Big Data Analysis Methods, Techniques & Tools

These days, data flows into your business from every angle. With big data analysis methods, you’re able to transform raw data into usable insights to make key business decisions. Rather than having to manually sort through data, you can leverage big data analytics tools to automate and streamline the detail-oriented processes for you.

In this article, we will share what big data analytics is, see how data analysis tools can be of service, and understand the crucial importance of using analytics for your business functions.

Coming Up

What is Data Analysis?

What is Big Data?

Why is Data Analysis Important?

What is the Difference between Quantitative and Qualitative Data?

What are the Types of Data Analysis?

What are Big Data Analysis Techniques?

What is the Data Analysis Process?

What are the Best Tools for Data Analysis?

What are the Limitations and Challenges of Data Analysis?

Final Thoughts

What is Data Analysis?

Data analysis (DA) is the process of collecting data and drawing conclusions and insights from data sets. Data sets can come in the form of text, video, or audio and exist across multiple systems and software.

Data analysis begins with data collection and runs through its modelling and analysis to extract value. Upon completion of the analysis, you will have charts, tables, images, and graphs at your fingertips.

What is Big Data?

Data analysis is increasingly necessary because of the volume of data all around. Big data refers to large sets of data that are diverse.

This means that the data can be structured, unstructured, and semi-structured. It exists at high speed and in high volumes. While there’s no single definition of what big data is, it’s referred to data that checks off the 4 V’s, namely:

1. Volume: the size of the generated data
2. Velocity: the speed at which the data is both created and processed
3. Veracity: a measure of how accurate the data is for reliability purposes
4. Variety: the diversity of data, be it structured, unstructured, or semi-structured

Why is Data Analysis Important?

Imagine you have a book of codes, and if you can decipher the codes, you’d have a way to increase revenue, reduce costs, and amplify your business’ efficiency. In this scenario, the codes are raw data and the key to deciphering the code is data analysis.

Data analysis helps organisations to make informed business decisions. The data comes from so many different sources without end, including online tracking, surveys, social media monitoring, marketing analytics, and more.

Data analysis provides a way to paint a picture of each customer to better understand their behaviour, needs, and desires.

What is the Difference between Quantitative and Qualitative Data?

Big data analysis methods help to tackle the two main types of data available. These include quantitative and qualitative data. The difference is:

Quantitative

Quantitative data is measurable by numbers. It includes sales figures, percentage revenue increase, amount of website visitors, email click-through rates, and the like. This type of data can be approached with numerical analysis using statistics and algorithms. It’s best intended to make forecasts.

Qualitative

On the other hand, qualitative data is less subjective because it cannot be measured numerically. Qualitative data includes survey responses, product reviews, texts, social media mentions, and other kinds of unstructured data (like texts or transcripts).

What are the Types of Data Analysis?

There are several types of data analysis methods. Here are three of the most popularly used types:

1. Descriptive Analysis

Descriptive analysis answers the question, “What happened?” While it won’t tell you what you can expect moving forward or get to the root cause of why an occurrence took place, it will answer what the occurrence was for further investigation.

2. Diagnostic Analysis

As the name implies, diagnostic analysis answers why something happened. It provides context to a situation. When you have the combination of why and how an event transpired, you’re able to take action to prevent it from happening again (if it were negative) and promote it to happen again (if it was favourable).

3. Predictive Analysis

Predictive analysis does exactly as it sounds: predicts. This method answers, “What will happen?” by leveraging historical data and the power of machine learning and artificial intelligence. It’s useful to discover trends, problems, or inefficiencies.

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With the aid of automation software, you can enact the system to collect, transform, and analyse data for you. This way, you get the results you need without having to manually configure and apply formulas to gain insights.

What are Big Data Analysis Techniques?

By now, you may be asking, “What are the methods of data analysis?” And, that’s a great question because it’s exactly how you’ll get to the answers you seek!

In order to get what you need from data, you must use big data analysis methods, such as these:

1. A/B Testing

A/B testing is comparing a control group to a test group to figure out what variable changes will create the best outcomes. For example, this type of data analysis method is often used in email marketing to test different subject lines or images. Once the system figures out which one is generating the greatest level of engagement, it becomes the chosen selection.

2. Data Mining

Data mining finds patterns from large data sets by utilising statistical methods and machine learning. A great use of data mining is to figure out customer behaviour in order to offer the most fitting products to a specific segment of your entire customer database.

3. Regression Analysis

As a statistical method, regression analysis aims to determine the effect of an independent variable on a dependent variable. It figures out how a dependent variable changes with fluctuations in the independent variable. It can be used to decipher how customer satisfaction rates affect customer loyalty, for example.

4. Natural Language Processing (NLP)

Under the umbrella of computer science and artificial intelligence, natural language processing (NLP) uses algorithms to process human language. This can be used in a variety of settings, such as text-to-talk when phone assistants like Apple’s Siri are able to transcribe what humans say out loud and then respond with a fitting answer.

5. Cluster Analysis

This type of analysis is used when looking to extract patterns from large data. It works by grouping data elements that are like one another in a sense. It can also be used to add context to trends.

With a large customer base, it’s hard to understand each person’s behaviour one-by-one. Cluster analysis is a way to combine records together based on specific elements, be it demographics, purchasing behaviours, or something else.

6. Time Series Analysis

Time series analysis analyses data within a defined period of time. It helps to see how different variables affect outcomes at different points in time. It’s very effective for understanding seasonality effects of customer decisions.

What is the Data Analysis Process?

With whatever big data analytics method you wish to apply, the process of data analysis requires:

1. Pose the Question

What are you trying to answer by performing the analysis? This is referred to as your problem statement. It’s the definition of the business problem you are attempting to resolve. With this defined, you’ll be able to determine what kind of data you need to find your answers.

2. Collect Data

The next step is to aggregate the data you need to analyse. This will be either quantitative or qualitative data, or the combination of both.

3. Clean Data

Data doesn’t always come in the same format. In fact, it rarely does. For this reason, you’ll have to clean it first to remove any errors, duplicates, typos, or gaps. With the influx of big data, this could be immensely time consuming to do by hand. Rather, it’s a great place to leverage the aid of automation solutions to handle the data cleansing process for you.

4. Analyse Data

With your data ready to go, it’s time to apply your chosen big data analytics method. Remember, this could be: descriptive analysis, diagnostic analysis, prescriptive analysis, predictive analysis, or another form.

5. Share Results

Once you have the findings you need, it’s time to communicate the insights with stakeholders and those who were asking the questions in the first place. It’s best to prepare a visual report or graph to make it easy to read.

Automation solutions can generate customisable dashboards so that each respective stakeholder gets real time insights into what they need without having to dig.

What are the Best Tools for Data Analysis?

When looking for big data analytics tools, there is technology that works in your favour. Some of the top tools include:

• Hadoop: Hadoop is an open-source framework that can manage both structured and unstructured data.

• NoSQL Databases: This stands for “not only SQL” and refers to non-relational data management systems that are a good option for unstructured data.
  
  
• Python: Python is an object-oritented programming language that can be applied to data analysis.
  
  
• No-code Automation Solutions: Rather than having to know how to code, you can use an out-of-box solution with drag-and-drop functionality to execute your data analysis needs. Such tools will deliver insights in visual formats to make it easy for everyone to understand.

What are the Limitations and Challenges of Data Analysis?

While automation tools make data analysis a breeze, you may find your organisation facing some challenges, such as:

1. Lack of Direction

In order to set up your data analysis methods effectively, you have to first have a clear set of goals and business questions that you wish to answer. This helps to figure out the data you need, as well as to set guidelines to follow.

2. Data Visualisation

The outcome of data analysis is your insights. But, if you can’t understand them, then they become a moot point. That’s why the ability to create data visualisations is so paramount. This could be in the form of reports, graphs, or charts. Automation solutions will do this for you!

3. Incomplete Communication

Every department and team typically has their own share of data, goals, and strategy. It’s valuable for everyone to communicate and collaborate to reach shared business goals. With automation software, all data is centralised and pulled together. It also adds visibility within the organisation which promotes increased collaboration.

Final Thoughts

With all the data your business collects on a second-by-second basis, it pays to apply big data analysis methods to be able to use the data to make business decisions.

Rather than having to manage big data manually, which is undoubtedly complex, time-consuming, and error-prone, you can utilise automation solutions to cleanse and transform data for you.

This way, you have visualisations and insights at your fingertips whenever you need a question answered!