Advancing communications measurement and evaluation

Big Data vs. Small Data: What’s the Difference and How Can You Use This Information?

If communications and technology blogs in recent years are any indication, big data is an important emerging business trend that professionals need to understand.

But what do we mean when we say big data?

And what about small data?

Does that matter?

Data sets, regardless of their size, are important tools for communicators. They offer insight about your customers’ behavior and satisfaction, efficiency and success of business functions, and so much more.

Data can improve the decisions communicators make and the stories they tell. Data, especially small data, has become more accessible for organizations to collect and manage, so it’s key to leverage this information to gain the most insight for your organization.

It’s important to know the differences between big and small data, as well as to understand how you plan to use this information to improve your business.

What’s big data?

Although the term “big data” gets used often, a set of information must meet certain requirements to be considered actual big data.

At its most basic level, experts define big data as sets of information unable to be processed by human minds or common processing applications, due to the volume and complexity of the data.

Big data can be categorized into three different classifications, structured data, unstructured data, and semi-structured data.

Structured data, which comprises 20 percent of all existing big data, includes data saved in databases. This information, created by both humans and machines, is used often in programming. Human activity, such as entering personal information to web forms, comprise the majority of human generated structured data. Machine generated structured data comes from sensors, web logs, and financial systems.

The remaining 80 percent of data exists as unstructured data, or data not organized into the traditional rows and columns format. Similar to structured data, this data can be categorized as human or machine generated. Mobile activity, website content, and social media data fall into the human generated category. Machine generated data includes satellite and radar data.

Semi-structured data, the third and final kind of big data, includes information with some kind of organizational logic but lacking the traditional row and column format.

Big data tends to require a data specialist or outside assistance to process the information. With proper knowledge, however, big data provides key insight to streamline business and communications decisions.

According to a study conducted by NewVantage Partners, leading Fortune 1000 companies use big data in a variety of effective ways, namely cost-cutting measures. The leaders at these companies also named finding innovative solutions, adding revenue, and creating new products and services as leading advantages of leveraging big data.

The Point Defiance Zoo & Aquarium in Tacoma, Washington, for example, layered years of historical weather data with years of corresponding data from their point of sale system to predict future attendance based on the weather for a particular day. The insight gained from the comparison of data allowed them to make decisions about staffing and supply distribution, allowing them to cut costs.

Provided your organization has the resources to hire an outside analytics team, or if it has in-house analytics experts, consider accessing open data files, such as sets on public safety, climate, and education, as these are becoming increasingly available through local governments. Layering this data with data you have about your business, as the zoo did with their attendance data and historical weather data, provides insight that allows you to predict and plan for the future.

So what’s small data?

Three aspects, the volume, variety, and velocity of accumulation, differentiates small data from big data. Experts define small data as sets of information that can be stored on a single machine.

Small data serves as the foundation necessary to implement big data efforts into a business’ plans, so a core understanding of small data is key before moving onto using big data.

This information allows communicators to gain a more targeted, detailed view of their business and how its customers interact with it. Smaller data sets are more accessible to the average person, who may not have experience in analyzing larger data sets.

Your business already collects tons of small data sets. Tools your communication team uses, such as your Google Analytics account, CRM platform, and media monitoring tool, aggregate this information and allow for easy review.

Select the range you want to work with and export this into a usable format, often an Excel spreadsheet, and review the information, highlighting trends, anomalies, and problems as you see them.

If, for example, you want to produce customer profiles to identify key audiences to market to, you could export your CRM database. Depending on the size of your business and its database, you could choose to export all customers or a sample size.

Review the profiles of your current customers and prospects to understand key information about them, such as where their business is based, what their job title is, and what industry they work in. Analyzing and gathering this information identifies trends that emerge from your existing customers and provides insight on key demographics you should be targeting with your communications campaigns.

Although confusing at the outset, data, like any other communications measurement tactic, provides deeper insight about business and communications decisions, allowing for better, data-driven decision-making.

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