One of the best and easiest ways to stay ahead of your competition in business is to use available data to come up with new ways of improving business processes.
This involves an in-depth analysis of raw data to generate actionable insights for business. But how do you analyze data to know what actions need to be taken to improve your business? Read on to learn the best processes of data analysis and the importance of data-driven analytics.
What Is Data Analysis?
Data analysis is the practice of processing raw data to obtain actionable insights that can be implemented in different areas of your business for greater return on investment. This process reduces the risks involved in making serious business decisions by giving you reliable insights and data. The insights are mainly delivered in charts, tables, graphs, images, and other informative illustrations.
Every day, your business generates large amounts of data that can help you make informed decisions, but the raw data is unusable as is because it can contain inaccuracies and fluff that need to be tidied. Therefore, data analysis is an important process that every entrepreneur must carry out regularly to ensure that the decisions they make are informed by actionable findings. The next step is to understand how to get actionable insights from data.
How to Analyze Data for Actionable Insights
Data analysis involves several key steps. Understanding the important stages in data analysis is necessary because each stage is vital. The quality of actionable insights you draw from the analysis depends on your ability to follow each step correctly. Here are the main steps in data analysis:
Defining the Need for Data Analysis
Before you determine how you’ll acquire insights through data analysis, you must define why you need data analysis. In most cases, data analysis is a business issue or question that requires a well-informed solution. For example, you may need to do data analysis to reduce production costs without lowering the quality of your goods. You can also do it when you want to increase sales or improve the customer experience.
Before you define the purpose, think about the metrics you’ll need to monitor as you continue and identify the sources of your data before you start collecting it for analysis. Choose reliable sources for accuracy.
As noted above, the nature of your sources determines the depth and accuracy of your data analysis. There are two types of sources of data: primary and secondary sources. You start by collecting data from your primary sources (internal sources)–this is structured data obtained from your ERP systems, marketing automation programs, CRM programs, etc. It contains details about your clients, finances, sales, and so on.
Once you’ve exhausted your primary sources, go to secondary sources (external sources), which offer both structured and unstructured data. This data can come from various places, including review sites, social media APIs, etc.
Clean Your Data
Before you start analyzing the raw data, it’s important to clean and sort through it. This is a critical step because you’ll have loads of unnecessary data. For instance, you have to identify duplicate, erroneous, and inconsistent data and remove it from your system. If left untouched, this will skew your analysis to give you inaccurate insights. Fortunately, you can incorporate cleaning tools to help accelerate the process.
This stage involves analysis and manipulation of the cleaned data, and there are several ways to do the analysis. The first one is data mining, which includes simple techniques like clustering analysis, association rule mining, anomaly detection, and more. These techniques help uncover hidden patterns.
You can also use business intelligence and data visualization programs like Synder to analyze large amounts of data and generate actionable insights in a short time. Lastly, you can use predictive data analytics to predict what will happen in the future.
Interpreting the Findings
The last stage is the interpretation of findings to gain actual value. This step validates the reasons why you did the analysis and should involve data analysts and users.