Data analysis and data analytics are two very similar terms that describe extensively different fields. While data scientists normally have the distinction memorized, it’s one that often troubles people that are new to the field.
In this article, we’ll break down both data analysis and data analytics, demonstrating absolutely everything that you need to know about what sets them apart. We’ll cover a core definition of both before moving on to the main factors that separate them.
Let’s get right into it.
What is Data Analysis?
Data analysis is the practice of interacting with data in order to draw meaning from it. In this case, interaction refers to cleaning, modeling, querying, questioning, and visualizing data in order to uncover previously hidden trends and ideas. Data analysis is a subset of data analytics, formed around an attempt to find solutions to our problems within the data we have access to.
Much like data analytics, data analysis comes in many different forms and styles:
- Comparative Testing – When putting two sets of data in conversation, we can draw meaning from them and use this insight to increase the efficiency of business practices.
- Pattern Identification – Using data mining, those engaging in data analysis can expose trends and patterns in large sets of data and extract them.
- Predictive Analysis – Moving through data and identifying trends will allow us to then extrapolate that information into the future, creating a prediction of what’s to come.
- Diagnostic Analysis – This form of analysis allows you to determine why certain things are happening. You can use data to pinpoint commonalities in your user base, or identify key moments when a change occurred and why.
- Perspective Analysis – Finally, prescriptive data analysis is where you take insight from data and use that to find new opportunities for growth and improvement. It is a composite form of data analysis that pulls from many different areas.
Data analysis is an incredibly expansive field. In our shortlist, we haven’t even begun to touch on all of the different forms of data analysis that experts can enact. It is a field that has become vital in the world of business, helping organizations around the globe streamline their operations.
What is Data Analytics?
Data analytics is a broader field, one that actually encompasses data analysis. At its core, data analytics is all about the movement of raw data in order to streamline decision-making processes.
The entire data pipeline, including collecting data, transforming it, and loading it into a data warehouse or other data infrastructure, is included in general data analytics. While data analysis is the final stage of analysis, data analytics covers the entire process.
Data Analytics vs. Data Analysis – What’s the Difference?
When comparing these data analytics and data analysis, it’s easy to get the wrong end of the stick. In fact, there are a lot more similarities than differences. However, that doesn’t mean that they’re exactly the same thing. There are a number of differences that we can focus on to clear up the matter.
Across these two fields, there are three main differences:
Let’s break these down further.
The score of data analytics and data analysis is one of the largest areas of difference. Considering that data analysis is a part of data analytics, it’s no wonder that analytics is a much broader field.
Data analysis is concerned with the lowest possible level of data insight. It will work with data that has already been processed, providing insight. At its most advanced form, real-time data analysis will provide a continual flow of new information for businesses to work with.
Data analytics takes a much wider approach, covering the entire process right from data collection all the way to drawing insight. Instead of just covering the final push, so to speak, it spans across your company’s entire dealing with data.
The vast majority of the time, all of the data analysis that your teams engage in will come directly from a designated platform for that style of analysis. For example, if your marketing team is tracking SEO data, then you might use Google Analytics – a platform filled with tools for that specific kind of analysis.
Any data analysis that you conduct will likely be done from within an associated program or platform. If your business has the budget, you may have developed your own analysis platform for exploring certain kinds of data. Most of the time, your CRM platform and any other attached tools will become your go-to site for data analysis.
Data analytics, on the other hand, encompasses a much larger process than just analysis. Due to this, much of data analytics will rely on having a stable and flexible data infrastructure in place. A larger percentage of this infrastructure is now provided by third-parties. For example, when it comes to conducting customer-facing analysis, businesses need to use several specialized engines.
If we take a look at a comparison between leading specialized analytics databases, like Druid vs Pinot, we can see how advanced the array of features that these third-parties now offer. Businesses are able to tap into these tools to perform a high level of customer facing analytics.
One final, yet important, distinction to make between data analysis and analytics is the precision of their findings. Most of the time, businesses will use data analysis for descriptive analysis. If a business wants to find out why something is happening, or pinpoint movements and changes, then data analysis is the way to go.
While its scope is much smaller, it often gets right to the core of data. Data analysis is fantastic when approaching data with a specific question. Data analytics doesn’t have the same level of exactitude, instead being used for uncovering general relationships and trends instead of descriptive explanations.
Data analysis and data analytics both make up a vital part of modern business. With 97.2% of businesses currently investing in big data, it would be incredibly surprising if your company isn’t already engaging with at least one, if not both, of these fields.
Establishing and understanding the distinctions between data analysis and data analytics will allow your teams to use the right vocabulary when discussing data. Increasing your company’s data literacy is always a net positive. As data becomes an ever-important part of the business world, the more people that are data natives, the better.