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Big Data: Debunking the Buzzword

You need to ensure your big data is working for you.

Big data: There’s a lot of uncertainty around the term. Type it into Google, and more than half of the organic first-page results have titles starting with “What is big data?” And in the “People also ask” box, Google tells us that top questions are “What is big data with examples?” “What is big data technology?” and “What is considered big data?”

So, let’s stop right here and ask a question. I’ll even bet you already know what it is.

What Is Big Data?

Big data refers to aggregated data sets whose volume is so massive and that are so complex that traditional data analysis and tools aren’t able to manage it effectively. Big data is made up of multiple data input streams: the profile and use of big data would be different for a healthcare organization than an investment firm, for example. But both benefit greatly from a deep dive into the relevant data available to them and analyzing it to see what insights and prescriptive analytics can be drawn from it.

In order to better understand big data, we can turn to an easy-to-use framework: the five Vs.

The 5 Vs of Big Data


There is an incredible amount of data generated every second of every day — 2.5 quintillion bytes each day! And as technology advances, so does the amount of data we generate. In fact, 90% of the data in the world has been created in the last two years alone.

  • As of 2019, some estimates put the number of CCTV cameras in London as high as 627,000. That means in London alone there could be over 54 billion seconds of CCTV footage recorded every day!



As technology advances, the speed at which data is transmitted grows. A tweet can go viral and have immediate global impact, seen almost instantaneously by millions of people.


One of the biggest challenges in working with big data is dealing with the many different types of data that come in from so many different sources. And most of this data is unstructured, which doesn’t fit neatly into tables for storage.

  • Thinking about gathering consumer data from social media? Be prepared to store pictures, geotags, timestamps, tweets, likes, videos, follows, impressions, clicks and search history — and that’s just a start. Turning that mess of data into insightful information is where the real work begins.



What good is data if you can’t trust it? Ensuring the integrity of such massive amounts of data is no small feat and requires systematic processes to automate a lot of the monitoring. But investing in these types of processes can in turn help overcome the concern of untrustworthy data; governing a complete data set eliminates the chance of biased representative sampling or the fear that suspicious data will slip through the cracks.

  • Have you heard of “fake news”? Troll factories? (Not the kind making the little dolls with spiky neon hair.) When you rely on data to inform your decision-making process, you want to ensure it’s accurate and reliable.



The last of the five Vs. Arguably, this is also the most important of them all. You can store immense quantities of data. You can capture it in real time. You can process it from multiple sources of input. You can even verify its accuracy to a great extent. So what? What’s the point of having it if it doesn’t create value for your organization?

  • You need to ensure your big data is working for you. It costs a lot to hire top-notch data engineers and invest in machine learning tools. You’re going to pay to store all the data you collect, and with the volume we discussed, an existing architecture may not be cost-effective. Not to mention the necessary changes to day-to-day workflows and processes. So you’d better make sure you’re taking that data and turning it into a value-add resource.


So is it worth it? Let’s take a practical look at how big data can provide immense value to an organization.

Big Data Analytics in Action: McDonald’s

In the first quarter of 2019, McDonald’s spent $300 million to acquire Dynamic Yield, a startup firm using big data to implement decision logic (personalized recommendations). The specifics of how the firm’s technology will support the Golden Arches aren’t fully known, but there’s plenty of room to speculate how it could be implemented.

You pull into the drive through to order dinner. A camera captures your license plate number and associates it with your registered loyalty account. That information gets transmitted to the digital menu board up ahead, and when you pull up, the specific offerings change from what the previous car would have seen.

You can, of course, order what you want. But for your benefit, the menu now highlights a deal tailored just for you. The system connects your license plate with your most recent order, which included a Big Mac and a Caesar McWrap — guess which two entrees the menu board suggests for you today? When you order three Happy Meals, a small coffee or an iced Frappé is recommended as an add-on to give you the boost you need to finish out your day.

Or maybe you go inside to order. You have the McDonald’s app on your phone, and location tracking is built in to the app. Before you walk through the door, an algorithm calculates what your order might be and what to upsell you. The digital kiosk could have geofencing capabilities, so when you (and your phone) move within a specified distance, it recognizes you and your account, provides offers tailored specifically to you and lets you reorder your most recent purchase with one click.

Going beyond customer personalization, maybe McDonald’s will apply big data analytics to inventory management. Is there fish in the freezer that’s been a slow seller? Maybe the menu boards will offer a two-for-one deal on Filet-o-Fish sandwiches. Does historical data reflect a correlation between weather and temperature and what (and how much) people order in an area? In a busy period, are there entrees that are faster to prepare in order to speed up the flow of customer traffic?

Time will tell how McDonald’s implements Dynamic Yield’s technology, but one thing is sure: even from an outsider’s perspective, the sky’s the limit for applying big data to business.

Put Big Data to Work for You

Big data can drive big results. And as you can see, big data is quickly becoming the only type of data there is. You have sales data, consumer data, operational data. Utilizing all of that well means breaking down silos to get it into the hands of the business in an easy-to-use format, such as dashboards, unified views, and self-service analytics platforms. Maybe you already have a couple of tools to make use of the data you’ve collected. But are there additional data sources, outside of your transactional systems, that you may be missing out on?

Big data brings big complexity, too. As I said earlier, just having it isn’t enough. In fact, just having it probably creates more of a liability than a benefit for your company. You need a strategy for data unification. You need analysis that is not just descriptive but predictive and prescriptive. You need to actively draw value out of your data.

At Zirobi, we help our clients use tools like streaming analytics and machine learning to understand big data and use prescriptive analysis to guide their business. Starting down the big data path is a lot like mining for gold — it can be dark and dirty, but there are hidden nuggets of incredible value just waiting to be uncovered.

Reach out to us today — we’ll help you find the treasure buried in your big data.

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A full-funnel, fun-loving marketer, AnnMarie uses data findings to craft marketing strategies for every stage of the customer journey. She’s seasoned in customer segmentation, digital media buying, lead generation and content strategy development, using her knowledge to help ensure a fantastic customer experience for all.

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