chose a competitor over your brand
  • Some products are more popular than others
  • Your sales increase or decrease

The data analysis process

Data analytics is an interactive process that, when done correctly, requires multiple steps to go from raw data to conclusion. The basic steps of the data analysis process are:

1. Define the question

Data analysis aims to answer questions. The more narrowly you can define your question, the better. These are a few questions effective data analytics can answer:

  • How much time do users spend on your app?
  • What’s driving your website traffic?
  • How often do customers recommend your products?
  • Which products do your customers prefer?
  • How much does the average consumer spend per visit?

To get the most out of your data, you’ll need to formulate your questions clearly beforehand. Defining your questions gives you a roadmap to follow when you begin your data analysis.

2. Collect data

Once you know the goal or question you want to answer, you must collect accurate and relevant data to analyse. You can collect it from a variety of sources, but most Data Analysts start with internal or first-party sources. Companies collect this type of data directly from customers and other tools.

These include:

  • Customer relationship management (CRM)
  • Website heatmaps
  • Customer and user surveys
  • Google analytics and other tracking tools
  • Marketing and sales data

You can also supplement data with external sources depending on your goal. Examples include:

  • Review websites
  • Public and government sources
  • World health data

3. Clean and organise data

Raw data, on its own, is difficult to organise. Before you can analyse data, you’ll need to clean it and put it into a format that you can use. Check for duplicates and delete any unnecessary information. Make sure that you haven’t left any fields blank, as this can throw off your data.

4. Analyse data and pull insights

Analysing data typically means looking for patterns, drawing connections and then, understanding what it all means. To make sense of data, you can use various data analysis methods. As you spot trends and connections, you’ll be better able to answer your initial question.

5. Visualise the data

You can visualise data by putting it in charts, graphs, tables and other visuals that help someone quickly interpret it. Data visualisation is an ideal way to share and explore insights with the rest of your team.

Once you’ve visualised and shared your analysis, you and your team can build an action plan or make decisions from the insights.

This is also the stage where you’ll become aware of any limitations in your data analysis. Visualising data can reveal holes or missing data in the process. Are you missing data points? Do the answers to your questions seem incomplete?

If so, go back and complete your analysis.

Quantitative data analysis methods

As previously mentioned, there are qualitative and quantitative data analysis methods. Qualitative data helps you understand why people make certain actions. It deals with words and feelings and you gather data through observation and interviews. Quantitative data focuses on information that you can measure. It puts insights in terms of quantifiable percentages and numbers that reveal how much, many or often.

In this post, we’ll focus on quantitative data analysis methods. But, both qualitative and quantitative analysis have important roles to play in data analytics.

Four of the most common quantitative data analysis methods are:

  • Regression analysis
  • Cohort analysis
  • Cluster analysis
  • Time series analysis

Depending on your goals and the specifics of your analysis, you may use one of these approaches, all of them or a blend.

Regression analysis

Regression analysis leverages historical data to reveal which variables have the most impact on your present outcome.

To carry out an effective regression analysis, you’ll need access to relevant data. In many cases, this means sales data. But, it can also include product quality, marketing, retail design and other relevant information.

Regression analysis is a useful way to make sense of changes in a business over time. For example, suppose you’ve noticed that your sales took a dip in the past year. You might want to do a regression analysis to try and understand which variables are driving the downward trend. Other questions it can help answer include:

  • Are consumers spending less across the board?
  • What’s changed over the past year— in your business and the overall economy?

Looking at a range of factors can help you pinpoint which variables drove the change that you’re seeing. Then, you can make educated decisions about how to address the change.

Cohort analysis

Cohort analysis looks at the behaviour of a particular set of people or “cohort.” To do a cohort analysis, you group cohorts based on similar behaviour or categories. Grouping cohorts together helps you more easily look at patterns and trends.

If you want to better understand how and why a group behaves a certain way, use cohort analysis. For example, you can do a cohort analysis to understand when users churn, or stop using your product.

To begin, you’d group users based on when they began using your product. Then, look at what points that group dropped off. You can further analyse cohorts to identify why users churn, form a hypothesis and an action plan to keep users.

Overall, cohort analysis can help you understand your customers better. You can use it to decide which product features to prioritise or craft more effective marketing campaigns.

Cluster analysis

Cluster analysis groups data points together according to their similarities. The goal behind cluster analysis is to seek out patterns that you may not have noticed before.

As we have seen, cohort analysis and regression analysis are both focused on answering a particular question. In contrast, cluster analysis is a way of looking for patterns and insights that you may not have been aware of otherwise.

Cluster analysis is a great place to start when you really aren’t sure what’s driving a particular trend. It can often push you to explore new areas and to re-analyse your existing data.

Time series analysis

A time series analysis lets you zero in on changes in a particular variable over a period of time.

The beauty of time series analysis is that it isolates the variable. So, you get a broad picture of how it operates throughout the time period. You can also understand how the variable interacts with other variables.

Time series analysis allows you to make successful predictions about the future, based on past behaviour. For example, let’s say you notice that consumers buy more leather boots in the autumn. You’ll be able to predict when demand for those boots will increase and alter your inventory ahead of time. In general, the more accurately you can track past patterns, the more effectively you can predict the future.

Data analysis tools

The right tools can make beginning your data analysis career much easier. Here are some of the most useful data analysis tools that apprentices learn in Multiverse’s analytics programmes.

  • Excel: With Excel knowledge, you can clean and organise data. You can also create pivot tables to quantify data and more.
  • Business intelligence (BI): The best BI tools help you analyse and create reports to share with decision makers. Tableau is a data visualisation tool, but Multiverse apprentices also it to prepare data and build dashboards.
  • SQL: SQL is important for Data Analysts and Scientists to learn. You’ll use it to organise and extract data.
  • Data visualisation: In Multiverse’s data analytics apprenticeship training, apprentices learn how to visualise data in Power BI.

Learning data analytics with Multiverse

Data analytics jobs are in-demand and often come with higher salaries and better benefits. One of the best ways to begin your data analytics career is through an apprenticeship.

Unlike university, Multiverse apprenticeships are tuition-free—and you get paid a salary while you learn.

Multiverse partners with top companies to provide on-the-job training for a broad range of tech roles. Participants gain real-world knowledge and experience, all while building relationships with successful people in the field.

Multiverse provides training in data analytics, software engineering and more. To become an apprentice, check eligibility requirements and complete an application(opens new window). Apprentices must be between 16 and 24 and have the right to work in the UK. Depending on the program, you may need GCSE English and Maths 4-9 (C-A) or equivalent.

The process takes less than 15 minutes and once you submit your application, we’ll get to know you better. Then, identify apprenticeship opportunities for your goals and provide coaching and interview advice.

Team Multiverse

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