There is much confusion surrounding the difference between analysis and analytics. Both sound so alike, yet they are separate in terms of definitions. Due to the similarity of the words, however, some people believe they have the same meaning and, thus, use them interchangeably. Technically, this isn’t correct – there is, in fact, a distinct difference between the two.
In general, there isn’t a consensus about which activities fall solely under the category of analytics and which can’t be defined as analysis. This leads to people incorrectly placing the terms under the same denominator. Additionally, titles like a business analyst and data analyst significantly differ from each other, yet the 2 positions are still confused for each other – if you’re interested in learning what sets them apart, however, check out our article on the differences between the analytics job roles.
In this article, we’ll shed some light on these two very important terms and provide you with clear-cut definitions so you can more clearly distinguish between them.
What Is Analysis?
Analysis is the investigation of why something happened.
When we look at the variance between actual figures and the numbers in the company’s budget, we are implementing an analysis. If we want to understand the underlying business performance of a company, we’d be analyzing the variance of a financial item such as revenue. And the way we do these assessments is by using data we have gathered about our performance so far.
The important thing to remember about analysis, however, is that you seek to interpret events that have already happened in the past. Such as to explain how and why there was a decrease in your company’s sales last summer. Essentially, when you are doing analysis, you look backward in order to understand how your company has performed against the expectations of your stakeholders.
What Is Analytics?
Every stakeholder has expectations that we need to translate into numbers. This is also known as the long-range plan and the annual business plan, or ABP for short.
That means that when we prepare these plans, we need to predict what will happen in the future. Of course, we’d usually have some past business data, however, the past is not always the best outlier for what the future holds. Therefore, we need to have more sophisticated tools that are forward-looking rather than backward.
Here is where analytics comes into play. As you have probably guessed, it generally refers to the future. Instead of explaining past events, it explores potential future ones. Analytics is essentially the application of logical and computational reasoning to the component parts obtained during analysis. And, in doing this, you are looking for patterns in the data and exploring what you could do with them in the future.
This term refers to a model that creates scenarios and predicts performance on the basis of past scenarios. In essence, analytics is a strategic asset that enables top management and the Board of Directors to make better-informed decisions through various techniques, such as time series analysis.
The Two Areas of Analytics
What are the areas of Analytics? If we narrow our focus, we’ll see it branches off into:
This type of analytics requires using your intuition and experience in conjunction with the analysis to plan your next business move.
With quantitative analytics, you apply formulas and algorithms to numbers you have gathered from your analysis.
Qualitative and Quantitative Analytics Examples
Say you own an online clothing store. You are also ahead of the competition and have a great understanding of what your customer’s wants and needs are. This is because you’ve performed a very detailed analysis based on women’s clothing articles and feel sure which fashion trends to follow. This intuition helps you decide which styles of clothing to start selling – otherwise known as qualitative analytics.
However, you might wonder when to introduce the new collection. In that case, relying on past sales data and user experience data, you could decide in which month it would be best to do that, based on when your last collection’s sales hit their peak. This is an example of using quantitative analytics in fashion.
The Benefits of Data Analytics
Success in business is crucial – and so is staying on top of all the technological advancements. And, one of the best ways to grasp the numerous benefits of data analytics is to see how hugely successful companies have reaped the rewards from implementing analytics into their ranks.
The streaming service has been dominating the scene for some time now and continues to set precedents of success among its competitors. What lies behind Netflix’s success is their initiative to implement data analytics to predict what their consumer would most like to see. Their analysts spend countless hours poring over not only what the users watch, but also when and for how long. Then, they feed this information onto the company’s higher-ups, assisting them in their decision making.
Netflix, as of recent years, also plays a large role in producing most of the mainstream original content based on what their viewership responds to best – Backlinko shows that these Netflix Originals actually generate the most traffic for the company.
Furthermore, according to Statista, in 2020, Netflix saw an increase of 36.57 million paying subscribers and reached more than 200 million users worldwide. Clearly, data analytics is working for them.
You are probably familiar with Etsy - the hugely popular online marketplace for bespoke handmade goods. Much like a real-life market, sellers use the platform to set up shops and offer their crafty goods to interested buyers. However, since the products are all so unique, they’re also difficult to categorize.
How has Etsy managed to solve this problem? By taking on a very data-oriented approach in its business. The company has focused on building a product recommendation algorithm that anticipates buyers’ interests in advance, offering them the trinkets they’re most likely to click on. And, what is more, it is clearly working – according to data, the website’s revenue has been steadily growing for the last 5 years, reaching a spectacular surge: 10 billion dollars in gross merchandise revenue in 2020.
But that’s not the only place Etsy’s implemented data analytics. About 80% of the employees access the collected data and work with it on a weekly basis as part of the decision-making process, as well as to prevent payment fraud. In fact, Etsy is so data-driven, they’ve launched an organization where data analysts and machine learning engineers can experiment with data and help improve users’ experience on the website.
Walmart is well-known as the world’s largest retail company. With more than 20,000 stores across 28 countries, the chain offers a variety of goods, ranging from groceries to home renovation tools and tech gadgets.
The company launched a so-called Data Café where teams of analysts go over thousands of datasets, most happening in real time, to locate problem areas – for example, where and why certain products are not selling. This way they fix possible issues and optimize sales for the given location. In addition, Walmart uses real-time analytics to predict customer inflow in order to allocate the most employees at checkout at busier hours.
Of course, they don’t just deal with internal data – analysts pull external data on weather conditions, economics, upcoming local events and more, to ensure that their locations are stocked appropriately and can meet customer demand. One such example is stocking emergency equipment and, surprisingly, Pop-Tarts before a predicted hurricane in the US, based on the data from a previous event.
Analysis vs. Analytics: Next Steps
Analysis and analytics are not exactly homophones but might as well be with how often people get their definitions wrong. The good news is, you’ve now learned that analysis deals with events that have already happened, while analytics steps on past and current data, and is primarily forward-looking. This makes you one step ahead of the game!
Additionally, analytics can completely transform a business. You’ve by now seen the colossal impact it has had on industry giants like Netflix, and how they’ve used it to rise to the top. The way they’ve done this is by hiring expert data scientists that have used their analysis and analytics skills in their favor. Your portfolio and career outlook as an aspiring data science professional will be greatly improved by acquiring these skills as you dive deeper into the data-oriented business world.