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Data insights are the understandings we take away from analysing sets of information. Turning data to insights allows businesses to make the best decisions for their company as they move forward.
The relationship between data, analytics and insights
People often get confused when trying to understand the differences between data, analytics and insights. We're going to cover the definitions of each component, outline the differences between them and how they're related to each other.
What is data?
Data is simply the information we collect from users. This can be in the form of activity, behaviour and demographics. As technology has advanced, more access to information has been made available. Read how data can be the lifeblood of your business.
What are analytics?
Analytics is the process of examining data sets to identify relationships and patterns. This is done through the use of different analytics tools, and it allows companies to draw insights, leading them to make data-driven decisions.
What are insights?
Insights are new trends that have been discovered through data and analytics. They provide a deep understanding of a situation and are initially unknown to a company. Companies are looking to find actionable insights that are clear and easy to understand. Explore NashTech's articles around insights.
What are the differences between data, analytics and insights?
In order to turn data to insights, we need to collect relevant data and use suitable analytics tools. There is a step-by-step process that should be followed if you're wanting to find new insights for your business. The first step is to collect the data. You need to ensure that you're targeting the right data for what you're wanting to find out. This stage can take a while to get right, but creating a solid foundation to collect data is the best way to find those game changing insights. Secondly, we want to apply analytics to the data that we've collected. The difference between analytics and data is that analytics is how we make sense of the data, whereas data itself is the actual numerical information. Analytics make sense of what is actually happening in the data. What follows analytics are data insights. Turning data to insights is a rigorous process, but when implemented correctly, you can make some important decisions for your business based off of these data insights. If analytics tell us what is actually happening based on the data, insights tell us the trends found within that information.
Examples of data insights
The important thing to remember is that data insights differ between different industries. Your ability to discover useful insights is dependent upon the data that is available to you, the parameters of that dataset and the problem you are wanting to solve. So what are data insights? Here are some useful examples of data insights that cover a range of industries:- Discovering that there are certain weekends across a year when your product or service has significantly more demand.
- Noticing that a particular product or service has significantly more demand in a specific geographic location.
- Working out that a certain email outreach campaign has the best conversion rate.
What makes data insights actionable?
Now that we've discussed what data insights are, it's useful to know what makes these data insights actionable: Actionable data insights are insights derived from data analysis that have a corresponding action or actions against them. These actions allow a business to integrate the data insights into their working processes, moving forward, in a hope to make the business more efficient. There are a few attributes we can consider when determining whether data insights are actionable or not:- Relevance
- Context
- Specificity
- Clarity
- Alignment
Relevance
When turning data to insights, we often get the insights from a single set of data. If this is the case, we need to consider whether those insights are relevant to the problem that we're trying to solve. In order to discover useful actionable insights, we want to be using data that is as relevant to the problem as possible.
Context
Putting context behind the data you're using will keep you on track when discovering actionable insights. Comparing market baseline data against your own datasets is an important step to take when understanding context.
Specificity
The best actionable insights are specific, as this means that they have been well thought through. When talking about specificity, we want to know what has happened and why it has occurred. Another benefit of specificity is that it can help when sharing data insights with upper level management. The specific nature of your insights can help provide management with clarity for what you're wanting to change. This leads us nicely onto the next point.
Clarity
The fourth stage to finding actionable insight is clarity. The key to clarity, is to be able to communicate the information around your data insights to other key stakeholders in a simple way. This helps reduce any form of scepticism around the insights you're presenting.
Alignment
The final test for an actionable data insight is whether it aligns with your business' goals. Passing the alignment test is a key characteristic of an actionable data insight. Some questions to ask yourself at this stage are:- Do these insights fit well with your Key Performance Indicators (KPIs)?
- Do data insights align with the company's current or future goals?
When a data insight is well-aligned, it makes the process of getting everyone on board much easier.
Implementing actionable data insights
Now that we've taken you through the five-step process for working out whether a data insight is actionable, we're going to take you through how to implement those actionable insights. We're going to base our actionable data insights on the following scenario - A business has discovered that sending out a designated mail out on Wednesdays at midday versus Fridays at 5pm was more effective at achieving conversions. The actions in order to implement this insight could look like this:- Ensure that the individuals responsible for sending the mail out rearrange their weekly priorities to complete the task for the new deadline.
- Make sure any data collection is now collected in good time for the new deadline.
- Follow the data insight collection process to ensure that you can measure success against this new deadline moving forward.
Useful Links
- The case for automating university admissions
- Why emotional intelligence matters in modern workplace
- Technology sales: a field of rapid advances and boundless possibilities
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