Patrick Schwerdtfeger is a motivational speaker who can speak about monetizing big data and executive strategies for predictive analytics at your next business event. Contact us to check availability. The full transcript of the above video is included below.
Full Video Transcript:
Hi, and welcome to another edition of Strategic Business Insights. Today we’re going to talk about big data, which is revolutionizing business as we know it. It’s going to revolutionize the way services are delivered for the next years, decades.
So let’s start with a definition. For those people who don’t know, there are organizations all around the world that are accumulating a colossal amount of data. Think for example about the stock market, all the different price volume action of all the different stocks, commodities and ETFs that are being traded every minute of every trading day. Think about weather. There are satellites out in space that are circling the earth and they’re looking at the earth and they’re monitoring data 24 hours a day. Huge, huge quantities of data. Think about banks. Banks are accumulating enormous amounts of data about the spending activity, credit card activity.
Think about a Safeway card or some sort of a club card that you have when you go shopping. These are all programs and things that are generating an enormous amount of data. Think about medical—medical records online, digital medical records or pharmaceuticals across an entire country like the United States, or in fact dozens of countries like in Europe—again, massive quantities of data. Think about cell phone usage, mobile phone usage. These are all examples of organizations that are accumulating an incredible amount of data.
Now, the question is, what do you do with it? What do you do with this data? There’s an enormous amount of data out there but people don’t know what to do with it, and right now a lot of these companies, even the largest companies in the world, some of them are just stockpiling, they’re just storing the data. They’re saving it on the hard drives, on the servers because they don’t know what to do with it.
Now, the first thing you need to do, and this is what big data is all about—the big data is a very, very hot topic right now. So if you’re not in the IT space you may not be familiar with it, but I promise you you’re going to be familiar with it because it’s overlapping into marketing and into all other aspects of business as well. So when you have the data, job number one is analytics. An analytics is people who can look at that data and start looking for patterns, start looking for things that they haven’t missed. At the end of the day, we’re looking for wants and needs that we haven’t even noticed yet.
For example, there was an academic study that was done where they looked at cell phone usage by intersections across an entire city over a period of time, and what they found were enormous spikes coming off of certain areas of the city at certain times. And so they could reverse engineer and find out what was causing all those spikes in cell phone usage, and that actually helped them to realign their public transportation networks. So you can see that the data that’s coming in might be coming in from one use, but when you analyze it you realize that you can use that data for an entirely separate thing. So maybe you can use the data to deliver new services that you’ve never provided before, which are highly intuitive to the actual wants and needs of your customers or the people who you have data for. In many cases, you might be discovering a want or a need that no one’s ever known it even existed yet.
But the other thing is that you might data that you realize you can sell to another type of business who could use it in their business. So now you’re sitting on a resource in that data that you didn’t even know you had. So, for example, maybe a hotel is accumulating data, like say Marriott or Hilton. I have no idea what they’re doing. I’m just postulating in my head. But they could be accumulating data across all of their hotels and the activities of people who are using those hotels, and they might realize that there’s a need that they can’t service themselves but maybe they can sell that data to a company that can service that need.
I’m not saying that 100% of businesses can take the data and use that information themselves to deliver new services, even though in many cases that’s the case, but there’s also the possibility that you have a resource in that data you can end up selling to somebody else, and this data analysis and analytics can completely revolutionize your business and even the entire industry that you might be a part of. Big data is extraordinarily disruptive because it ends up revealing wants and needs that nobody ever knew existed.
Now, there are basically three steps in this process. The first is data accumulation. You have to accumulate the data, and tons of people are already doing that. The second is analytics. You have to analyze that data to look for those patterns and look for those anomalies where you can realize that there’s a want and a need there that maybe hadn’t been exploited in the past.
And the third step is algorithms. Algorithms is where you take the data and you crunch it into some sort of a formula which spits out some sort of a service that you can sell. Like think, for example, in the stock market, the data can reveal what’s called arbitrage opportunities, which is what a commodity or let’s say some sort of a tradable stock or whatever, commodity, might be trading for one price on one stock market, say in Chicago, and a slightly different price in a different market, say in New York. And so because there’s a slightly different price on the two different markets, you can exploit that—it’s called arbitrage—and sell at the higher price, buy at the lower price, until those prices equate again.
And there are algorithms in trading houses all across the country and in fact all across the world that are looking for arbitrage in the pennies. The stock markets are actually much more efficient today than they were before just because all of these algorithms which are processing the data and spitting out trading information to trade the stocks, buy this, sell that, they’re doing that 24 hours a day in stock markets around the world, and so it’s actually made the stock market more efficient.
And there’s even an algorithm, if you can believe it—in many cases there are sporting games like soccer games or football or baseball or what have you taking place all the time, and there are algorithms where the actual data from the game, so the people who scored, the people who had assists or whatever, there was some really interesting thing that happened, that’s all data. That’s input data. And that data can generate an actual sports journalistic update at the end of the first quarter or at halftime, which it can spit out in English, whatever language, but obviously we’re here in the United States. It can spit out a paragraph that describes the game in perfect English. It’s actually driven by an algorithm. No human being ever actually wrote that paragraph. But the algorithm has all the English characteristics, different phrases that are used, and you can just put the data in and it can kick out a paragraph, which is why in many cases a lot of these sports updates that go into news feeds all the time, they come out so quick when that game is in progress and you think to yourself, “How in the world did something write that?” Nobody wrote it. That’s a computer program that generated that paragraph. It’s an algorithm.
The same is true for weather. These weather models that people, the people on TV, meteorologists who are predicting the weather, they’re not predicting the weather themselves. They’re relying on models which are processing data and then spitting out implications of that data in the form of an algorithm. It calculates what’s likely to happen with the wind patterns, high pressure, low pressure, all those things which I don’t know anything about. But the algorithm knows all those patterns and it makes those calculations and it kicks out a service which you can monetize, because people, these meteorologists, they have licenses to these models. They pay a fee to have access to those models. So all of a sudden you’ve monetized the data through the algorithms.
So there’s one overriding implication of all of this in the big data space: IT, information technology, has historically been a support function within a company. In other words, picture the C-level managers, CEO, CFO, COO, all the top-level managers, the executives of a company. They decide the strategy of the company, and then they take that strategy and they give it to the support functions to facilitate, including IT. So they have a strategy to do this or that and the IT department is there to help facilitate that strategy. But that’s a supporting function. The strategy is developed here, and then it’s given to them to support.
That is completely reversed through the big data process. Now, I’m not saying it’s happened already. Some companies are way ahead of the game and some companies are still way in the back. But what’s going to be happening over the next few years and decades is that the IT department, which is the department that owns that data, is going to end up being the strategic helm of the company. It’s going to guide where the company goes in the future. The IT department is going from a support function to a strategic function because the answers are in the data.
The future of your company, the future of your product, your services, your revenue, your profits, where in the country you deliver services, where in the world you expand into other countries, all of those answers are in the data. The data is where those answers are coming from. So the strategic role of big data is something that’s completely revolutionizing the role of IT within a traditional company, and we’re going to see in the years ahead a really interesting transformation where the data drives the ship rather than IT being a support function in the back of the ship or in the engine room.
So that’s big data. It’s an exciting time. It means that we’re going to be seeing a lot of products and services that are very, very closely attuned to what it is we’re actually looking for as human beings. That means better products, better services, most likely at a cheaper price or a better value one way or another. It’s going to be an exciting time.
I certainly appreciate you watching this video. This is Patrick reminding you to think bigger about your business, think bigger about life.
NOTE: Patrick has a keynote program entitled Monetizing Big Data which he updates regularly with new case histories and offers at business conventions and conferences around the world.
Patrick Schwerdtfeger is a keynote speaker who has spoken at business conferences in North America, South America, Europe, Africa, the Middle East and Asia.