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What are the Different Types of Data? Finding Your Data Niche

business man with multiple white boxes that say data hovering in his hands

Data comes in many shapes and sizes, but not all data is created equal when it comes to accessibility and usability. In the least granular sense, there are two major types of data: qualitative and quantitative.

Qualitative data is often referred to as the “subjective” form of data and would include things like smells, textures, level of enjoyment from a given activity, etc. When speaking about qualitative data, there are very few facts, just observations. A simple example of a qualitative data set would be information gathered from a focus group. All of the members of the focus group may agree on something, but it’s still not to be taken as “fact,” just relevant information to use when trying to appeal to the broadest audience.

Quantitative data is the measurable type of data and can be taken as fact once proven. A simple example of quantitative data would be a count of how many visitors a website received. If a service like Google Analytics reports that 700 people viewed a website, that can be taken as fact and used in a quantitative data report.

Having a large bank of both quantitative and qualitative data will make it much easier for data analytics teams to turn your data into information that will make your business more profitable.

Now That You Know Your Data, Better Know What You Want to Do with it

Being able to monetize data is an ever-changing challenge, and one with no set path for success, but that also increases your chances of becoming a data specialist even if you don’t have a background or degree in the data science field.

Depending on what type of data you have collected/are planning on collecting, here are a few examples of jobs that require professionals in the analytics field: business analysts (very wide field), healthcare system advisors, image processing positions, financial analysts, marketing strategists, and a whole lot more.

Most companies know they need data analysis, but not all companies know exactly what that means. Simply showing that you know what you’re talking about will almost surely nullify any work experience requirements you may not officially meet.

Protecting Different Types of Data from Data Breaches

Once you’ve checked the boxes of knowing your data and knowing how to use your data, it’s important to know how to protect it from people looking to take shortcuts (also known as stealing).

Data breaches cost companies billions of dollars, so spending a pretty penny on preventing them is a good idea. Cyber-attacks include viruses, spyware, phishing, and more, and can mean the disclosure of names, email addresses, credit card numbers, home addresses, and more.

If your company is large enough, hiring a cybersecurity specialist to prevent cyber attacks on your data is a great way to get cybersecurity off your plate, but there are many small actions that can be taken to increase your chances of avoiding a data breach. The more known actions are simple things like hiding your Wi-Fi network name, creating backups of valuable data, constantly train employees on data protection, utilize antivirus/antispyware software, and change your passwords regularly.

So You Want to Be a Data Scientist? Here’s What You Need

Does data science seem like something you’d enjoy? It’s certainly lucrative and some statisticians believe that the data science industry is worth $3 billion today and that it will double by 2025.

As mentioned before, there is no single path to becoming a data scientist, but a graduate education in the field is a great start, especially if becoming a data scientist is something you want to do in the slightly distant future, rather than tomorrow. Degrees in computer science, mathematics, physics, and the like provide the best tools for an aspiring data scientist, and obtaining a Master of Data Science is something that is being offered more frequently at universities.

Whether you choose to obtain a graduate degree or not, honing your hard skills in areas such as data mining, statistical analysis, social media, and business strategizing will put you well on your way to becoming a confident and knowledgeable data scientist.

For more great business tips, check out the other blogs on Career Geek.