You say to-mah-to, I say to-may-to, but we can call the whole thing off because, no matter how we pronounce the word, both you and I are referring to the same thing – a plump red fruit that tastes good in a sandwich. There’s no real dispute here – just a variation in pronunciation. What we might genuinely disagree upon as we eat our lunch, however, is the differences between data analytics and data analysis. You, like many people, may tend to use the terms interchangeably – but are we really taking about the same thing here, or are you in fact trying to slice a peach on top of my cheeseburger?
Well, like tomatoes and peaches, data analytics and data analysis are similar – but that’s not to say they’re the same thing. A tomato is a round red fruit with seeds inside. So is a peach – but that doesn’t mean you can use them interchangeably when making soup or ketchup.
Let’s start off by putting things as simply as we can. Data analysis refers to the process of examining in close detail the components of a given data set – separating them out and studying the parts individually and their relationship between one another. Data analytics, on the other hand, is a broader term referring to a discipline that encompasses the complete management of data – including collecting, cleaning, organizing, storing, governing, and analyzing data – as well as the tools and techniques used to do so.
So, data analysis is a process, whereas data analytics is an overarching discipline (which includes data analysis as a necessary subcomponent).
That’s the fundamental difference – but let’s drill down a little deeper so we fully understand what we’re talking about here and how companies use the two approaches to gain valuable business insights.
The Value of Data
Today, data is the new gold rush for businesses. Around the globe, organizations of all shapes and sizes are scrambling over one another to get their hands on high-quality data in order to extract quantified insights that can lead to better business outcomes. Data takes the guesswork out of decision making. Rather than relying on gut instincts and speculation, having access to data means that businesses can make informed decisions on how to proceed with everything from marketing campaigns to sales, recruiting and product strategies.
However, data – even big data – isn’t valuable in and of itself. Rather, it is the actionable insights that can be drawn from it where the value lies – and this is where data analytics and data analysis come in.
Both data analytics and data analysis are used to uncover patterns, trends, and anomalies lying within data, and thereby deliver the insights businesses need to enable evidence-based decision making. Where they differ, however, is in their approach to data – to put this simply, data analysis looks at the past, while data analytics tries to predict the future.
Data Analysis vs. Data Analytics: Examining the Past and Predicting the Future
Let’s turn to some dictionary definitions of “analysis” and “analytics” to get a better handle on the two terms. According to the one I use, “analysis” is “the detailed examination of the elements or structure of something”. “Analytics”, on the other hand, is defined as “the systematic computational analysis of data or statistics”.
So, let’s consider “data analysis” in light of my dictionary’s definition. In order for a “detailed examination” of data to take place, that data must already exist. And since it already exists, the data must pertain to something that happened in the past. As such, what data analysis does is answer the question, “What happened?”
For example, a clothing retailer may analyze last year’s sales data to understand profit trends and which lines of t-shirts, dresses, jackets, coats, etc. sold well during which particular seasons, months, and/or weeks. In this sense, data analysis can be considered an in-depth review of current facts.
So, what about “data analytics”? The dictionary’s definition of analytics as a “systematic computational analysis” includes the word “analysis”, but introduces a significant modifier with the words “systematic computational”. Breaking it down, what this means is that although data analytics methods do indeed analyze data to discover what occurred in the past, they are further concerned with conducting logical, systematic and deductive reasoning to provide insights for how to act in the future. Gartner, in its IT Glossary, offers its own definition of “analytics”, within which an inherent aspect of future forecasting is also embedded. “Increasingly, ‘analytics’ is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen.”
So, let’s return to our clothing retailer. Here, data analytics users would combine the outcomes from the analyses of last year’s sales data with “systematic computational” reasoning. In practice, this means they will employ advanced machine learning tools and algorithms to go beyond the historical review and anticipate or predict future sales patterns. In this way, with data analytics, the clothing retailer can make informed decisions on when best to launch new lines of shirts, dresses, etc. over the coming weeks and months.
While of course data analysis (a review of current facts) may often be sufficient in certain decision-making scenarios, the wider and more comprehensive discipline of data analytics powers the next phase of decision making – the ability to be predictive and prescriptive.
While data analysis concerns itself with analyzing often small data sets to answer the question, “What happened?”, data analytics gives organizations the ability to both pose and answer the even more valuable question, “What will happen next?”
Importantly, you don’t get to do data analytics without first conducting data analysis – though of course, data analysis is useful in and of itself. Data analysis is used when organizations want to identify market dynamics, assess sales performances, conduct risk assessments, gauge the effectiveness of marketing campaigns and business processes, and many other things besides. Taking a deep dive into historical data reveals insights into what worked, what didn’t, and what might be expected from a product, service, or campaign.
Data analytics, on the other hand, is more expansive in its scope. As is hopefully clear by this point, it includes data analysis as a subcomponent, but takes things a step – or rather a giant leap – further by enabling organizations to utilize the potential of their data to identify new opportunities, predict the future, and prescribe the best path forward. Whether this is in identifying where and how to reduce costs, determining when and where to launch new products or services, understanding customer sentiment, or uncovering where process improvements can be made, data analytics unlocks the insights needed for the right decisions to be made that lead to business growth.
Data analysis is a necessity for data analytics, but they are not the same thing. One’s an investigative tomato tunneling into the past, the other’s a predictive peach prescribing actions for the future – now we can call the whole thing off.