Data & Analytics

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September 9, 2020

9 Different Data Types to Better Understand Your Business

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SECTIONS
  1. Internal data
  2. External data
  3. Time-stamped data
  4. Structured data
  5. Unstructured data
  6. Open data
  7. Big data
  8. Genomic data
  9. Real-time data

Data is all the rage these days and for good reason. Tech giants like Amazon and Netflix have shown us just how important it is to actually know your customers. To do that, they’ve made use of algorithms that monitor the data on customer behaviors.

The end result of this process is a plethora of recommendations that are catered to each individual.

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While it’s paramount for businesses today to get hold of valuable data to be relevant and tech-savvy, it’s also important for business owners to first understand the many different nuances of data that currently exist. In this article, we’re going to be covering a number of different data types. Knowing them will allow you to focus your direction on the ones relevant to your business.

One important thing to note is that the data types mentioned below are not mutually exclusive of each other. One data type can fall into many other categories as well. The amount of overlap each data type will have with the rest is debatable, but there is always a connection with something else.

Internal data

Blurry company report with graphs, tables, and other figures

Internal data is the data generated by a company’s internal functions. Internal functions include anything that every department under the company would perform. To put it simply, all data that is stored within the company’s databases is considered internal data.

The value of internal data is immeasurable. It allows the company to gather insights about their workings and improve on them. It also allows them to see what isn’t working according to expectations and take appropriate action on it.

Internal data includes, but is not limited to, customer behaviors, sales figures, marketing expenses, social media trends, etc. These are statistics that companies rely on to function and hold very dearly. Companies tend to not disclose such information to the public in hopes of keeping their strategies confidential.

Internal data allows the brand to tell its story in a unique and interesting way. It allows brands to showcase their success and shortcomings while presenting them in a way that is interesting for readers. Internal data can make every company’s content marketing really shine.

External data

External data is generated outside of the company’s control. It can range from anything including weather forecasts, government datasets, police records, tax records, etc. The list is non-exhaustive and will only continue to grow.

Data of this type can be of interest to many, and countless companies are already making use of it. An insurance firm will be interested in the hospital records of all their customers. An agricultural company will be vehemently consumed by weather-related data so they can plan all their strategies accordingly. Political campaigns are now taking massive interest in how social media trends evolve to better their propaganda strategy.

External data is virtually limitless in its usage. As we begin to collect more information from new devices (via the explosion of the Internet of Things) about our routine behaviors, what seems like a sci-fi fairytale right now may just be a few years away.

Time-stamped data

This is a category in which the collected data has one more element: time. Time allows us to enter a new dimension of analysis and its importance is being emphasized greatly. User experience is a very valid concern for developers and product designers these days and for good reason.

It’s been known that even minute differences can make or break an idea. How long a user takes to complete a certain task is a great predictor of their overall experience with a product. Google has been keeping a track of this metric ever since Search Engine Optimization became a serious business.

From page load times (i.e. a response 2 seconds slower than competitors can easily push you down in rankings) to seeing how long a user decides to stay on a page, Google wants to know it all.

The world is speeding up and so are our requirements from the tasks we do regularly. If something can be improved even slightly, no opportunist would ever let the chance go.

Structured data

Rectangular wooden file cabinets lined up in rows and columns

This is one of the simplest forms of data that every person is familiar with. Structured data, in its rawest form, simply means organized data.

Data is considered organized when you can decipher what it means by looking at its structure. One of the most popular forms of organized data is tables. Even on paper, we tend to separate information into rows, columns, and tables. This simple modeling can allow most forms of information to take tangible meaning.

For instance, your grocery lists are considered structured data. Items are usually listed by the name, price, and quantity you want to buy it in. In the digital world, the need for organized data grew rapidly and soon we were presented with relational database management systems.

These systems employ carefully-structured tables that are linked together and, when used in amalgamation, can present data in a way that is unbounded in its use cases

In short, any data that represents information in a decipherable way can be considered structured data.

Unstructured data

Unstructured data is the opposite of structured data. While we expect structured data to be stored in a presentable way, unstructured data is defined by its inability to showcase meaningful information just by looking at it.

Examples of unstructured data include emails, text messages, raw audio data, videos, webpage content, etc.

An email can contain extremely sensitive data about an organization/person. But, in its natural form, we can’t extract meaningful information from it. Think of it this way, how would you fit an email into a table? 

Without performing a precise sentiment analysis of the text, the email can be meaningless. If it’s processed word-by-word, it can generate its own table of useful information. 

The same goes for web page content. When Google examines a page to see where it should rank, it examines the content of the page rather than simply looking at the page as a whole.

It breaks down its structure into an HTML document and analyzes every part individually to judge how useful that information actually is. If it passes all the checks, the page gets a good rank. This is simply not possible by skimming over the page. Once the page has been analyzed, its information is considered structured data, and that is used to generate results.

Open data

Open data is the kind that is freely available to use. It’s also synonymous with open-source data which can be used by anyone for whatever purpose they feel like. Government databases, such as those on the US Consular Affairs website, are part of open data. For instance, firms that specialize in helping immigration rely heavily on the data from government-vetted sources.

Anything that you can find on Google is also open data, as long as there aren’t any restrictions that prevent you from publishing your findings on it.

While the open-source community has flourished with freely available information, companies will not want to share their data on every occasion. In fact, many privately-owned companies around the world will prefer to keep some sort of check and balance on how they release data to the public.

Companies rely on their data to stay ahead of the competition, and it’s also their right to disallow the breach of their data. Gone are the days when data security might’ve only referred to secrets discussed inside sensitive government institutes. These days, even a normal company will make you sign contracts that state data security offenses will be handled in court.

Big data

Large black data servers with the logo "imgIX"

This is a term that gets thrown out a lot and has been a buzzword for many years now. Big data is used profoundly in the field of Data Science. Data Science has seen eye-catching growth over the past decade and that’s primarily because of how the amount of data we generate has grown.

According to this article, over 2.5 quintillion bytes of data are generated every day. With data being so abundant, the growth of Data Science is inevitable. It also means that the current ways we handle data (through relational databases) are no longer viable. The surplus amounts of data need adequately designed tools that can produce results in a meaningful timeframe.

Normally, big data is referred to as something that cannot fit inside our typical tools. Hadoop is one such tool that is designed to work with humongous inflows of data.

The rise of big data has allowed the fields of Artificial Intelligence (AI) and Machine Learning to prosper. This is in part of how these fields effectively rely on large amounts of information to be successful.

With more businesses adopting AI to boost their successes every day, it will always be a viable choice to incorporate it into your own business. It might not work for every business out there right now but will soon be a necessity.

Genomic data

This data type is mainly focused on the medical field. With computers allowing more complex and faster computations, reading DNA structures has been made viable. Our DNA contains about 3 billion base pairs which used to be extremely costly to analyze just a decade ago.

The ability to analyze DNA sequences allows scientists to understand unsolved problems the human race faces. It can include why different diseases attack people in different ways, help us understand cancer better, and even allow us to mutate our genes to prevent certain diseases.

Breakthroughs in the medical field are possible because of large amounts of data and complimenting machinery. With more data being available, previously impossible problems now have a chance to be solved.

Through this data, a controversial debate has also emerged. Some companies are experimenting with “designer babies,” which are genetically-altered newborns. Their mutations allow parents to choose certain traits for their kids. The debate is whether science has gone too far and is venturing into domains that should be left to nature. 

If these mutations can help prevent unwanted diseases, then it seems fair. However, once people begin to make changes to appearance, that can get difficult to justify.

Real-time data

Hand holding a smart phone that displays a Google Maps app query for "butcher"

Real-time data makes use of currently available information to predict future events. We take advantage of real-time data almost unconsciously these days in the form of Google Maps and Uber rides

These technologies have allowed us to make better routines by choosing the less crowded route to get to work and also finding nearby places to eat. All data that makes use of information regarding our current position or status in time can be called real-time data.

Its use cases are also prevalent in medical technologies. Patients that are in the ICU have their data transmitted to caretakers through linked devices. They’re immediately notified if something changes, and that’s possible because of real-time data. If patterns deviate strongly for even a second, alerts are sent to the right people.

Closing words

Data has impacted our lives to an extent where we simply cannot deny it anymore. Every other industry is trying to make the best use of it and it would be interesting to see how new industries will pop up as we continue to generate new kinds of data every day. 

This is also why data security is a major concern now. If data falls into the wrong hands, people’s lives can get torn apart. Data is not a joke and everyone should keep their data safe by following general guidelines.

That being said, if you're interested in incorporating these different data types in your everyday business operations, we encourage you to check out the helpful links below:

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