We know that data is important–after all, it’s how we make the best possible data-backed decisions. And when it comes to the process of turning the raw data you’ve collected into actionable insights, you may feel a bit stuck. How can you ensure you get all value out of an incredibly large data set? What’s important and what’s not? Is there a way to double check your analysis to make sure it’s correct?
These are all incredibly legitimate questions and concerns that all data professionals have at all points in their careers. And as the world of data becomes more complex and more and more companies are collecting vast amounts of data, there’s a growing need for employees who know exactly how to handle this data.
Data analysis transforms raw data into actionable insights that drive business decisions. For instance, in the healthcare industry, data analysis can help predict patient admission rates, optimize staffing, and improve patient care outcomes. Leading companies like Amazon use data analysis to enhance customer experience by recommending products based on purchase history and browsing patterns.
According to a report by McKinsey, companies that harness big data can increase their operating margins by up to 60%. Additionally, Gartner predicts that by 2025, 75% of businesses will move from piloting to operationalizing AI and data-driven initiatives.
Before we dive right into these questions and help you understand exactly how to make the most of data for your business, let’s explore the importance of data and what actionable insights really are.
Key Steps in Data Analysis
If you’re here, you’re probably very aware of the importance of data. But just in case you’re curious or want to explore reasons that you maybe haven’t thought of before, let’s explore why data is so important.
Data helps you make better decisions: data is synonymous with knowledge and when you use data to back your decisions, you avoid assumptions, mistakes, and bias, helping your decisions be better overall.
Data helps you avoid problems later on: if you’re constantly collecting data, you’ll be able to monitor how things are working and solve issues on the fly while they’re still minor instead of waiting for them to become major.
Data helps back you up: if you want to propose a change or adjustment to your boss, you’ll have to explain why. And there’s no better way to prove a point than with numbers and data that clearly back your ideas up.
Data helps you achieve your goals: the best designed strategies are ones that have data behind them to properly evaluate the success of the strategy; by using data, you’ll be able to plainly and clearly see what’s working, what needs tweaking, and what isn’t working at all.
Data allows you to be strategic: with clear answers as to what’s working, where your money is going, and what clients are liking, you’ll be able to be more strategic with your planning and decision-making, saving time and resources across the board.
What is raw data?
Understanding the process of turning raw data into actionable insights is only possible if you fully comprehend the concept of raw data and what it is. So, let’s dive right in: raw data is the data you’ve collected before you start cleaning, analyzing, or organizing it. It refers to the entire set of data, regardless of if it’s been collected from various sources, and can take practically any form: databases, spreadsheets, images, videos, survey results, and more.
Although it may seem like any data could prove to be useful in some capacity, raw data isn’t a random compilation of information. On the contrary, skilled data professionals know how to collect raw data that will be useful later on. When beginning your data collection process, make sure you follow these three steps:
Clearly define your ideal outcome: what are you looking to achieve with your data analysis? Knowing what your goals are will help you organize your data collection process properly and ensure your data set is actually useful.
Choose your data: If you’re looking for business statistics, you’ll probably need to turn to financial reports or market research for valuable data. On the other hand, if you’re looking to improve the overall client experience, customer surveys may be your best bet.
Collect your data: with your goals and methods determined, get started collecting your data. And don’t be afraid: you’ll probably end up with loads of data, but you’ll be able to sift through the important points later on in the data analysis process.
Photo by Chris Liverani on Unsplash
Turning raw data into clean data
To make your data set useful, you’ll need to follow a few steps to make sure there are no errors or issues that will make your outcomes incorrect. To process your raw data, you’ll:
Prepare the data: during this initial step, you’ll check for any errors or invalid values and ensure that all data is in the same format (if you’ve collected data from various sources, it may take a bit to unify all values).
Translate the data: no, we don’t mean to translate the data into a different language! Here, data translation means making sure it’s readable for a machine to process it. If your data was collected online, you’ll have an easier time than if it’s manually collected, but double check your file format is correct before diving right in.
Process the data: the data will go through various machine learning algorithms that are specifically instructed on how to make the most from the data. Here, patterns, trends, relationships, and problem areas will be highlighted.
Visualize the data: you’ll be able to organize and display your clean, understandable data set in a variety of formats. Think about what you’re trying to portray and make sure you pick the visualization method that is right for your exact situation.
Store the data: last but definitely not least, you need to respect local and international privacy regulations, properly storing and securing the data you used in your analysis. Ensure your company’s storage policies are in line with industry standards and don’t be afraid to explore cloud storage options.
What are Actionable Insights?
Insights are information you’d get from looking at data, right? The answer is yes: that’s exactly what insights are. But the truly valuable insights and information collected from data are referred to as actionable insights, highlighting the ability to take action from that information and make improvements. Generally speaking, there are two types of actionable insights:
Actionable insights gathered by data professionals during their data analysis process
Actionable insights drawn from big data by machine learning tools
No matter which kind of actionable insight you’ve created, there’s an important step required to make that insight truly valuable; it isn’t enough to just have data in front of you. You must engage in thoughtful and deliberate data analysis to find these actionable insights. To be actionable, insights must be the following:
Based on data: this is probably quite self-explanatory, but it’s important to highlight that actionable insights must, with no exceptions, be data-based. You can obtain this information from surveys, market research, or reports, but there must be facts behind your actionable insights.
Relevant: actionable insights aren’t valuable if they’re outdated or address an issue that can’t be dealt with just that; your actionable insights need to relate directly to current issues that your company is facing.
Specific: general statements won’t be very helpful here; it’s important that your insights are incredibly specific and focused on an issue itself to help you solve it.
Credible: without a trustworthy source, your data won’t be credible and is therefore rendered useless. Make sure your data is clean, properly analyzed, and taken from quality sources.
Photo by Firmbee.com on Unsplash
Why are actionable insights so important?
Now that you understand what makes insights actionable and why they’re so important, it’s time to dive into the actual process of drawing actionable insights from your data. And how can you do that? Let’s explore:
Actionable insights allow you to better understand your clients: at the end of the day, you’re trying to sell a product or service to a customer and as competition grows practically daily, unhappy customers will simply turn to the next option. With actionable insights as to why clients are leaving your company and looking to fill their needs elsewhere, you’ll be able to address those specific problems and hopefully convince more clients to remain loyal in the future.
Actionable insights help you stay ahead of the competition: we mentioned competition is popping up left right and center and it’s true–that’s why you need to be on top of what your competitors are doing and make sure you’re offering comparable services or better alternatives to help foster customer loyalty.
Actionable insights help you grow: if you don’t know where your problem areas are or what actions you’re taking that are working well, how will you improve your business strategy? There are so many different moving parts in a company that it can be almost impossible to truly know what is working well and what needs to be adjusted. With actionable insights, you’ll have that answer clearly defined.
Source: Towards Data Science
Data drives business and there’s no arguing with that. In fact, we’d go so far as to say that it’s the driving force behind the vast majority of companies and that’s why we’re focused on creating the next generation of data professionals that are ready to take on the challenges posed by collecting and analyzing large amounts of data.
Steps to Perform Data Analysis
Collect Data: Identify and gather data from reliable sources.
Clean Data: Remove duplicates, handle missing values, and correct errors.
Analyze Data: Use statistical and machine learning methods to uncover patterns.
Visualize Data: Create charts and graphs to present findings clearly.
Generate Insights: Interpret the results and make data-driven recommendations.
At Ironhack, we work with industry experts to ensure our Data Analytics Bootcamp students are equipped with the knowledge and skills necessary to be an asset to the data world.
The future of data is vast and there’s a spot waiting just for you–are you ready to take on the challenge? We’ll see you in class.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
