Data can be confusing if you do not understand how to use it. After collecting the needed data, you should have other data supporting your collected data. Also, you should have another data set that supports the accuracy of your data. However, this does not change the fact that all the data you have is raw in nature.
When doing eCommerce analytics, you need to convert raw data into actionable insights your readers can understand and enjoy reading. Since companies generate huge amounts of data from their customers, converting this data into insights is not a walk in the park. Turning data into insights is a detailed process that needs you to have a keen eye for details to get the work done.
This guide offers you a simple strategy that you can apply to convert data streams into actionable insights that you can utilize during decision-making. Let’s get deep into the details and learn more about it.
What is the Difference Between Data and Insights
If you are a data analyst, you may realize that data and insights are almost two similar things. But if you are not used to viewing the world in terms of numbers, you will find it difficult to convert data into insights. The big question is how you convert raw data into insights. Let’s begin by defining these facts before getting deep into the details.
Data refers to tiny bits of measurement, while insights are used to define and interpret what all these measures explain. Actionable Insights such as charts and visuals like radar chart, and comparison charts provide detailed information that helps business stakeholders make decisions based on practical information they collect from data. A real-life example of data and insights is when you measure your toddler’s growth. The measurements you get are what you refer to as data.
After collecting the measurement, you can then compare the current data with the past to find out the difference in growth. This is now the information you get from the collected data. Depending on every inch that your child grows, they outgrow their clothes and shoes. This means that you will have to replace their wardrobe after a certain duration. This is what we call actionable insights.
Turning Data into Insights
It’s evident that every business has a definitive scope of responsibilities. Business intelligence and analytics teams have taken charge of collecting data from different corners of a business setting. After collecting the data, they present the feedback using a survey tool to marketers and other company stakeholders who aim to make decisions and curate strategies that impact the company’s growth.
On most occasions, decision-makers tend to find themselves lost in the available pool of data since they fail to find the connection between the data and their business reality. You may have the data at your disposal, but the insights from the data are still missing. This will automatically cause a gap in your decision flow. When this aspect is not considered, it can heavily impact business growth.
Given that the amount of data available is steadily escalating, the gap is expected to grow wider every now and then. However, this does not exist in digitally mature companies, where different teams of task forces operate in different departments. Converting data into insights is not fine blindly as you think, it’s a matter that goes step by step. Let’s check them out!
Basic Principles to Consider When Extracting Insights from Data
● Collaboration
Company teams need to combine their efforts and assume mutual responsibility when they want to extract actionable insights from data. Proper communication and mutual support can significantly yield more fruits than confrontation and demand. The most important thing you need to ensure is that there is collaboration among your team members to make your work easier.
All the team members should work hand in hand to achieve a uniform goal. Mutual understanding is the cornerstone of all these aspects and can make your work easy.
● Transparency
Data analysts have a clear understanding of the source of the data, the processes, and the types of data metrics that need to be considered. On the flip side, the company management understands their respective goals and the question they are trying to answer. Given that everybody knows where to introduce their expertise, there is a need for transparency and communication.
Communication is the pillar that holds the business stakeholders and the data analysts together. Every group should understand their respective responsibilities and spell out what every team needs to achieve.
● Specificity
Business units are responsible for understanding all the key drivers of revenue, risks and expenses within the specific business field. When identifying the representative data sets, it’s vital to ensure that all parties participate in the process by defining their requirements, goals, and intent. Specificity is necessary to ensure that the data analysts understand the correct metrics to monitor and focus on things that matter.
Applying the Basic Principles
● Define the Specific Question
When you become vague in terms of answering questions, you are likely to cause chaos. The first step is to answer the primary question or the questions presented to you by the business stakeholders. The mode in which you answer the question can easily tell the way forward on how you are going to analyze the data.
● Determine the Significance, Context, and the Business Impact
By understanding the context of the data analysis, motivations, and restrictions, you will easily identify the best metrics to monitor and the best way to do it. Once you have uncovered this information, you will need to create a robust connection between the metrics and what the data intends to represent.
● Set Clear Goals and Expectations About the Final Outcomes
At the beginning of the process, you need to start by setting up the insights that can be gained from the data that you will provide. For instance, you need to specify whether you will present a whole number, an average number, or change the rate. However, this is only possible once you have learned more about the data context and its significance.
● Set Measurable KPIs
When evaluating the data questions you have presented, you nerf to ensure they are closely attached to measurable metrics. To make your work easier, you can choose to use the SMART structure, which represents specific, measurable, attainable, relevant and time-based.
● Generate a Hypothesis for Maximum Clarity
Defining your hypothesis can help you achieve all the points you have with ease. Also, this is the simplest approach that you can use to define all the aspects that are presented to you by the company stakeholders.
● Collect the Right Data in the Right Way
You need to choose the metrics that are capable of displaying your desired information. At some point, you may have to correlate several measures and craft a detailed plan to determine how to arrive at the results. As a result, this will enable you to reach your desired answer.
● Utilize Segmentation
Segmenting your data can help you be more specific and focus on what matters. Also, it can help you to gain a more granular view of your data. You can choose to channel all your focus on a particular subset of data, such as the industry segment, website segment, or market audience. This will give you room to dive deeper into the data behavior.
Conclusion
Converting data into actionable insights is a detailed process that you need to learn and master. Raw data is meaningless and cannot help you make any change within your business. You need to understand how best to turn around your data values and extract actionable insights that you can use during decision-making. However, extracting insights begins when you know the business goals.
In addition, ensure that there is superb collaboration across all the departments within the business. Creating a favorable environment for your customers and team members starts by understanding your goals in business. This will help you reflect on the kind of metrics that you need to collect and the kind of data that is suitable to get the job done.
