How to turn data into actionable insights

Data can help you grow by giving you visibility into what’s going on in your business. At its simplest, data can help you make sure your business is on the right track. When analyzed to their fullest potential, every customer interaction, transaction, and digital footprint generates data that can offer valuable insights. These insights can help you streamline your operations, learn more about your customers, develop new products, identify potential risks, spot security breaches — and the list goes on. 

The difference between scaling and stagnating can depend on effectively turning data into actionable insights. Outages, cyberattacks, and even just inefficient operations are all consequences of mismanaged data. Failing to optimize your systems and take action on potential threats can lead to major problems. This is why it’s crucial to have your data and make use of it, too.

So how do you turn that raw data into something meaningful? How do you mine for those critical pieces of information that can drive decision-making? And, once you’ve extracted the insights, how do you effectively communicate them to others?

An actionable insight is more than just an interesting data point or a trend. It’s a piece of information that directly leads to a specific, practical action that can improve your business.

Unlike raw data, which might tell you what happened, actionable insights help you understand why an event happened — and more importantly, what you should do about it. They bridge the gap between data analysis and decision-making, turning numbers into clear steps that your team can implement for targeted results.

For example, maybe your website data shows that users keep abandoning their shopping carts before completing their purchases. This is good information — but what do you do with it? The actionable insight comes when you dig deeper to figure out that many of these users drop off during the payment process. From there, you discover that there might be an issue with your payment gateway or that the checkout process is too complicated. The action? Simplify the payment process by reducing the number of steps in the checkout flow.

By turning data into actionable insights, you get clear, specific steps geared toward a targeted outcome. They’re not speculative suggestions; they’re direct recommendations that can lead to visible, tangible improvements in your business.

Collecting and preparing your data correctly are your first steps to turning data into actionable insights. How you execute these steps will determine the overall outcome. Without a solid foundation, your analysis could lead to misleading conclusions. So, go slow and do it with care.

Start by identifying what types of data your business needs to reach its goals. This might include things like customer behavior, sales figures, website traffic, or social media metrics and interactions. 

When trying to turn data into actionable insights, you’re likely looking at two different kinds of data: discrete data and continuous data.

• Discrete data refers to numbers that represent fixed data values. They’re always positive, whole (no fractions), and countable. Things like the number of employees in a company, the number of a certain product sold, or the number of clicks on a call to action (CTA) are examples of discrete data.

• Continuous data represents measurements that can take on any value within a range — things like time, temperature, sales revenue, or customer spending amounts. It’s helpful when you’re looking for trends over time or across different conditions.

To gather this data, you’ll need tools like CRM systems, social media monitoring tools, or observability solutions to provide a comprehensive view of what’s going on.

Once your data is collected, the next step is to clean and organize it in a way that’s easy to turn into actionable insights. Raw data can come with inconsistencies and errors that throw off your analysis. Data cleaning spots and fixes these issues. This process might mean spending some time on standardizing data formats, fixing inaccurate entries, and removing duplicate records. It’s a key step — you can’t make good decisions based on bad data.

When pulling data in from multiple sources, you’ll need a solution that can correlate data across environments without having to move it from where it currently resides. No matter if they’re PDFs, images, sales figures, or IoT data, you will need a solution that can convert disparate data into a common schema to make it useful. Data integration allows you to connect the dots between different data types to spot patterns and trends that might not be visible if your data was siloed. You can present clean, easy-to-read data more effectively across teams and departments. 

It’s important to consider how you can present the data in a way that inspires stakeholders to act. The goal here is to find insights that align with what you’re all trying to achieve.

Data scientists are trained to present information that is as complete and accurate as possible, but that’s not necessarily a good fit for business settings. Charts that are too complicated or statistics that are too long are, in the end, not helpful. Your stakeholders should walk out of your presentation remembering key facts, a few significant numbers, and some general trends you’ve identified for them.

Follow these best practices for visualizing and presenting data: 

• Keep your visuals simple and only share one takeaway on each chart.

• Keep your numbers simple (21M is better than 21,000,000) so that they’re memorable.

• Choose the appropriate chart — generally speaking, bar charts are good for comparisons and line charts are good for presenting data over time.

• Make sure your data is labeled.

• Know your audience. For example, presenting a topic to your finance team might require a different approach than to your marketing team.

The last step in turning data into actionable insights is analysis and retrieval. This means figuring out what the patterns and trends you’ve spotted mean for your business and how you can use them to make more informed decisions.

For example, say your ecommerce store sells two different brands of canned tomatoes. The marketing team contacts you and asks how tomato brand A is doing because it recently got some online attention when it was featured in a spaghetti recipe by a famous chef. After examining the data, you discover that traffic to brand A’s page has spiked: it has twice the amount of traffic but a lower conversion rate than tomato brand B, which has less traffic but a higher conversion rate. However, your month-over-month data shows that overall sales for brand A are up even though the conversion rate is down. Brand B, which has a lower price, has historically had a higher conversion rate which remains steady month over month. 

What would this data look like if it was turned into actionable insights? 

Simply presenting the conversion rates isn’t the full picture. You want to compare the completed purchases, month-over-month trends, and profit margins of the two brands. You might want to dig deeper and see if people purchasing brand A are new customers or if there are any other items they’re also adding to their carts. (Yes, many are new customers. And, oh look, they’re also buying the same brand of spaghetti the famous chef recommended.) 

Finding insights in the data means you have:

• Identified key findings: You spotted significant patterns and/or anomalies in your data that align with your business objectives. For example, Brand A sales are up without affecting the sales of Brand B, and spaghetti sales are up, too. That’s good!

• Contextualized the insights: You and the marketing team communicated about why this anomaly might be happening in the first place, which allowed for better context.

• Prioritized the findings: Not all insights are equally important. For example, brand B’s consistent sales are good to note but don’t need to be focused on.

• Continued monitoring: These insights aren’t static — they will evolve as new data is continuously generated, which you can derive insights from later. 

This way, your data has given marketing the information they need to take action. They’ll spend the next month promoting brand A and the spaghetti to try to boost sales. Meanwhile, you’ll continue to monitor the data and see if their promotional campaign was helpful while also looking for any new patterns or anomalies along the way.

Turning data into actionable insights doesn’t have to be intimidating. By following a few key steps — gathering and cleaning your data, analyzing and visualizing it, and then highlighting insights — you can enable your colleagues and stakeholders to make informed decisions that help your organization reach its goals.