What are Data Analysis Methods? Techniques You Must Know

Data Analysis
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There's no doubt that executives understand the importance of data and data analysis methods to secure their success in increasingly competitive markets. In fact, according to Accenture, 79% of executives believe that if they don’t utilise big data, they could lose their competitive edge or even lose their business entirely. 

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Being able to collect and access data is just the first step in the process of gleaning insights and making informed decisions. To use data to your full advantage, you have to utilise analysis methods. While some businesses rely on or data analysts for this work, many have also begun to realise the immense benefits of data automation and analysis tools to assist in this area. 

There are various data analytics methods to incorporate in your data analysis process. Here, we will break down the different types of data analytics methods, as well as how to implement them within your organisation. 

Coming Up

1. What is Data Analysis?

2. Why You Need to Use Data Analysis

3. Data Analytics Methods and Techniques

4. The Data Analysis Process

5. Quantitative vs Qualitative Data

6. Your 10-Step Guide to Implementing Data Analysis Methods

7. The Bottom Line

What is Data Analysis?

Data analysis is the process of cleaning, transforming, and modelling data. This process is performed to gain insights for better decision-making. There are a variety of techniques for data analysis. 

Regardless of which you choose to employ, data analysis serves both the leadership in business as well as its employees (and even its customers, too). 

Why You Need to Use Data Analysis

Methods of data analysis help businesses answer essential questions and, in turn, perform better. From providing financial insight to helping to understand customer behaviour, using data analysis can drive your business forward and help to decrease risk. 

Data analysis helps a business:

  • Predict customer behaviour and realise trends
  • Implement business process improvement
  • Decrease overall costs
  • Lower compliance risk
  • Increase adaptability and flexibility
  • Provide insight behind the financial performance 
  • Make better decisions in less time

Data Analytics Methods and Techniques 

Depending on the technology and your business goals, you can choose from a few different data analysis techniques. 

Here’s a brief overview of each to help you better understand which may be best to use depending on the current questions, data and scenarios you are considering: 

1. Text Analysis: This is also known as data mining. Text analysis uses databases or data mining tools to find patterns within big data sets. This means that that analysis will transform raw data into business insights. With the ability to locate patterns in big sets of data, you can then make better decisions. 

2. Statistical Analysis: With past data displayed in the dashboard, the statistical analysis answers the question of “What happened?” Statistical analysis can be divided into two categories, namely:

3. Descriptive Analysis: Descriptive analysis relies on either complete data or a sample of summarized numerical data to derive insights like the mean and standard deviation. 

4. Inferential Analysis: Inferential analysis is gleaned from using a sample of complete data. In this type of analysis, you can draw different conclusions by interpreting different samples from the same data set.  

5. Diagnostic Analysis: Taking statistical analysis a step further, you can use diagnostic analysis to answer why something happened. Therefore, the name implies what this analysis is used for - diagnosing the cause of an event.

6. Predictive Analysis: Drawing on previous data, the predictive analysis assumes what will happen in the future before it takes place. It provides a reasonable answer to “what is most likely going to happen?” The accuracy of this forecast depends on the quality of the past and current data, or inputs. 

7. Prescriptive Analysis: Also like the name implies, this type of analysis is about prescribing the next steps. It utilises all the previous models of analysis to define the best action to take to resolve a current problem. It is one of the most common forms of analysis that business leaders use today to maintain their competitive edge. 

The Data Analysis Process 

To achieve any of these data analysis techniques, you must complete the data analysis process. The process follows these steps:

  • Data collection
  • Data cleaning
  • Data analysis
  • Data interpretation
  • Data visualisation 

Quantitative vs Qualitative Data

Along with different data analysis techniques, there are different categories of data in the first place. This includes quantitative and qualitative data. 

Quantitative data: This refers to data that is numerical and defined by hard facts. For example, this applies to payroll data, revenue, click-through rates, sales numbers, etc. 

Some ways to perform data analysis quantitatively include:

  • Regression Analysis: Regression analysis helps to predict future trends. It relies on defining the relationship between a dependent and independent variable. In regression, the dependent variable is what you are trying to measure, which is done by using independent variables (the data) to predict the dependent variable. You can use an infinite amount of independent variables, but only perform regression for one dependent variable at a time. 
  • Hypothesis Testing: Hypothesis testing is also called T Testing. You create a hypothesis and then test it by comparing two variables to find their correlation. You can use T Testing to see how an important business decision may affect your business before implementation. For example, you can use T Testing to determine something like how much productivity may decrease if you scale back the work hours for a particular team. 
  • Monte Carlo Simulation: The Monte Carlo simulation is a popular technique used to predict uncertainty and risk. It relies on data to create multiple scenarios using unknown variables to test possible outcomes before they happen. By pulling random numbers from data, a Monte Carlo simulation tries to make the unknown known so that you can see multiple results to understand better how big or small the effect of taking a current risk could be. 

Qualitative Data: This is based on qualities, rather than numerals. Since it’s not cold hard facts, it’s inherently more subjective than quantitative data. For example, this type of data comes from customer satisfaction surveys or employee feedback. 

Since the data is less black and white, the types of analysis are less structured. 

  • Content analysis: When you have a lot of data in the form of content, such as responses to a customer satisfaction survey, you can start to categorise answers by colour coding them. Then, you can see what colours recur the most, which can help you assess what problems to deal with first. 
  • Narrative analysis: Perhaps the most subjective, narrative analysis has no particular structure to it. Instead, it relies on somewhat emotional types of information, like how employees feel about their role or how an organisation manages its processes. The interpretations come from how stories are told and how communication occurs between parties. Narrative analysis is useful for change management and the implementation of new ideas. 

Your 10-Step Guide to Implementing Data Analysis Methods

Given the different types of data analysis techniques, you may be wondering about implementation and next steps. 

Here’s a 10-step guide to what you need to do to perform data analysis optimally:

1. Communicate needs: Data analytics can be used across departments and for various reasons. As such, it’s essential to sit down with internal stakeholders and communicate your goals before implementing the analysis. 

2. Establish your questions: To achieve your goal and find the answers you want, you’ll need first to ask the right questions. Asking the right questions will set up the entire process to get you the correct answers. 

3. Collect and combine your data: Begin collecting data from the most verified sources. In many businesses, data is disparate and sits in different locations. It is a best practice to consider using a tool like SolveXia so that you can pull data from any format and utilise it for your analysis. 

4. Define your KPIs: Defining Key Performance Indicators (KPIs) will ensure that you measure data correctly. KPIs are useful in both types of analysis - quantitative and qualitative because they help you define how you measure success and outcomes. 

5. Delete useless data: With the mass amount of data your business collects, it's imperative to get rid of the data that doesn’t serve your needs. If data doesn’t help you reach your goals or serve your KPIs, then consider getting rid of it. 

6. Perform analysis: Implement a data analytics technique (from the list above) that is fit to answer your questions. 

7. Design a data management plan: The analysis you’ve completed is likely going to need to be used again. Consider devising a data management blueprint or roadmap. This can quickly be done using a tool like SolveXia that is automated. You can define the type of data analysis process you want to run and then save it. Once saved, you can even use the tool to determine the frequency that the model should run and automate the results to be sent to whoever needs to see them. 

8. Integrate technology: Using technology like automation software makes data analytics a seamless process from the get-go. Automation tools like SolveXia offer everything you need to optimise business processes and analyse results successfully. Tools help to store, transform, and share data with those who need it. For example, SolveXia provides you with analytics in real-time via dashboards and reporting so that you can make decisions quickly and share the data with the right people. Businesses experience change immediately, so the ability to utilise a tool to answer questions and predict the unknown offers you an edge over the competition and another way to secure business success. 

9. Answer your questions: Big data and proper analysis provides you with answers to important questions. Internal and external stakeholders, along with customers, benefit from useful data and good data analytics because it allows leaders to make better decisions on the whole. 

10. Visualise your data: Numbers shouldn’t be overwhelming. The ability to visualise data means that everyone within your organisation, even those without a technical background, can see what is going on. Dashboards and data visualisation make this a reality. For example, SolveXia takes data and transforms it into customisable dashboards so that those with access to the system can define the KPIs they are tracking and see them in real-time, any time. 

The Bottom Line 

Data analysis is becoming less of choice and more of a priority for a business and its leaders. The ability to utilise data for decisions stems from the ability to perform proper data analysis. 

Automation tools like SolveXia make this easily achievable and scalable for a business of any size. Imagine having the information at your fingertips to answer questions about the future before they happen. This is the power that data analytics can give you. 


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