It is one thing to collect data, but making it work for you requires a different skill set. Insights can be created by analyzing data and making findings or drawing conclusions from it.
Data alone is not knowledge. Even with access to all the customer data in the world, it is not worth anything if you cannot utilize it in making workable decisions for your business.
That said, with actionable insights, you can make strategic, well-planned decisions.
9 Ways to Turn Data Into Actionable Insights
Here we’ll show you how to extract actionable insights from tons of data, sieve through the not-so-important information, so you can make quicker data-based decisions and achieve maximum productivity.
Below are ways to turn data into actionable insights.
1. Ask The Right Questions
Asking the right questions is like setting the right compass before an expedition. Determine the information you want from your data. or the questions you want your data to answer. Doing this will ensure you don’t spend valuable time on unnecessary questions.
How will the data help improve your productivity? A report that does not answer any questions is just an update dashboard. Actionable insights are alike in that they drive actions, and actions lead to results and promote productivity.
2. Learn To Recognize Patterns
With patterns, you can turn information into knowledge. Pattern recognition can help turn bits and pieces of data into actionable insights. For instance, the data of an online sales company might show that customers who visit the company’s website and request a particular product become repeat customers demanding the same service, while those who are offered a different product do not make any more demands. That’s a pattern that you can use to arrive at actionable insights.
Also, you can spot when there’s an upward trend or a downward trend. To do this, you visualize a connection between two or more occurrences.
Find anything that doesn’t fit? Something that looks out of place? Look into it, and you’ll discover that there’s likely something there. However, it would be best if you recognized that not all patterns have a bearing on your business. So, you must determine the potential utility of each pattern.
Nevertheless, never study patterns out of context, and do not ignore patterns.
3. Determine Your End Goals
Know what you want to achieve before you begin to work at it. You want to turn data into actionable insights, but after that, then what? What kind of action do you want to take?
You’ve most likely considered it from the very beginning, so state what you want to accomplish. Knowing where you’re going with your analysis will help you get there faster.
At the end, write them down. The goals will help you create a winning hypothesis.
4. Create an Excellent Hypothesis
Any Analysis will thrive with a detailed hypothesis. A hypothesis has to do with your theory, gut feeling. A typical hypothesis usually follows the format below.
If you are in sales, for instance, you can begin your hypothesis like so: “I believe we should do more advertising for this product, we can buy Google ads for the product… and if I’m right, we could bump up sales by 15%”.
You may have a hard time formulating a hypothesis, but if you don’t, you may very well spend time looking through truckloads of data without finding any actionable insights.
5. Integrate Data Sources
To reach maximum productivity, you have to collect data from different sources. However, when converting your raw data into actionable insights, you should integrate your data sources.
You get closer to hitting the mark on your analysis when you integrate your sources. Therefore, use all the tools at your disposal to consolidate varying information sources.
Most likely, your data sets will only take into cognizance a section of the population. So, when you connect all your sources, it will enhance your understanding of your business and customers. Such understanding can deliver excellent results.
6. Predict Logically
Take into consideration the reaction of your audience or customer base. Analyze their reaction regarding your business or service capabilities. That should help you predict results moving forward. What does your customer require, what can you deliver?
Prediction should be based on current circumstances and trends, not future or past events.
Once you can make your predictions, you and your team can apply them to make decisions that align with the expected patterns.
7. Simplify findings using context and visualization
The method you present your data is key to turning the bulky data into actionable insights. The use of visuals is very common with data analysis, and it is estimated that about 65% of the population understands visuals more than plain text.
It would help if you also kept in mind that a portion of the population cannot read. Also, data only becomes valuable when it is paired with context.
For context, you need to dissect your data. Although no raw form of data is a bunch of mumble-jumble, you’re most likely not getting all the info if you don’t put it in the right context.
Usually, a superior understanding of context makes for better decisions. The idea is to connect the numbers and letters in the report for better clarity. So, please contextualize. For context, you can make use of the five Ws.
- Who: the client, customer, consumer, audience
- What: the business, service, product, goals, events
- Where: online markets, social media, website
- When: the time frame, schedules, timing
- Why: cause
The kind of Ws or your interpretation of them solely depends on the nature of your enterprise. However, the use of context makes your data resonate, gives it meaning and form.
8. Time-Based Action
Making data-based decisions without taking a critical look at the back story is a recipe for disaster. You should not only look at the data before you but examine the past as well. You should note the historical data or the history of the data.
One must look at the past to make sense of the present. Some past occurrences like sales surges or dips, internet crazes may have been responses to certain events that may have occurred at that time. Some instances include.
- Quick sales in response to a trend
- A lot of clicks on a website because of holiday
- Massive streams in response to a scandal
- Many views on a video in response to a performer’s popularity.
It would be a mistake to think that you can replicate the same results without the same or at least similar circumstances. As such, you should consider this so you can approach your task insightfully.
9. Hire smart people
You may find tools to collect data, but people find the insights. Good, smart people who know their onions as far as the business is concerned. Such people are needed to convert the data into actionable insights.