What Is Data Analytics? A Definition, Types & Examples

What Is Data Analytics? A Definition, Types & Examples


Data analytics is the process of deriving insights from data. It uses techniques like statistics, machine learning and artificial intelligence to understand what’s happening in your business and how to improve it.

Data analytics can be used in almost every industry and type of business, and it’s an essential part of any modern organization. For example, Google uses data analytics to help advertisers target their customers more effectively; retailers use it to find new ways to save money by optimizing inventory levels; and hospitals use it oncology data analysis for faster diagnosis times when treating cancer patients.

What Is Data Analytics? A Definition, Types & Examples

What is data?

Data is a collection of facts. It can be collected in many ways and then stored, transmitted and analyzed in many ways. Data can also be used to make decisions in many ways.

What is data analytics?

Data analytics is the process of using statistical methods and computer programming to extract knowledge from data. It’s a cross between science and art, where you use your intuition and experience to analyze large amounts of data in order to make predictions about future events.

Data analytics is used in many industries, including healthcare, finance and retail–but it also has applications outside of business as well. For example: imagine you’re an airline pilot who wants to know if there’s any correlation between weather patterns at certain locations with flight delays or cancellations over time (this could help identify areas where weather conditions pose a threat). Or perhaps you want to determine whether patients with similar medical histories tend not only recover more quickly but also have fewer complications than those without such similarities? Data analytics can help answer questions like these!

Data analytics vs. business intelligence

While BI is about reporting and analysis, data analytics is about gaining insights. Data analytics can be defined as the process of discovering patterns in your data that lead to new insights. These insights can be used to make predictions or take action on what you’ve learned.

Business intelligence (BI) tools help you analyze current business performance, while data mining and predictive analytics tools help you predict future trends based on historical patterns in your data sets.

Data analytics vs. machine learning, artificial intelligence and deep learning

Data analytics is a subset of machine learning, which in turn is a subset of artificial intelligence.

Data analytics also overlaps with deep learning and business intelligence (BI). Data science is not a synonym for data analytics, though some people use it as such because they think it sounds more impressive or technical than “analytics.”

Real-world examples of data analytics

Data analytics is a powerful tool that can be used in many industries. As an example, let’s look at how it can be applied in healthcare.

To start with, data analytics helps doctors identify patients who are at risk for heart disease and other illnesses. This allows them to provide early intervention and preventative measures when appropriate. Data analysis also helps hospitals improve their overall patient satisfaction scores by providing more personalized care based on individual needs; this means less time spent waiting around and more time getting treatment!

The retail industry uses data analytics for similar purposes: identifying shoppers who are interested in certain products so stores know what products they should stock more of or advertise more heavily online through targeted ads based on past purchases made by other customers who bought those same items from competitors’ stores nearby where yours currently operates (or even across town). This kind of information helps retailers stay ahead of competitors while making sure they’re not wasting money advertising items nobody wants–which could drive down profits if done incorrectly over long periods of time due to poor planning decisions made under pressure due to lackadaisical leadership styles among executives whose only goal seems focused solely upon increasing sales figures rather than ensuring quality service delivery which would lead them towards profitability without fail every single year without fail no matter what happens next week next month next year etcetera ad infinitum .

Data analytics is a cross between science and art.

Data analytics is a cross between science and art. The science part is the data collection, analysis and visualization of information; the art part is how you use that information to make decisions.

The best way to understand this relationship between science and art is through an analogy: Imagine you’re in a museum looking at paintings hung on walls in chronological order from left to right. The first painting is Picasso’s Les Demoiselles d’Avignon (1907), which depicts five prostitutes in their dressing room staring back at us with blank stares on their faces; it’s a masterpiece of cubism that represents modernity through its use of geometric forms. Next up is Monet’s Water Lilies (1914), which shows us his view from his houseboat on Lake Geneva–the colors are beautiful but simple: blue sky above green water below with yellow flowers floating around them both!


Data analytics is a cross between science and art. It’s not just about crunching numbers, it’s about understanding how those numbers relate to each other, what they mean and why they matter. There are many ways to do this: some people prefer the rigor of statistics while others gravitate towards more exploratory approaches like machine learning or artificial intelligence (AI). You can even combine different methods into one solution! The key point is that if you want to succeed in today’s data-driven world then you need strong analytical skills – no matter what industry you’re working in.”

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