The Rise of Big Data and Big Analytics, and the Inception of Return on Investment
If you’re in business — particularly in marketing, advertising, web development, sales or another other digital venue — then it’s safe to assume that you spend time on the internet reading. If you’re like me, you might tend to skim over terms that you don’t have the time to delve into.
I’ve done that, and I’ve also been caught off guard when someone asked me to explain what they meant. In my case, those terms were Big data and Big analytics. It was a little embarrassing, but I muddled my way through.
Big data and Big analytics are words that tend to appear in articles and documents that quickly get overrun with tech heavy terminology. This causes many people to skim or to skip reading the documents entirely (like I did).
As my job has grown and changed over the last 12 months I have actually learned quite a bit about them. I’m ready to share a little wisdom.
What Big Data Means
Big data (or Big Data) is a general term used to describe the amount of structured and unstructured data floating around the webosphere and throughout other digital mediums.
It’s the data that comes from everywhere: weather sensors, social media posts, digital picture and video uploads, purchase transaction records, GPS signals, website clicks, any number of text files that are shared digitally.
Big data is true to its name. It’s data that would take too much time and cost too much money to load into a relational database for analysis. Its more data than Microsoft, IBM, and Oracle can handle alone.
How Much Data Exists
Although Big data doesn’t refer to any specific quantity, as the amount of unstructured data tends to be variable. The term, however, is often used when speaking about petabytes (1 quadrillion bytes, or 1X1015) and exabytes (1 quintillion bytes, or 1X1018) of data. There’s enough data in one exabyte to fill more than 20 million four drawer filing cabinets with text.
Every day, we create 2.5 quintillion bytes of data. According to the tech folks at IBM, we create so much data that 90% of the data that exists in the world today has been created in the last two years alone.
Why Big Data Matters
A primary goal for looking at big data is to become more perceptive. When you analyze new data types together you can often find new patterns, trends, and insights. These trends give you the opportunity to expand and grow your business.
Big data keeps your business agile, modern, and gives you the opportunity to answer questions previously considered unanswerable. Ultimately, big data gives you more opportunity to engage your business or brand audience on a much deeper level, making seemingly impossible interactions probable.
What Big Analytics Means
Big data analytics is the process of examining the Big data that we produce each day to uncover hidden patterns and unknown correlations. Such information can provide incredible advantages over competitors and result in more effective marketing and increased revenue.
The primary goal of Big data analytics is to help companies make better business decisions. Traditional business intelligence programs only analyze traditional transaction data like financial orders, invoices, payments, activity records, storage records, and other logistical information.
Big data analytics takes the analysis one giant step further by scrutinizing that transaction data alongside all the unstructured data that can be collected by a business or brand.
Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines (re-enter Microsoft, IBM, and Oracle). But in many cases, unstructured data sources do not fit in those traditional data stores. This has led to the creation of a new class of technology used in many big data analytics environments, such as NoSQL databases, Hadoop, and MapReduce.
How you can use Big Data and Big Analytics
Big data has been a hot topic lately and rightfully so. Sound measurement of unstructured data has been hard to obtain for the last 5 years. We haven’t actually been accessing all the information needed to understand day-to-day digital interactions. Marketers have tried to make up for the lack of insight by justifying transactions and conversions with, what I call, minute data; for example, determining the return on investment for spending in social by measuring followers and likes.
These minute data points create inaccurate measurements and don’t fulfill the original purpose of determining the true return on investment. Whatever we were measuring before no longer matters. Why? Because neither you nor I actually understand the transactions that took place to lead to that social conversion; that new fan on the company Facebook page. How did they get to the page? Had they been there before? How many days overlapped between their first visit and their decision to become a fan? You don’t know the answer by measuring minute data, and neither do I.
Big data and Big analytics offer all of us a chance to understand the true consequences of our actions online, and Big analytics marks the inception of true return on investment. This is the first time we’ve ever had the tools to identify, measure, and manage what impacts our brand and business decisions.
The folks at IBM tell us that 1 in 3 business leaders don’t trust the traditional information they use to make decisions, meaning they can’t (or shouldn’t) act upon that information. This is an incredible obstacle when you consider that today, the difference between being a true thought leader and being second best can be as simple as posting a YouTube video before your competitor. Today, you really can be two minutes too late.
Simply put, if you ever plan on making intelligent, fact based decisions about your business or brand you’re going to have to start making use of Big data and Big analytics.
If any of this seems overwhelming, relax. Big data has given us a chance to discover new ideas and interactions. We’re only now truly beginning to engage with each other. And isn’t that exciting?
Author Bio: Elizabeth Miller is a native Texan and an [A] Intelligence Agent at Simple [A]. She specializes in content marketing and communications, brand marketing, community management, and analytics. Elizabeth is very active on personal Twitter account and is always looking for new friends to discuss ideas about content marketing, social media, SEO, and any digital related topics. You can find her there at elizabethanm or on her website.
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