Data analytics is the process through which you make useful inferences after processing the raw data. In the current technological landscape, data has become an important aspect of all types of businesses. From driving revenue to launching new products, various organizations are leveraging data to create their strategies. Raw data can be used to extract actionable insights that inform leaders to make profitable business decisions and optimize processes. Check out the comprehensive guide we have created below to learn how you can turn raw data into actionable insights and make the most of what data analytics has to offer. 

What is Raw Data?

Before diving deep into the world of data analytics, it is essential to understand what raw data is. It is the very foundation on which everything you will be doing depends. Raw data, as the name suggests, is the data that has not been cleaned or processed. It is the data that you have collected and created an entire set of.

It can be stored in various ways, such as in the form of spreadsheets, images, databases, images, surveys, etc. This data can be from one source or various sources. However, it is also important to note that raw data is not randomly collected data. Skilled professionals only collect and store data that they know will hold some form of value for them and can be used in the future.

This is the reason why it is important to always have a clear goal in mind before you start collecting your data. It is also important to know what kind of data will suit your purpose, whether you need numbers to create a graph or client experiences to create strategies. Raw data is an important part of the initial stages of your operation, and even though it will be processed later, it is essential to always be mindful when collecting it in the first place.

What are Actionable Insights?

Actionable insights simply refer to the information you have gathered from looking at your data. However, that is not all; their importance goes beyond this simple definition. Actionable insights are not just any observations from the data; these are the insights that are valuable and can be used to take certain actions for improvements or changes.

Actionable insights can be of two types. The first is the insights that are collected with the help of machine learning tools, and the other is the insights that professionals gather during their analysis.

Whether you are relying on machine learning tools to process big data or engaging professionals, it is important to be thoroughly engaged in data analysis. There needs to be a deliberate focus on analyzing data to ensure you get what you actually need, i.e. actionable insights. 

Here are a few things that will help you determine whether the data you have processed actually has produced actionable insights or not.

• Check whether your insights are actually based on the data you have collected or not.
• Check if they are relevant to your goals or not. They need to fit the current needs of your organization.
• Check whether these insights are giving you specific information or not. If you are getting generalized statements, there is probably something wrong with your insights.
• Lastly, check for credibility. Check whether your data is from a trusted source or not and whether it has been cleaned properly or not.

Steps to Turn Raw Data Into Clean Data

Your raw data is most useful after it is cleaned. The process of cleaning raw data is important to ensure that it is free of errors and will produce the results you want. Here are the steps to turn your raw data into clean data.

1. Prepare – This is the first step in your data-cleaning process. Check your data for errors or any invalid information. Prepare your data to be in the same format and unify all values so it makes sense. 
2. Translate – Data translation is a process through which the data becomes readable to the machines. This will help the machines process information efficiently. Make sure all your file formats are correct, and double-check the data to ensure its validity.
3. Process – Now that your data is readable, the machine learning algorithms will do their work. These machines are specifically instructed to make the most sense of the data you have provided to them. Any patterns and trends that can be recognized will be highlighted in this process.
4. Visualize – The next step is the organization of the data. While it does not have to look visually appealing, it needs to be presented in a way that allows the users to make the most sense of it. Carefully choose the visualization method that best fits your requirements and situation.
5. Store – The last step in this process is to store your data. Make sure you know the local and global regulations about properly storing and securing the data you will use in your analysis. You can explore cloud storage options. Just make sure whatever you choose is secured and safe. 

From Raw Data to Actionable Insights – A Workflow

We have established that raw data leads to actionable insights, but what is the workflow behind it? There is a whole process that starts even before you start collecting your data. Below, we have provided the steps on how you can turn your raw data into actionable insights.

1. Define Your Goals and Formulate Questions- Before starting any project related to data, have a clear goal in your mind. What are you trying to achieve from this process? What kind of data do you want? What are the insights you would like to have by the end of the process? Formulate as many questions as you want that aim towards your final goal.
2. Collect the Data – After formulating your questions, start collecting data that you think can bring you the right answers. You can find the data online or offline. Leverage as many sources as possible to make sure you have collected quality data that will fetch you relevant conclusions. You can also set up machines to collect the relevant data for you. 
3. Clean and Process the Data – The raw data you have collected will probably be full of errors and irrelevant values that need to be weeded out. Clean your data and process it thoroughly to ensure you finally have a data set that can help you achieve your goals.
4. Do an Exploratory Analysis – This step is known as Exploratory Data Analysis. This is the stage where you will explore your data to understand it. You can employ various techniques to find relevant patterns, relationships, and trends from your data. This information will help you make informed decisions about future analyses.
5. Feature Engineering -This stage involves creating or transforming features from existing datasets. This is done to improve the quality of work that machine learning tools do. You need to apply your creativity and domain knowledge to get the most relevant data for your purposes.
6. Building and Evaluating Models – Once you have a dataset you are satisfied with, it is time to build and train models to do further work. You can use various metrics to assess the effectiveness of your model. Make sure you have a model to help you get the best results.
7. Interpreting Results for Actionable Insights – After everything you have done till now, you will have the desired results. Now, it is time to interpret these results and service actionable insights from them. This will involve communicating what you have found to the stakeholders and leaders so they can make decisions that will have real-world impact.

To Sum Up

It is clear that data is important for businesses. There is little success businesses can derive without investing in data analytics. From collecting raw data to getting actionable insights – the whole process takes considerable effort and time. However, it is an essential process without which no business can survive in today’s world.

Professionals need to turn their raw data into insights that can help businesses make the right decisions. Once you have the raw data, the possibilities of what you can do with it are endless. So, make sure you are processing it in the best way possible to get the actionable insights to drive the business forward.