How to Analyze Data: A Basic Guide

Effective data analysis is essential for all employees, regardless of department or role. The ability to identify and study hidden trends is a necessary skill for both a marketer analyzing the ROI of advertising campaigns and a product manager reviewing product usage data.

Unfortunately, many companies struggle with collecting, processing, and analyzing data. A global survey by Splunk found that 55% of all data collected by companies remains unprocessed and unused. Sometimes, the company doesn’t even know it’s being collected; in other cases, employees simply don’t know how to analyze the data.

76% of executives believe that training employees to analyze and process different types of data will help solve the problem and the company will be able to use information effectively.

Fortunately, data analysis is a skill that buy telemarketing data can be learned. You don’t need a degree in statistics or hours of studying modules to understand how to analyze data. Instead, we’ve put together this guide to help you understand how to analyze data—cleaning your data, choosing the right analysis tools, and analyzing patterns and trends. You’ll gain valuable, actionable insights that will help you draw accurate conclusions.

Set goals

Set specific goals before you even begin analyzing your data. If you don’t have a clear idea of what you’re looking for, you’ll spend hours just staring at a spreadsheet or sifting through countless support tickets waiting for a moment of insight.

Your goals depend on the team you’re on, the the beginning of a success story data you collect, and your role:

The finance team analyzes expenses and looks for opportunities to save money.
The marketing team monitors potential customer activity and looks for ways to increase conversion through a free trial of the product.
The engineering team needs to understand how many customers were affected by a recent system outage, so they look at product usage data.
The product development team must prioritize the development of new features and bug fixes, so it analyzes the latest support requests and prioritizes the most important ones.

Your goals will influence what data you taiwan data collect, what analysis tools you use, and what useful information you get as a result.

Clear data and remove unnecessary things

Your data analysis is only as good as the data you collect. If the information you collect is fragmented, inaccurate, or inconsistent, your conclusions will be incomplete or misleading. So after collecting your data, be sure to clean it up and make sure it is consistent and free of duplicate information.

If you’re working with a small amount of data, you may find it easier to manually clean it up in a spreadsheet. Here are some simple things you can do to clean up your data :

Add header rows to your spreadsheet to make the information easier to understand.
Remove duplicate rows or columns if you have multiple copies of the same record.
If you exported data, remove rows or columns that you don’t want to use. For example, many tools add an “ID” column or timestamps that you don’t need.
Standardize data so that numeric values such as numbers, dates, or currency are expressed in a consistent format.

If you work with a large amount of data, it is much more difficult to clean it manually. To speed up the process, use data cleaning tools such as OpenRefine or Talend . They quickly remove confusing, inconsistent information and the data after processing is ready for the next stage of analysis.

Implement a data management strategy to establish clear principles for organizing your data and reduce the amount of time spent on cleaning processes in the future. Here are some data management best practices:

Develop a standard procedure for when and how to collect data.
Adopt a standardized naming convention to reduce inconsistencies.
If you have automated data collection, be wary of any incorrect data. If you receive an error message, review your settings to determine the cause.
Edit and update data collected in the past to meet your new quality standards.

Cleaning and standardizing data is an important preparatory step for analysis. This step reduces the likelihood of drawing incorrect conclusions from inconsistent data and increases the likelihood of obtaining valuable and useful information.

Build Your Own Data Analysis Toolkit

Many companies rely on Excel or other spreadsheet tools to store and analyze data. But there are many other services that can help you analyze data. The tool you choose depends on two things:

Type of data. Quantitative data is often in numeric form, which is ideal for presentation in spreadsheets and visualization tools. But qualitative data, such as questionnaire responses, survey responses, support requests, or social media posts, is unstructured, making it difficult to extract useful information into a spreadsheet file. To effectively analyze qualitative data, you need to structure it.
Volume of data. If you analyze a small amount of data once a week or a month, you can do it manually. But the more data you process, the more likely it is that you will need to invest in tools to automate the process. Specialized services will reduce the likelihood of human error and speed up the analysis process.

Here are a few data analysis tools that would be a useful addition to your toolbox. Of course, you won’t use them all at once, as each one is suited to a specific type of data.

Spreadsheets such as Excel or Google Sheets are traditional data science tools. They are great for analyzing small to medium amounts of data and do not require deep technical knowledge.
Business intelligence (BI) tools are used by companies that collect and analyze large amounts of data.
Predictive analytics tools use machine learning algorithms and historical company data to predict how changes in work processes will impact future results.
Data modeling tools show the structure and nature of information flows and their relationship to various business systems. Companies use this type of tool to see which departments store what data and how that data interacts.
Department-specific analytics tools are used by teams to analyze data specific to their job functions. For example, an HR department tracks a lot of people data, such as payroll, performance, and assignment data, so ChartHop is a good choice for HR analytics . It will be easier to use than a spreadsheet.
Data visualization tools present information in the form of charts, graphs, and other graphical images, making it easier to spot trends.

Choose the tools that will help you quickly analyze your data and extract hard-to-reach information.

Look for patterns and trends

Your data is clean and you have a variety of tools at your disposal – start the data analysis process.

The first step is to identify trends. If most of your data is in numerical format, it’s relatively easy to show patterns in charts or other visualizations. But if you have unstructured data, such as emails or support tickets, you may need a different approach. Here are some data analysis techniques to try in this case:

Text analytics uses machine learning to extract information from unstructured text data, such as emails, social media posts, support requests, and product reviews. This method discovers and interprets patterns in unstructured data. Examples of text analytics tools: Thematic , Re:infer
Sentiment analysis uses machine learning and natural language processing to detect positive or negative sentiment in unstructured text data. Companies often use this analysis to assess brand perception in social media posts, product reviews, and customer service requests. Examples of sentiment analysis tools : IBM Watson , MonkeyLearn .
Thematic analysis uses natural language processing to assign predefined tags to text data. This is useful for organizing and structuring text data. For example, you can use thematic analysis to classify customer support reviews to understand which areas are causing the most problems for customers. Examples of thematic analysis tools: Datumbox , MonkeyLearn .
Group analysis involves examining data in groups of similar customers over a specific time frame. You can track changes in product usage by customers who signed up for notifications during the same time period. Examples of group analysis tools : spreadsheets, Looker

When looking for patterns, don’t assume that the relationship between two things is always cause and effect. For example, if you see a significant increase in social media followers and a parallel increase in email signups, you might assume that all your new customers are coming from social media. But if you track the data in Google Analytics, you’ll see that very few people are visiting your site from social media, let alone signing up.

Assuming that when two things are related, one of them is the cause is called false causation, and it is one of the most common mistakes in data analysis. Often, there is another factor that is causing the trend you are finding. So take the time to gather enough information to make sure your conclusions are accurate.

Compare current data with the previous period

If you have trouble spotting trends and patterns, it may be because you are looking at the data in isolation. You are failing to spot trends because everything you see is just a part of something bigger. You are missing the connection to the previous period’s data.

To find this connection, compare your current data to historical data. If that’s not possible—for example, you’re looking at usage data for a brand-new product feature, or this is your first analysis—then look at your industry’s benchmarks.

A Google search for “[department] performance statistics” or “[department] [industry] statistics” will provide useful benchmarks for different companies, departments, and industries. Journals on the subject, as well as research presented at conferences, are also good sources of benchmark data.