Every company in the world – from a kid’s lemonade stand to a large corporation like Apple – has data at its disposal. Though a lot of data is created in similar ways, the real value of data comes from being able to interpret it through data analytics and use your analysis to make strategic decisions. There have been an incredible amount of technological tools developed in the past few decades to help businesses interpret data. These analytical tools have become an extremely important skill for the 21st century business leader, and a hot industry in their own right as well.
Stop us if you’ve heard these data buzzwords – data analytics, big data analytics, data science. But what do they all mean? We’ll explain in order to help you determine when to use which tool, or if the data analytics industry is right for you.
What is Data Analytics?
Data analytics involves taking existing sets of data and extracting patterns and meaning from them. There is obvious benefit to a business to have data guide business decisions. In short, analytics helps uncover actionable insights that allow a business to solve a problem or identify a problem it didn’t even know existed.
Data analytics works best when it is focused, meaning there is a specific set of data that will be examined and there are key questions you have in mind (we call this being hypothesis-driven). You can think of data analysts as “slicers and dicers” of data, usually using tools like Excel, SQL, or Tableau.
What is Big Data Analytics?
Big data can be thought of as a specialized field that fits under the broader umbrella of data analytics. The main differentiator of big data is that it examines extremely large amounts of data to uncover hidden patterns, correlations and other insights. An easy way to identify if a data set can be considered “big data”: it doesn’t fit within the parameters of Excel.
Think of it this way – if several decades ago companies could analyze hundreds of rows of data in Excel, big data focuses on analyzing anywhere from thousands to millions of rows. With powerful software and technology, big data helps companies make meaning out of extremely large datasets.
Data Science vs. Data Analytics
Though the two sound very similar, data analytics and data science are two distinct and separate fields. Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data, though it tends to be a lot more “technically intensive” than data analytics. Data scientists typically require all the same foundational skills as data analysts but require several additional technical skills.
Here are some of the key differences between the two fields:
- Data analytics usually involves data from a single source such as a CRM system, while data science examines data from multiple disconnected sources.
- Data science usually requires machine-learning, statistical modeling, and mathematical modeling skills, while data analytics doesn’t require advanced technical knowledge.
- Data analytics usually focuses on solving questions provided by a business, while data science will involve formulating questions and finding solutions to best benefit the business.
Concluding Thoughts – Why Are These Fields Important?
Data analytics, big data analytics, and data science can be thought of as distinct fields, but they also have many overlapping characteristics. Depending on the size and needs of a company, there are times where one, two, or all three will be important to utilize. Given the advanced skills required for big data analytics and data science, those two fields tend to garner higher salaries than simple data analytics.
These fields help companies save on costs, make faster business decisions, and generate better products and services. In a world with more data than ever before, and less people who know what to do with it, these fields will only continue to grow in size and importance.
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