THE FIVE STAGES OF DATA ANALYSIS

By: Lauren Erdelyi

The power of data is perhaps best captured in Moneyball. The book and subsequent film tells the story of Billy Beane, the legendary general manager of the Oakland Athletics who used statistical analysis to reinvent the game of baseball. With one of the league’s smallest budgets, Beane relied on data to predict how many runs a player would score and then built a roster of undervalued—but talented—players to compete against rivals with deeper pockets.

Spoiler alert: it worked. A little while after they adopted this approach, the Oakland A’s started to win big. They even became the first team in over 100 years of American League baseball to win 20 consecutive games.

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Fast forward several years, and you’d be hard-pressed to find an industry that isn’t applying Moneyball-like strategies to make smarter decisions. Telecommunications companies are tracking calls to refine their customers’ experience. Health care experts are using it to develop a deeper understanding of patients and ultimately improve outcomes. Media service providers are turning to data not only personalize content but also produce entirely new shows for viewers. The cases of industries using data are virtually limitless.

While organizations have more data than ever, people who have the skills to put it to good use are rare. The outcomes? Lost revenue, dissatisfied customers, disengaged employees—to name a few.

The good news is that there’s a straightforward five-step process that can be followed to extract insights from data, identify new opportunities, and drive growth. And better yet, the ability to do so isn’t limited to data scientists or math geniuses. People across all disciplines and at all stages of their careers can develop the skills to analyze data. It’s useful whether one is looking to upskill in a career or move into an entirely new industry.

Here, we’ll walk you through the five steps of analyzing data.

Step One: Ask The Right Questions

So you’re ready to get started. With no time to waste in discovering what makes your customers or employees tick, you quickly set out to collect as much data as you can get your hands on by digging through records and surveys. The more the better, right?

….Not so fast.

Before you start collecting data, you need to first understand what you want to do with it. Take some time to think about a specific business problem you want to address or consider a hypothesis that could be solved with data. From there, you’ll create a set of measurable, clear, and concise questions that will help answer that.

For example, an advertiser who wants to boost their client’s sales may ask if customers are likely to purchase from them after seeing an ad. Or an HR director who wants to reduce turnover might want to know why their top employees are leaving their company.

Starting with a clear objective is an essential step in the data analysis process. By recognizing the business problem that you want to solve and setting well-defined goals, it’ll be way easier to decide on the data you need.

Step Two: Data Collection

This brings us to the next step: data collection. Now that you have a solid idea of your questions, it’s time to define what data you need to find those answers. As a starting point, you’ll want to determine if the data is readily available within your organization—like within employee survey results or annual performance reviews in the HR case.

Then, ask yourself: do you have all the data you need or will you also need to externally source it? If it’s the latter, you may decide to run an experiment or conduct another survey. Whatever you choose, the end goal of this step is to make sure to have a complete, 360-degree view of the problem you want to solve.

Step Three: Data Cleaning

You’ve collected and combined data from multiple sources. Great. But it’s not yet time to roll up your sleeves and dive into it. That’s because raw data is seldom usable in its current form. You’ll often find flaws within it, like missing values. While seemingly minor, these can actually be quite pernicious: even the tiniest inaccuracies can skew your results.

Here’s where you’ll spend some time polishing the data to ensure it’s in tip-top shape. This process, called data cleaning, consists of amending or removing incorrect or superfluous data, as well as checking for incompleteness or inconsistencies. For instance, you might clean spaces in front of letters or symbols or remove duplicates.

This is a vital step—because ultimately, the accuracy of your analysis will depend on the quality of your data.

Step Four: Analyzing The Data

You now have a wealth of data. You’ve spent time cleaning it up. It’s as organized as it’ll ever be. Now you’re ready for the fun stuff.

In this step, you’ll begin to slice and dice your data to extract meaningful insights from it. Using the techniques and methods of data analysis, you’ll look for hidden patterns and relationships, and find insights and predictions.

Step Five: Interpreting The Results

After you’ve interpreted the results and drawn meaningful insights from them, the next step is to create visualizations by selecting the most appropriate charts and graphs.

But pretty visualizations aren’t all that are needed here. If you want your valuable discoveries to be implemented, you need to be able to present it to decision-makers and stakeholders in a manner that’s compelling and easy to comprehend. The best way to do this is through what’s called data storytelling, which basically means turning your data into a compelling narrative.