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The Three Main Types of Big Data and What They Mean for Your Business
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The Three Main Types of Big Data and What They Mean for Your Business

Big data is the Frankenstein’s monster of the information age. In a world where nearly everything is digitized, from communications and transportation to the economy and even hobbies and pastimes, a barrage of data points constantly bombards the airwaves at any given moment.

While this could be a blessing for companies looking for more information to fuel business intelligence, it’s also a double-edged sword that could wreak havoc on your business process and make you less competitive.

Big data is a beast that, when tamed, can power innovation and growth in your business. To do this, you first need to master the different types of big data and their applications.

In this guide, we’ll tour the world of big data, taking a deep dive into the three main types of big data: structured, unstructured and semi-structured. We’ll look into why you need each type and how best to incorporate them into your business operations.

What is big data?

Big data is a blanket term for any amount of data that’s too large to be processed and stored using regular data management tools and techniques. Due to its sheer size and complexity, specialized tools are required to analyze, store and utilize big data to harness it for business intelligence and forward planning.

Oftentimes, businesses don’t realize they have a big data problem until it begins causing serious inefficiencies in their business processes. They wake up one morning and realize that the system they’ve been relying on has been overwhelmed, unable to organize and account for a huge swathe of data.

If that sounds like your business, your big data problems could be over sooner if you can change your approach.

Your business most likely needs big data solutions when your business data begins to exhibit certain characteristics. Familiarizing yourself with these traits can help you tell exactly how big your big data problem is, the categories of big data you need to cater to and the right tools and techniques for managing the data.

The Three Main Types of Big Data and What They Mean for Your Business

These traits include:

  • Volume

Your business enters big data territory when the size of your data approaches or surpasses the petabytes range (with 15 zeros). This is far beyond the processing and storage capacities of regular devices like desktops and laptops.

  • Variety

Big data is also characterized by a huge variety. Not only do you have expansive amounts of data, but you also have data from many different sources pertaining to many different things. This can include contact details, buyer data, your customers’ preferences and records of your employees’ activities, as well as all the data points generated whenever anyone interacts with your brand.

  • Velocity

You’ll also know you have a big data problem when the data is pouring in thick and fast – too fast for your regular data management tools to catch up with. Sometimes, you need to process and analyze data in real time, and the overwhelming pace of it can seriously undermine your workflow.

  • Value

With big data, you’re facing an exponential increase in valuable data. It’s not just some random data points, but rather highly targeted, high-value data that you can’t afford to underestimate or misrepresent.

  • Veracity

You can count on the source of the data. Big data is trustworthy, high quality and authentic. That means you have more valuable, reliable data to help you figure out growth-inducing insights.

Types of big data

Now that you have a fair idea of why you might have a big data problem at hand, let’s examine the three types of big data and what they could represent in your business.

  1. Structured data

As the name suggests, structured data comes prequalified and organized, making it easy to sort and analyze. Data points are grouped based on predefined properties.

Also called relational data, structured data comes with data points juxtaposed with each other. Rather than containing random data with unrelated purposes, structured data is often interrelated and arranged accordingly.

That makes it easy to find specific data points and make cross references. You can instantly make inferences about each data point at a glance – there is no need for in-depth foreknowledge of the data sets to make sense of them.

A typical example is the classic spreadsheet, but there are a host of other formats for organizing structured data.

Oftentimes, a schema is used to organize the data at the point of collection, enabling automatic sorting. This significantly minimizes the efforts needed to maintain and update the data set.

Sources of structured data

Here are some of the ways your business can generate structured data:

  • Registration and subscription data: The most instantly recognizable example of structured data is customer data generated from KYC (know your customer) forms, subscription forms, lead opt-ins, etc.
  • Feedback and user-generated content: This includes data from interactive activities like polls, surveys, questions and answer sessions, etc.
  • Experimental data: Like user-generated data, experimental data also is also used to gain more insight through interactions with users, but this time indirectly. Experimental data is derived from tests and iterative sequences. An example is when you’re A/B testing different copies of a landing page, tracking their click through rates, dwell times, conversion rates, etc.
  • Transactional data: This relates to data collected during transactions – your customer contact details, purchase details, etc. This can also include data from all your customers’ interactions during the transactional stage of their buyer journey.
  • Researched data: Sometimes, a business might need to go out of its way to find data from outside sources for reasons like competition intelligence and research. Most times, data for these purposes are gathered using methods that pre-organize them to expedite the research.

Why consider using structured data: The pros

Let’s look at some key reasons why you should incorporate structured data in your business processes.

  • Easy navigation: You can easily scroll to any specific data point and cross-reference multiple values.
  • User friendly: You don’t need to know much about the data set beforehand to be able to understand it and make inferences. That means people won’t need much guidance or expert knowledge to make sense of the data, from your employees to stakeholders and customers.
  • Analysis ready: Structured data clears the runway for analysis and research. This leaves you with much less processing to do and with more time at hand for your analysis and conclusions.
  • Extensive compatibility: Since it’s analysis ready, structured data can easily be plugged into most data-processing tools, from analytics tools to business intelligence software and CRM software.
  • Security: It’s much easier to store and secure clean, organized data compared to disjointed sets of data.

Why you may not need structured data: The cons.

  • Limited applicability: Structured data must come in a certain format, and this inherently rules it out from being used in applications that require a different format.
  • Longer preparation time: Structured data doesn’t actually mean there’s no effort required to prepare the documents. Oftentimes, structured data isn’t organized into the proper format at the point of collection. For instance, you might need to transfer data from a certain subscriber list to a larger database of subscribers, and it might take some effort to reorganize the data into a new format.
  • Complex data structures: Structuring data can also lead to issues like duplicate data (in which the same data points are stored in multiple formats), redundant data and unnecessarily cumbrous data structures.
  1. Unstructured data

Unstructured data is information that lacks any predefined properties. Unstructured data make up most of big data, and by extension, most of the big data problems that businesses face today.

Think of the tons of random videos constantly uploaded on YouTube, Facebook chats and posts or your GPS location data with random timestamps and geo coordinates. They’re massive and diverse.

Also, unlike structured data, which are stored in data warehouses as processed files, unstructured data is often stored in its raw form in data lakes, where it can easily be taken out and organized into any specific schema.

Sources of structured data

Here are some of the data sources that could inundate your business with unstructured data:

  • Captured data: The problem with captured data from landing pages, KYC forms and more is that, although there may be measures in place to pre-organize the file, you might still end up with unstructured data. That is, you still might need to make an extra effort to extract the data into a required format for a particular application.
  • User-generated data: This encompasses any user-generated content related to their interactions with your brand. That includes anything from your brand mentions around the web to tweets, comments and reviews of real-life customers.
  • Qualitative data: Any set of data quantifying or qualifying certain entities or items might come presented in an unstructured form. Think of medical records and how they might need reorganizing when moved between different databases to share or update a patient’s records.

Why your business needs unstructured data: The pros.

While unstructured data basically sounds like a huge pile of mess, there are many reasons why you might want to preserve data in this format. Here are the pros of using unstructured data:

  • Highly adaptive: Unstructured data is stored in its raw form, free from formatting of any sort. That makes it much more malleable and easier to shape into any desired structure. That also makes it highly versatile and easy to co-opt into any specific use case.
  • Quick processing: Unlike structured data, which needs to be broken up before being processed, unstructured data in its raw form requires much less processing to prepare it for any application. Applications like natural language processing and data scraping work better with unstructured data.
  • A wide range of database options: A vast crop of database solutions has been introduced in recent times to help businesses manage their unstructured data. The most popular ones are the NoSQL databases like MongoDB, Redis and Neo4J. These databases support big data and business analytics processes by offering immense capabilities for storing unstructured data in a wide variety of formats. Learn more about this exciting field with a DBA in Business Intelligence from Marymount University.

Why you might not need unstructured data: The cons.

  • Complex processing requirements: Unstructured data is easily malleable and requires less processing – but only for the data experts who are conversant with the data. An unfamiliar set of unstructured data could take much longer to prep and process.
  • Difficulties with maintenance: Unlike structured data, it’s quite challenging to clean, maintain and update unstructured data.
  • Advanced tooling requirements: Unstructured data requires special tools and expertise. Unlike structured data, unstructured data isn’t easily cross-compatible. You might require a different set of tools for managing different sets of unstructured data.
  1. Semi-structured data

You can have data in both a structured and an unstructured form on the same file. Semi-structured data could also contain unstructured data that is loosely defined by properties like tags or metadata but isn’t transfixed into any specific schema, bringing together the best of both worlds of structure and no structure. The most common examples are JSON and XML.

Structured data sources

Here are some of the most common sources of semi-structured data:

  • Media files: When you shoot a photo or video and save it on your device, it doesn’t usually contain information that anyone can use to instantly establish the purpose of the content. But it does provide clues with metadata like time and date stamps.
  • Emails: A classic example of how structured data can be placed alongside unstructured data is in an email. The structured data (the sender and receiver addresses, the subject and date) is used to group the email (spam, important, labeled, etc.). The content of the mail can be unstructured, with no predefined parameters to quantify or qualify it.
  • Machine-generated data: Semi-structured data can also be generated by tech systems, such as satellite data, video coverage, electronic data interchange (EDI), etc.

Why you might need semi-structured data: The pros

  • Flexible schema: Structured data isn’t based on any schema.
  • Ease of use: Semi-structured data can be used by both experts and non-experts.
  • Easy maintenance: It’s also easier to maintain and update a semi-structured database.

Why you may not need semi-structured data: The cons

  • Lack of structure: The loose structure of semi-structured data can pose many storage and security problems.
  • Inefficient queries: Databases with semi-structured data might often generate inaccurate returns for search queries.

Leverage big data for business growth

This brief overview of the rough classification of big data has given you a quick head start. Depending on who you ask, these three types of big data are just the tip of the iceberg, and the classification cascades much further.

However, most of the big data problems facing businesses today emanate from these three main categories of big data.

Mastering and leveraging these three types can help you create more opportunities for business exploits.