What Is Data Analysis and How Can You Get Started?

What Is Data Analysis?

Data analysis is the process of cleaning, analyzing, interpreting, and visualizing data using various techniques and business intelligence tools. Data analysis tools help you discover relevant insights that lead to smarter and more effective decision-making.

You’ll often see the terms data analysis and data analytics used interchangeably. But, there’s a slight difference between the two.
Data analytics is the overarching discipline and refers to the whole process of data management: data collecting, storing, organizing, and analyzing. It includes the tools and techniques used to deep-dive into data, as well as those used to communicate the results ‒ for example, data visualization tools.
Data analysis, on the other hand, focuses on the process of turning raw data into useful statistics, information, and explanations.

Quantitative vs Qualitative Data

What’s the Difference?

Quantitative

Qualitative


Quantitative data deals with whole numbers, percentages, and statistics data analysis – data that can be expressed as a quantity.

Quantitative data is usually structured data that is pre-formatted and fits neatly into the columns and rows of spreadsheets. It responds to close-ended questions like “how many?”, “how often?” and “how much?”.

Structured query language (SQL) is a programming language used to communicate with and organize databases and particularly useful when handling quantitative data. You can easily perform quantitative data analysis in Excel to answer questions like:

  • How many users signed up in the month of May?
  • What was the churn rate compared to the previous year?
  • What was the drop-off rate at the shopping cart?

Qualitative data deals with features or characteristics – data that describes something and is expressed in words.

Also known as unstructured databecause it has no format or pre-configured design, qualitative data allows you to understand the “why?” behind numbers and statistics and provides insights into behavior and patterns.

AI data analysis platforms, like MonkeyLearn are great for qualitative data because they use techniques like natural language processing (NLP) to break down unstructured data, so that it can be understood and analyzed by machines.

General purpose programming languages, like Python, are also ideal for qualitative data analysis because they are much more open-ended and can be used to call or integrate with a variety of data analysis tools.

With qualitative data analysis, you can answer questions like:

  • Why were there fewer app users in the month of May?
  • Why was the churn rate higher than last year?
  • Why was the drop-off rate so high at the cart?

 


Data Analysis Methods, Techniques & Examples:

The six top methods for data analysis:

 

  1. Text Analysis
  2. Data Mining
  3. Statistical Analysis
  4. Diagnostic Analysis
  5. Predictive Analysis
  6. Prescriptive Analysis

How To Get Started With Data Analysis

When you’re ready to get started with data analysis, you’ll find that there are a couple of options: build your tools from scratch or use a SaaS (software as a service) solution. It’s the eternal build vs. buy debate.

There are many open-source libraries that developers can use to build machine learning models for data analysis. While they’re free, they’ll take time to implement and require complex and costly infrastructure, not to mention large upfront investments. You’ll probably end up hiring a team of developers and data scientists if you decide to build your own solution.

SaaS tools, on the other hand, are a simple and cost-effective alternative. These tools are cloud-based and ready-to-use, allowing you to perform a variety of tasks, from text analysis to data visualization, in next to no time.

Some of the benefits of buying SaaS tools over building your text analysis software include:

  • Lower costs & easily scalable. While open-source software is free, you’ll need a team of machine learning experts to set up the infrastructure, build complex algorithms, and test your tools. SaaS tools, on the other hand, are ready to use solutions and no-code, low-code options make it easy for non-technical users to get started with data analysis tools. That means you don’t need to hire expert staff, and you won’t waste time waiting for a solution to be built. You can easily scale up or down with SaaS tools, which offer various plans depending on the amount of data you need to analyze.

  • Quick to set up. In such a competitive market, businesses need to move fast to stay relevant. With SaaS tools, you don’t have to spend months developing and fine-tuning your machine learning models. Instead, it takes less than a month to implement out-of-the-box data analysis solutions, so you can start gaining insights and creating better customer experiences right away.

  • Maintenance costs included. When you build your own data analysis software, you need to take into account maintenance costs. You’ll need to keep a permanent team of experts on to tweak hyperparameters, update models, and maintain on-site infrastructure. If you choose to buy cloud-based data analysis software, you won’t have to worry about additional high maintenance costs. They’re included in the monthly license fee. And, since SaaS data analysis tools are in the cloud, there’s no physical software to maintain or repair.

Start Your Data Analysis Journey

Data analysis helps businesses explore customer data and find insights to support and guide their decision making.

With data analysis tools, businesses can sift through large amounts of qualitative data in just minutes. This frees up staff from repetitive and time-consuming tasks and helps them obtain real-time insights about their customers. Knowing exactly what makes your customers tick helps you design better experiences for them, and provides you with a competitive advantage.

Start gaining more granular insights by combining different data analysis techniques and connecting your results to data visualization tools.

July 29, 2024