The benefits of boundless: A guide to big data

Data saves companies time and resources while preventing unnecessary and costly errors. But what makes it ‘big’ and why is big data such an important part of the puzzle? Furthermore, how can we use it to our advantage?

What is big data?

True to its name, big data is bigger, more extensive groupings of files and information. Size, speed and selection characterize these large sets. Big data contains structured, unstructured and semi-structured types, and these information groups can be used in many unique different ways.

When is data considered big data?

In order to help you remember what makes a digital file qualify as big data, here are identifiers we’ve coined as the ‘S-factors’: Size or space; Selection; Speed. Let’s break down each in detail.

• Size or space. If a grouping is known to have a large space throughout different mediums, it is likely to be considered big data. Without a big capacity, it cannot really ever reach the size needed to qualify.
• Selection. Selection refers to how expansive the assortment of information are collected within systems. For example, if a set of files comes in numerous forms, such as images, videos, audio, spreadsheets, and more, it is considered big data.
• Speed. Speed refers to how fast the majority of information is collected, prepared and produced. When it comes to tasks and processes, big data flows in large quantities and at fast speeds from things like websites, logs and devices.

Note that it used to be common to identify big data using the three V’s – volume, variety and velocity, but it has evolved to encompass more after these terms were coined, so companies are moving away from using them.

5 reasons big data is so important

Available digital insight is only as valuable as you make it. The key, then, is to analyze and manage your big data marketing to find new ways to overhaul your projects and campaigns. When done correctly, analyzation and utilization can lead to the following benefits:

1. Minimized expenses

When a company is able to process big data, they find new ways to manage funds and spending, allowing them to cut down the costs of what it normally requires to build effective marketing campaigns.

2. Maximized use of time

Because of how informative big data can be, teams are able to move through each phase of a project much quicker and with more decisive resolve.

3. Improved customer experiences

With an expansion of customers comes an expansion of relevant details concerning feedback. Big data enlightens companies as to what customers expect out of them and lets them make future customer experience much better. This includes being able to better serve customers at different touch points in their experience with personalized offers.

4. Reduced service and/or product issues and failures

Because of the fast flowing nature of big data, using it gives companies the chance to minimize potential service problems which might arise. Furthermore, the wide variety of information within these digital sets helps teams uncover the main factors causing these potential issues.

5. Faster, more efficient projects and campaigns

By processing big data, companies are able to draw from many different sources of information, including things like social media platforms and search engines. These insights allow for accurate, quickly-created projects and campaigns.

Types of big data

Now that we know when to consider data ‘big data’, let’s go over the different types. The more you understand the nuances between each classification, the better you’ll be able to analyze and utilize the information. Here are the three types:

• Structured. Structured data is information organized according to specific indicators, such as numbers, payment types, age, locations and other numerical-based demographics. It is named for how easily it works in coordinated outlines that help bridge the gaps between information.
• Semi-structured. Semi-structured is a bit more raw and uncoordinated than structured data, however, it has helpful identifiers attached to it that make sure a user can locate and understand it. Ultimately, semi-structured is a perfect middle between structured and unstructured types.
• Unstructured. Unstructured is the most raw and uncoordinated type of big data. It is stored without any type of organization. Despite this, it is a giant portion of modern digital information, since it is the result of everything people do when they interact.

Notable use-cases

Though this is subject to change, these are some of the most common processes relating to big data.

Artificial intelligence

Artificial intelligence and machine learning are quickly becoming staples in modern software systems. Big data is one of the main catalysts behind allowing these machines to function at such a high level with inhuman accuracy. Without the fast-flowing figures in large quantities that it provides, machine learning would fall flat.

Customer experience

A customer experience is how favorable a customer views a company or brand based on previous interactions with them. Without the benefits of big data, marketers would be in the dark when it comes to building better customer experiences. This is because of all the statistics gathered from unique interactions that can lead to personalization.

Maximize general efficiency and accuracy

Big data brings the chance to overhaul efficiency through better monetary application to each new project. It also improves accuracy in terms of analyzing customer leanings and trends. And these improvements further as more information gets processed.

Big data Examples

The following illustrative examples give us a chance to see how successful companies are maximizing their digital insight in unique ways.

Twitter

The days of Twitter simply being a way for users to share their random thoughts are long gone. Now, Twitter connects us with celebrities, world leaders and friends, while also giving us news and personalized suggestions. And it couldn’t have done this without machine learning and AI driven by big data.

Once Twitter capitalized on the digital information it had available, it used machine learning to put this information to good use. The result is a platform that blocks out what you don’t want and gives what you do.

Spotify

If Spotify wished to exist simply as a basic music streaming service that played whatever a user demanded, it wouldn’t be too difficult. However, it also wouldn’t push the boundaries of streaming service evolution.

Spotify instead uses big data to create complex algorithms which give their customers personalized playlists, focused ads, and AI-developed content. Spotify essentially used available user insights to give their listeners music that even they themselves didn’t know they wanted until they heard it.

Google Maps

Google Maps wasn’t built using a compass and a few road atlas. Instead, it used (and continues to use) the power of big data to further its efficacy. Maps has quickly surpassed other GPS navigational systems as the most reliable mapping form, thanks to the large wealth of information it pulls from.

As more people began using smartphones and more smartphones came with GPS, the flow of big data went into overdrive, and Maps now incorporates this information to not only predict potential new routes and areas, but also solidify already-established mappings.

Where do we go from here?

No one has a crystal ball, but there are a few trends we can’t ignore. With how expansive and fast-flowing this information is, it’s making it harder and harder to analyze and control without the help of technology. Fortunately, as the digital landscape has grown, so has the availability of powerful artificial intelligence. If this continues, it’s safe to say that big data will always be put to good use.

To learn more, check out our big data marketing article.