But what is it that makes data, well, “big”? After all, data isn’t exactly a new thing. What’s new is the unprecedented digitization of our lives, where almost everything we do leaves a digital footprint. This is largely thanks to the rise of computers, smart phones, the internet, the IoT, sensors, and so on. Think of everyday activities like shopping online, reading the news in an app, paying for the morning coffee by card, messaging friends and family, taking and sharing photos, watching the latest show on Netflix, asking Siri a question, swiping right on a potential love match…we’re all generating data all the time.
The sheer volume of data that we’re creating, and the rate at which that volume is accelerating, is so vast that 90% of the data available in the world today was generated in the last two years.1 What’s more, every two years we’re doubling the amount of data we have available.2
How much data are we talking about? Well, we’re no longer talking about data in terms of gigabytes. These days, we’re talking about terabytes (just over 1,000 gigabytes), petabytes (a little over 1,000 terabytes), exabytes (roughly 1,000 petabytes), and zettabytes (approximately 1,000 exabytes). According to market intelligence company IDC, the amount of data in the world could grow from 33 zettabytes in 2018 to 175 zettabytes in 2025.3 To put that in perspective, if you stored 175 zettabytes on DVDs, you’d have a stack of DVDs so big it could encircle Earth 222 times! And the amount of data we’re generating is likely to accelerate further. In other words, big data is only going to get bigger.
Our ever-increasing digital footprint has also given rise to another interesting aspect of big data: the fact that there are many new types of data that can be analyzed. We’re no longer just working with numbers in spreadsheets, or entries in a database; today, “data” includes photo data, video data, conversation data (i.e. asking Alexa to play a certain song), activity data (such as browsing online or swiping left or right), and text data (like social media updates). Increasingly, the data we have to work with is unstructured, which means it can’t be easily classified into neat rows and columns, like in a spreadsheet. This unstructured data is more challenging to analyze – which is a major problem when you consider that data is pretty much useless unless we can find a way to extract meaningful insights from it.
This is where the augmented analytics part comes in. Handling masses of data can be an expensive, time-consuming, and highly specialized task. In other words, there are some serious barriers between the data itself and the ability to turn that data into actionable insights. Augmented analytics is about breaking down those barriers and making it easier to generate amazing insights from data.
In a nutshell, augmented analytics involves using AI and machine learning (see Trend 1) to automate analytics processes, including gathering data from raw data sources, preparing and cleaning that data, building unbiased analytics models, and generating and communicating insights to those who need them. What’s really exciting about this is it makes it easier for people to interact with data and extract the information they need, without the involvement of data specialists. So, in theory, with an augmented analytics tool, a non-tech expert could simply ask the system a question – like “Which of our employees are most likely to leave in the next 12 months?” – and the system would automatically generate a response.
Gartner predicts that by the end of this year, 40% of data science tasks will be automated,4 meaning augmented analytics is on track to become the leading analytics method of the future. As the trend really takes off, it’s likely we’ll see many more specialized augmented analytics apps and tools designed for specific industries in the future. This is good news for businesses, since augmented analytics provides a way for organizations of all shapes and sizes to handle the vast amounts of complex data they’re inundated with and give people in the organization easy access to analytics and insights from data. This wide access to data and insights is known as data democratization.
How Is Big Data and Augmented Analytics Used in Practice?
Now might be a good time to mention that, personally, I prefer the term “data” to “big data.” The “big” implies it’s the sheer volume of data that’s really important. But equally important, if not more, is what we do with data. And, boy, can we do impressive things with data these days. Data, coupled with other trends like AI, is transforming our world – it’s helping to making our homes smarter (see IoT, Trend 2), physically augment humans (see Trend 3), and build the smart cities of the future (see Trend 5), and that’s just for starters. Data is also changing the way we do business.
Let’s look at the main ways in which businesses can leverage data (big or otherwise) to their advantage.
Informing Business Decisions
Making better business decisions is absolutely one of the top priorities for most of the clients I work with. From how to hire the right people and target the right customers, to how to boost revenue, success means making the best decisions for your business. With data, you can better understand what’s happening in the business and the wider market and predict what might happen in the future – information that’s critical to good decision-making. Therefore, across every business function, data can and should be used to make smarter business decisions.
In one very simple example, US restaurant chain Arby’s discovered that its renovated restaurants made more money than its unrenovated restaurants. Based on this knowledge, the company decided to carry out five times more restaurant remodels over the course of a year.5
Better Understanding Customers and Trends
The better you understand your customers, the better you can serve them. Sales and marketing activity is often based on past sales history – effectively, which customers previously bought which products or services. But, thanks to big data and augmented analytics, this activity is increasingly becoming more predictive. In other words, companies are now confidently and accurately anticipating what customers will want in the future. Netflix predicting what you might want to watch next is one simple example of this.
In another example, German retail company Otto discovered that customers are less likely to return items when they arrive within two days, and when they receive all their items at once, rather than in multiple shipments. Hardly earth shattering – keeping goods in stock and shipping efficiently makes good sense. However, Otto is like Amazon in that it sells products from many, many brands, which means stocking and shipping products all at once is a major challenge. So Otto analyzed the data from 3 billion past transactions, plus factors like weather data, to build a model that could predict what customers would want to buy in the next 30 days. Not only could the system do this, it could do so with 90% accuracy.6 Now, the company can order the right products ahead of time and, as a result, product returns have been reduced by over 2 million items a year.
Delivering More Intelligent Products and Services
When you know more about your customers, you can give them exactly what they want: smarter products and services that respond intelligently to their needs. This has given rise to a wealth of smart products, such as smart speakers, smart watches, even smart lawnmowers. For plenty of examples of smart products and services in action, circle back to Trends 2 (IoT) and 3 (wearables), or turn to Trend 18 (digital platforms).
Improving Internal Operations
Every business process and every aspect of business operations can be streamlined and enhanced, thanks to big data.