Data Driven insights are critical for any business looking to make decisions based on information. After all, data doesn’t mean anything if you can’t glean actionable insights from it. But what exactly are actionable data insights? And how do you go about extracting them?

It is no secret that data is becoming increasingly important in today’s business landscape. But what many people don’t realize is that extracting actionable insights from this data can be the difference between success and failure.

If you’re not sure where to start, don’t worry. In this article, we’ll cover everything you need to know about extracting actionable insights from data. We’ll look at why these insights are so important, how you can observe and make sense of your data, and some tips on how to turn these insights into actionable steps for your business.

Let’s dive in!

What are Data Driven Insights?

Data Driven insights are information that can be used to make decisions, take immediate actions, or improve future performance. This type of data is specific and measurable, and it provides a clear path for how you can use it to improve your business.

What is the difference between Data, Analytics, and Insights?

Data is the foundation for analytics and insights. It is the information that you collect about your customers, products, and business. This data can be collected from a variety of sources, including website activity, surveys, customer interactions, and sales data.


Analytics is the process of turning data into insights. This usually involves using statistical methods to identify trends and patterns in the data. Analytics can be used to answer questions like “how many” or “how often,” and it can help you understand what your data means.


Insights are the conclusions you draw from your analytics. They are actionable pieces of information that you can use to make decisions or take immediate actions. For example, an insight might tell you that your website’s bounce rate is too high, or that a certain type of customer is more likely to churn.

Let’s consider an Instagram case for instance:

You log in to your account and allow it to access your location and other personal information. You also agree to receive push notifications each time you get a new follower or likes on your posts. Whenever you use Instagram, it sends this unstructured data to its servers in the form of numbers and text.

Instagram then uses data insights tools to break down this information into smaller groups. This helps them understand how and why you behave the way you do. They can tell from the data that you’re interested in fashion, travel, or food based on the posts you like and comment on.

Now that Instagram understands you better, they can send personalized recommendations to you and other users who are similar to you. The platform can now show you targeted ads for new clothes, travel deals, or restaurants in your area.

Actionable insights are what you learn from the data you collect, and how you can use it to improve an objective. In this case, the actionable insight is that Instagram can show targeted ads to users based on their interests. This insight can be used to improve the effectiveness of Instagram’s advertising campaigns.

Why are Actionable Data Driven Insights important?

Data insights are important because they help you understand what’s happening in your business and how you can improve. Every business, no matter the size, has data that can be analyzed to help them make decisions.

For example, if you’re a small business owner, you might want to track: 

  • The number of new customers each month 
  • How much each customer spends on average 
  • What products or services are most popular 
  • What channels generate the most leads or sales 

By tracking this data, you can see patterns and trends over time that can help you make decisions about where to allocate your resources.

For example, if you notice that you’re getting a lot of new customers from your Facebook ads, you might want to invest more money in that channel.

Or, if you notice that most of your customers are only buying one product, you might want to focus on cross-selling or upselling them on other products.

If you’re a marketing manager at a larger company, you might track: 

  • Leads by source
  • Conversion rates by channel
  • Website traffic by device
  • Cart abandonment rates
  • Customer lifetime value

Again, by tracking this data, you can see patterns and trends that can help you make decisions about where to allocate your resources and how to improve your marketing campaigns.

Why do organizations struggle with gaining data insights?

There are a few reasons why organizations struggle with data insights: 


  1. They don’t know what data to collect or how to collect it: Meaningful data insights require accurate and well-organized data. If you don’t know what data to collect or how to collect it, you won’t be able to draw accurate conclusions from your analysis.
  2. They don’t have the right tools to analyze their data: Data insights require the use of specialized tools, like data visualization software, for instance, to see patterns and trends. If you don’t have the right tools, you won’t be able to effectively analyze your data.
  3. They don’t have enough resources to dedicate to data analytics: Data analytics is a resource-intensive process. If you don’t have enough people or money to dedicate to it, you won’t be able to glean insights from your data.
  4. They don’t know how to use data insights to improve their business: Even if you’re able to collect and analyze your data, you won’t be able to improve your business unless you know how to use the insights you gain from the data.

How to Observe and Make Sense of Data to Gain actionable insights

There are a few steps you can take to make sure you’re collecting and observing data that will give you actionable insights. 

Consider Data Streaming

Data streaming is a process where data is continuously captured, transformed, and delivered to provide real-time insights.

In order to convert the data into a stream, it must first be collected. This can be done manually or through automation. Once the data is collected, it must be cleansed and transformed into a format that can be used for analysis.

Transform and enrich the data

After we have the data, we might want to do some work on it before sending it further.

We can:

  1. Add metadata to the data. For example, we can add contextual information, such as the location of the plant or equipment, or information about the process or product.
  2. Aggregate the data. For example, we can summarize data by hour, day, shift, or week.
  3. Filter the data. For example, we can remove outliers or data that is not relevant to our analysis.
  4. Enrich the data with reference data. For example, we might want to look up the product name based on a product ID, or we might want to convert raw data values into engineering units.

There are many ways to transform and enrich data. The goal is to make the data more meaningful and easier to work with downstream.

Post the data to an endpoint that can ingest streaming data

Once the data is in the desired format, it can be posted to an endpoint that can ingest streaming data. The most common endpoint is a message queue.

There are many different message queues available, such as Apache Kafka, Amazon Kinesis, Azure Event Hubs, and Google Cloud Pub/Sub.

Once the data is in the message queue, it can be consumed by any number of downstream applications or processes.

These applications can perform further processing on the data, such as storing the data in a database, running analytics on the data, or triggering an alert based on certain conditions.

The goal is to have the data flow through a series of steps, each of which adds value until the data reaches its final destination.

This process is sometimes referred to as an ETL (extract, transform, load) pipeline.

Extracting Insights from data

The most important part of processing data is extracting insights from it. Insights are the pieces of information that we can use to make decisions. For example, we might want to know:

  • What is the trend in the data over time?
  • Are there any unusual values in the data?
  • Is the process operating within acceptable limits?

Answering these questions—and many others like them—is how we turn data into value.

To get insights from data, we need to:

  1. Identify the question we want to answer
  2. Find the data that is relevant to that question
  3. Process the data to extract the information we need
  4. Visualize the data to make the information easy to understand
  5. Interpret the data to find the answer to our question

There are many different ways to find patterns in data. The most common approach is to use statistical methods, such as regression analysis.


Actionable Data Insights with Kiimkern

Without the right tools and processes in place, extracting actionable data insights can be a challenge. Kiimkern is purpose-built to help you overcome these challenges and get the most out of your data. 

Kiimkern ingests data from any source, cleanses and enriches the data, and stores it in a centralized data warehouse.

It also provides a web-based interface that makes it easy to visualize and interpret data. With Kiimkern, you can quickly identify trends, outliers, and other patterns of interest.

If you’re looking for a better way to get insights from your data, try Kiimkern today.

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Actionable Data Insights with Kiimkern

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