Analysis of data: steps for better business decisions in 2022 (2023)

Companies are impressed by the data, which means they are often ignored.

Ok, it can be difficult to classify large amounts of data, especially if you don't have time and resources.

However, with the appropriate tools and processes, you can start putting your data at almost any time and with a minimum manual input.

Acrossdata analysisTechniques, you can get valuable information reporting general decision making and helpingUnderstand the needs of your customers.

Then follow 5 steps in the data analysis process and learn how easy it is to turn your data into significant ideas.

How to analyze data in 5 steps

To improve the way you analyze your data, follow these steps in the data analysis process:

• Step 1: Define your goals
• Step 2: Decide how to measure the objectives
• Step 3: Collect your data
• Step 4: Analyze your data
• Step 5: View and interpret the results

Step 1: Define your goals

Before you jump to yourdata analysisBe sure to define a clear set of objectives.What do you want to get from the data?What is the problem or situation you are trying to solve or understand?Knowing this will help identify which data you will need to collect (and what type of analysis you will need to do).

Design your questions about a specific problem and possible solutions.

For example, if you come a sudden peak on the bassBuckle scoresThat they are related to a question you asked you about customer service, you can ask the following questions to solve the problem.

Objective: Improve customer service

1. Why are customers unhappy with our support team?
2. How can we improve customer service?

Step 2: Decide how to measure the objectives

After setting your goals, you should decide how to measure them.

For example, if you want to measure individual support agent's performance, you can deepen the numerical data to find out how long it takes each agent to respond to a customer.Then measure each agent's performance against the overall average.

However, if you want to know why customers are not satisfied with the level of customer service, you should see yourQualitative data.

Maybe a specific customer service agent is mentioned more often than others, but are they mentioned positively or negatively?You may also want to see specific topics mentioned, for example, response times or if customers were happy with how their problem was solved.

Step 3: Collect your data

Now that you know what your goals are and how you want to measure them, you can start collecting the right data type.If it is the best practice to collect bothquantitative and qualitative dataYou will also need to collect relevant data for the questions you are trying to answer.

• Quantitative data:Structured data that can be quantified and measured.For example, numerical labels and data,

• Qualitative data:Unstructured data that must be structured before extracting them to obtain information.For example, text, speech, images, videos.

Quantitative data is often stored in excel databases or leaves, ready to analyze them.But where can you find qualitative data?

Here are some examples of qualitative data:

• And emails from your customers
• Customer service tickets
• Chat conversations
• Product reviews on -line
• Social network data
• Open Research Responses

Most of these data will be stored in the tools you use daily, such as Zendesk, Gmail and social media management platforms.Something may be able to meet using the useWeb scraping tools(If the data live on websites, blogs or forums).

Note that the success of your data analysis depends largely on the quality of your data, then set aside a time toPrepare your data, eliminating unnecessary noise and characters, HTML elements and punctuation marks, which usually appear inUnmordial text data.

And remember, use relevant data sets.For example, if you want to analyze the feeling of customers throughout customer service, you will need a data set that contains opinions about customer service.

Step 4: Analyze your data

Data Analysis Tools, such as Excel, Google Sheets, and Business Intelligence Tools, such as Tableau and Google Data Studio, are excellent for ranking numbers.They allow you to connect your quantitative data and create complete views, graphics and graphics.

These tools are excellent for starting data analysis, but there are more complex.Data Analysis Methodsthat you can use to deepen further with your analysis.

Choose your data analysis method:

• Inferential Analysis- Analysis of statistical data to discover standards and trends
• Text analysis-The analysis of qualitative real time data
• Diagnosis Analysis- Exploratory quantitative and qualitative data analysis
• Predictive analysis- Analysis of historical quantitative and qualitative data
• Prescriptive analysis-The quantitative and qualitative data analysis based on scenarios

Some of these data analysis methods use andAutomatic learningTo help you automatically analyze large unruly data sets.But don't let it be discouraged.There are many ready -to -use tools that come with previously trained automatic learning models so you can get powerful ideas in just a few minutes without writing a single line of code.

Previous trainingWord Extractor -Chave, for example, you can quickly summarize your data by removing the most frequent words from your data.Feelings AnalysisOn the other hand, it automatically classifies your data as positive, negative and neutral.When combining, you can start to get deep information from your data, such as negative and positive trend problems.

Try this previously trained feelings analysis model to see how easy to extract qualitative data information:

Try this ready -to -use feeling analysis model how easy to extract qualitative data information:

If your data requires a more personalized approach, you can create your own code analysis tools without code, using an intuitive tool asMonocinillo.

After training your model using your data and commercial criteria, you can connect them to the tools you loveAPI MonkeyLearnointegration(Such as Excel, Google Sheets and Zendesk) and starts to get the value of the data in real time.

Step 5: View and interpret the results

Now comes the fun part.Transform your surprising data analysisData viewsusingData display tools, which help summarize your data so that they can easily detect trends, standards and relationships in your data.They are also a great way to support commercial decisions and present their discoveries to the rest of the team.

Data display tools

• MonkeyLearn Studio: Combine data displayed in the application with text analysis tools, creating a powerful data analysis solution, all in one.

Take a look at the board, which shows a feeling around different themes.

(Video) 5 Steps to Data Driven Business Decisions

• Graphic: Visual Analytics software with a drag interface and easy to use.

• Google Data Studio: For a simple data display tool that is free.

Once you see your data, start making decisions that help you achieve your trade goals.

Start with data analysis

Data analysis allows companies to learn what works well for them and what to improve to grow.

Companies have been taking advantage of quantitative data for a long time, but there are many more ideas you can get with qualitative data.If you spend more time preparing this type of data, the steps involved in qualitative data analysis are the same.

In addition, the analysis itself is easier, faster and more accurate, thanks to text analysis tools.By combining quantitative and qualitative data analysis, companies can detect trends and really startListen to the voice of your customers.

Use daily tools such as Excel to analyze your data and understand what is happening.

Then connect your data analysis with AI automatic learning tools, such asMonocinilloto understand why numbers and curves are up or down.

Register for a free demonstrationTo see how easy it is to perform a deep analysis of your data.