Ready to harness the power of big data and big data analytics? Before embarking on your big data journey, let’s make sure you’ve got the foundation for success. In this post, we’ll cover how to map your data, understand your data, how to use your data, and the value you’ll get from your data. So, without further ado, let’s get started!
What is big data analytics?
The word “big data” has been around since the 1970s, but it gained traction as a marketing buzzword during the late 90s. It gained popularity among businesses for being somewhat short and descriptive, and it’s now the lingua franca for marketers, business analysts, and data scientists around the world. The definition of big data varies from one person to another, but for the purposes of this post, we’ll use the definition from Wikibon, which is, “large, complex datasets, collected over months or years, that enable the analysis of information to provide useful insights about consumer and business behavior.” In my own co-authored research, big data refers to (check out our publication here: Suoniemi et al., 2020):
techniques, technologies, systems, practices, methodologies, and applications related to the acquisition, storage, integration, analysis, and deployment of massive amounts of structured and unstructured data to support business decision-making.
Big data analytics is basically analyzing this big, complex data set and extracting insights from it.
How to map your data
There are three parts to any analysis:
- Getting the data
- Understanding your data
- Cleaning your data
No matter how much expertise you have in the field, if you don’t have the right data, there will be issues with the results. You can’t even begin to perform any analysis until you have access to the right data. Your first step is to obtain the data you need. Depending on the size and scope of your problem, you may also need to get access to the raw data and some form of quality control in order to ensure accuracy. Step one involves gathering raw data. Where is your data coming from? If it’s already been collected, such as a Google Sheet, or a file on the web, such as images, CSV, and more, you’re already in the right place.
How to understand your data
Data, at its simplest, leads to the information that tells you how something works. It can be heat maps, documents, spreadsheets, demographics, crime, DNA, the War on Terror, even a blog post. You’ll want to make sure your data is structured in the best way possible, allowing you to make intelligent, meaningful inferences from it. A big problem with data — whether it’s online data or information about the city you live in — is that it’s rarely sorted and tagged as explicitly as you’d like. Oftentimes, this is because the companies that collect the data don’t know what you want. At the most basic level, they don’t know the way that you want to manipulate the data. As a result, they can’t package it in a way that makes it easy to find.
How to use the insights gleaned from your data
Not all data is created equal. It’s important to know how you should act upon your insights, as well as the basic steps for doing so. There are many different ways to use your data. The goal is to be pragmatic. The best analytics don’t require an enormous amount of processing power. In fact, they may not even require your data. For instance, if you wanted to conduct social media analysis on your customers, all you’d have to do is create an account with Twitter, and start tracking keywords. Take this example: Your blog is receiving lots of social shares. You also notice an interesting pattern. Your blog posts generate a high number of “glitches.” You decide to analyze this trend for your social media campaigns.
The value of big data analytics
Over the past few years, there has been a marked increase in the amount of data generated. Technology has evolved from storing and transmitting just bits to storing and transmitting bits, data, and more data. Today, we find ourselves with data that’s often a billion times larger than anything we could possibly hope to fit on a single hard drive. This, of course, poses a significant challenge for those who are looking to process this kind of data and turn it into value for their organization. And here’s the problem: With so much data being created and stored every day, it’s hard to stand out. But, with all the data flowing in, turning that data into insight is increasingly difficult and time-consuming.
Conclusion
As we’ve seen, there are many kinds of data and a variety of analytical methods that are available. However, to really make an impact, you’ll need to combine all these methods to produce an actionable set of insights that is truly useful. Understanding your data and getting insights from your data requires more than just aggregating, aggregating, and more aggregating. It requires you to understand your data and how to use it. So, let’s do just that!
The Marketing Analytics Academy‘s mission is to get you started on data analytics, its methods, and key concepts in various fields of marketing. Start your data analytics journey today and check out the online courses!