Data Analysis Examples & Best Practices

On a daily basis, the world (and all of us in it) creates 2.5 quintillion bytes of data. It’s difficult to grasp how much data that represents, but suffice it to say, it’s a lot. So, when it comes to businesses that undoubtedly collect and use data day-in and day-out, it’s beneficial to look at data analysis examples.

This way, you can see how data tools can help your business draw valuable insights from the mass amount of data being collected, stored, and shared.

Coming Up

1. What is Data Analysis?

2. Why is Data Analysis Important?

3. What are the Types of Data Analysis?

4. What is the Data Analysis Process?

5. What are the Best Data Analysis Tools?

6. What are Data Analysis Examples and Best Practices?

7. Wrap Up

What is Data Analysis?

Data analysis refers to the science that works to extract conclusions and information from a set (or sets) of data. Within the process, data analysis spans cleaning, analysing, and visualising data. In a business setting, the goal of data analysis is to draw knowledge to inform better decision-making abilities.

Given the volume of data floating in and out of businesses, there’s no doubt that it’s becoming unrealistic to perform data analysis manually. While it can be done with the aid of spreadsheets and math, it’s not advantageous. Instead, most companies are utilising data analysis tools to help them manage the sheer amount of data to be transformed for use.

Data analysis is a major part of data management. From adhering to regulation to forecasting the future, data analysis is crucial to businesses of any size.

Why is Data Analysis Important?

Data exists in every aspect of your business. From social media to customer satisfaction surveys, financial reports, and more, data helps you better understand your customers and know what they need (oftentimes, before they even tell you, or know themselves).

Across all data analysis examples, you gain a guide as to where to focus your attention, resources, and time. There are many moving parts in any organisation, so data analysis can empower optimal actions and results.

For example, when it comes to finance, your finance team can leverage data analytics to better understand the business’ risks, profitability, and resource allocation, to name a few.

What are the Types of Data Analysis?

Given that data is available across departments within organisations, there’s no doubt that there’s different processes to perform data analysis. The types of data analysis depend on the input (raw data) and desired output (insights).

1. Text Analysis

As the name implies, text analysis refers to text data. It relies on machine learning and natural language processing (NLP) to extract information from text (without the need for human intervention). For example, text analysis tools can sort human language (in text form) to extract keywords and understand emotion. This is highly useful when it comes to providing customer service.

2. Descriptive Analysis

Descriptive analysis is all about providing a description to answer “What happened” with the use of quantitative data. Commonly, descriptive analysis offers a summary and pattern recognition from data sets. Examples of descriptive analysis include finding the mean, mode, median, range, and frequency from numerical data.

3. Inferential Analysis

Inferential analysis looks to hypothesize “what happened” by comparing sample statistics with the entire population. In practice, inferential analysis often looks like hypothesis tests and estimation theories. It’s also a favorite type of analysis for conducting market research.

4. Diagnostic Analysis

Just like you got a doctor to diagnose why you’re sick, you’d use diagnostic analysis to understand why something happened. It’s also called root cause analysis for this reason. Diagnostic analysis identifies patterns (or deviations) within data to explain why an event took place (or didn’t).

5. Predictive Analysis

Predictive analysis looks ahead in an attempt to predict what is likely to happen. Given the exploratory nature of this type of analysis, it becomes more accurate with access to more data points.

6. Prescriptive Analysis

Prescriptive analysis is at the pinnacle of data analysis, marking the most advanced type. This is because it pulls together all forms of data and analysis in an effort to help you decide what action to take. To do so, it involved modeling multiple scenarios, predicting each outcome, and then choosing the optimal path forward based on the findings.

What is the Data Analysis Process?

With the multiple types of data analysis available, it only makes sense that there’d be a process involved for carrying out data analysis in practice.

Take a look at these data analysis methods. Then, we’ll touch on some data analysis tools that make the process efficient, accurate, and timely.

1. Data Decision

To start, you’ll set your data decision. This refers to what you’re looking to accomplish and gain from conducting data analysis. Your data decision informs the type of data you need to collect to get the answers you seek.

2. Data Collection

Now, it’s time to focus on data collection. Given that there are so many avenues in which you can obtain data, refer back to your data decision to focus data collection in the right places. With data analysis tools, this process is automated as the software (like SolveXia) will connect all your systems through APIs.

3. Data Cleaning

To reap desired results, data has to be clean (or free of noise). For example, this means removing duplicate data records or taking out special characters. For more on everything you need to know about data cleaning, check out this resource.

4. Data Analysis

Only once you have collected and cleaned data can it be used to perform data analysis. Data analysis doesn’t always follow a start and finish flow. It may require iterations. The most optimal way to perform data analysis is with the aid of data analysis tools that will handle the processing for you. Software like SolveXia automatically transforms raw data into useful insights.

5. Data Interpretation

After the analysis has been executed, you get to review the results, which helps you answer your questions from the start. To interpret data in the easiest fashion, make use of data visualisation (keep reading).

6. Data Visualisation

Data visualisation is a visual way to read data. In most instances, this will involve dashboards with charts and graphs. If you deploy SolveXia’s financial automation solution, you gain the ability to customise dashboards as you see fit, so you can always have a finger on the insights you need at a moment’s notice and in real-time.

What are the Best Data Analysis Tools?

Just like there is an abundance of data floating in cyberspace, there are also many big data tools to choose from. To decide the best for your organisation, you’ll need to consider your goals, budget, and technological acumen.

Here’s a quick look at three popular data analysis software options.

1. SolveXia

SolveXia is a human analytical financial automation software that is designed to drive operational excellence and empower people through automation. The no-code (or low code) solution can be used out of the box to offer an array of solutions, including: data preparation, advanced data analysis, reporting, process improvement, advanced analytics and governance.

2. Excel

Excel is one of the most popular software for data analysis that is based on spreadsheets and functions. However, it requires extensive knowledge of how to use it in order to utilise its robotic process automation abilities.

3. Tableau

Tableau is software that’s geared to help people understand their data, with drag-and-drop visualizations and the ability to share it.

What are Data Analysis Examples and Best Practices?

When looking at the various data analysis examples in existence, there are several best practices that come to light. These include:

• Collecting as much data as possible – with the caveat to be focused and specific about the type of data you collect (as in, it should be useful in answering your questions and accomplishing your data analysis goals)
  

• Keep data up-to-date (Big data tools like SolveXia inherently do this by connecting your data sources and updating data records as needed)
  

• Prioritise the safekeeping of your data - to perform data analysis on a regular basis and accurately. Plus, data is regulated in most industries, so you’ll need to keep safety in mind when handling data for compliance purposes.
  

Fortunately, automation tools like SolveXia are built to keep these best practices in mind, so that you can rest assured all your bases are covered while you can apply your energy on what you need to get done within your organisation.

Wrap Up

Data analysis examples and findings help organisations to understand the implicit value that data analysis provides. While you can collect all the data in the world, if you don’t know what to do with it, then you’re likely wasting time, money, and most likely even adding to your organisation’s compliance risk levels.

For all these reasons and more, data analysis tools can help to transform your business into a powerhouse that benefits from the ability to be agile and gain knowledge from raw data. See how a tool like SolveXia can manage your data analysis needs.