Today, I want to tell you about what is increasingly becoming one of my favorite sectors as a small-cap investor: big data stocks. But first, let me tell you a big data-related story that involves a bunch of college kids successfully predicting the 45th President…
How Did College Kids Predict a Trump Victory?
About a year ago, a group of college kids were given 20 hours to figure out who would win the 2016 Presidential election, Hillary Clinton or Donald Trump.
They were given piles of big data from numerous sources and in various formats, including PDFs, Microsoft Excel files, web content and sentiment data from social networks.
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After their 20 hours were up, their big data analysis showed how Trump could win. He only needed a relatively small group of Clinton supporters to vote for a third-party candidate. The data showed this wasn’t all that unlikely given that her base was prone to shifting their opinions on the democratic candidate.
How the heck did these college kids lay out the case for a Trump victory when the professional forecasters got it so wrong? How did they do it with so much disparate data in so little time? And how does their experience apply to investing?
I don’t know why the professional forecasters were so far off. But the answer to the second question is that they were given two and a half hours of instruction on how to use two big data analysis platforms, IBM Watson Analytics and another one that I won’t mention (yet). Even with so little instruction, these platforms helped them reach a conclusion by rapidly accessing, blending and analyzing disparate types of data.
The answer to the third question is that, as you’ve already seen, a good data platform can give people powerful, actionable insights. That’s made data platforms valuable assets for companies across all industry segments—and has led to rapid growth for several publicly-traded big data stocks.
Before figuring out which big data stocks to buy (and which ones to avoid), you need to have a basic understanding of the big data market.
Data is Power, If You Can Analyze It
Research shows that analysts spend up to 80% of their time just collecting data from various sources, such as the web, PDF files, income statements, log files and text reports, then getting it ready to analyze.
That’s a lot of time. And it’s time not spent making important decisions based on what the data shows. A good data prep platform gives analysts more time to do actual analysis, uncover important business insights and help their organizations make better decisions.
What kind of data is out there?
At one extreme, we have structured data that’s well organized. The best example that comes to mind is a public company’s income statement, which likely resides in an enterprise resource planning (ERP) system from a provider like SAP (SAP). This data is relatively easy to search through and analyze.
At the other end of the spectrum is a rapidly growing pile of data that is not very well organized, called semi-structured or unstructured data. Most comes from outside of an organization where the method of collection is beyond its control. The college kids I mentioned earlier worked with a lot of semi-structured and unstructured data.
Think about how data is used by companies that sell consumer goods, like Nike, The North Face and P&G. Ultimately, they all want to sell the right products to the right people in the right locations.
The right software will continuously access consumer-generated data, including mobile payment apps, electronic coupons, point-of-sale logs, e-commerce user clickstreams and social media feeds. A lot of this is unstructured.
It will then blend that unstructured, consumer-driven data with an organization’s own structured data, which typically includes product-level information, inventory and supply chain data.
The next step is to blend and clean the data to uncover relationships, correlations, and ultimately, opportunities. Then managers can act on the insights to better achieve the goal: put the right products in front of the right people at the right time.
Big data platforms are currently used a wide variety of industries. Banks use them to connect to core systems like Fiserv (FISV), Oracle (ORCL), SAP and Jack Henry (JKHY). Credit unions use them to detect fraud and manage risk. Health care institutions use them to enhance patient outcomes and improve the quality of care. And, as I just illustrated, retailers us them to gain a holistic view of their operations and seek competitive advantages.
Different types of companies will require different types of features from their big data platform. And when you start breaking the market down, you see different areas growing faster than others.
For instance, industry data suggests the traditional data prep market is growing at 8%, the self-service data prep market is growing at 17%, the streaming data analytics market is growing at 35% and the Cloud Analytics-as-a-Service market is growing at 38%.
Most big data providers offer access to more than one of these markets. While it’s a competitive industry, there are also a lot of strategic partnerships being formed as the industry shifts from reliance on products to reliance on platforms.
You can gain exposure through large software companies that sell traditional business intelligence products. These include the usual suspects: IBM (IBM), Microsoft (MSFT), Oracle and SAP. Then there are the mid-cap companies that are a little more focused on particular areas of the big data market, including Tableau (DATA) and Splunk (SPLK). Then there are more pure-play positioned companies that I find most intriguing, including UK-based First Derivatives (FDRVF) and Alteryx (AYX).
My Favorite Big Data Stock
My favorite big data stock is a small cap. Since I just recently recommended it to Cabot Small-Cap Confidential subscribers, I can’t talk about it by name yet. But I can tell you a little about why it caught my eye, and let you know how to get access to my research.
The company’s software is used by over 10,000 customers, including nearly all of the Fortune 100. It’s used extensively by financial services companies.
Customers are able to access, blend and analyze disparate types of data quickly and easily. The software pulls almost any type of data, including structured, unstructured and semi-structured data, from a wide variety of sources and formats, including ERP systems, reports, PDF files, excel files, web sites, point-of-sale terminals and real-time streaming data terminals.
Typical types of data being analyzed include sales reports, inventory, invoices, balance sheets, customer lists, equity trading logs, loan data and more. In the end, analysts have visually rich analytical applications, which, in turn, are used to dynamically discover the key factors influencing their companies’ operations.
In short, the company has powerful software. I like it because it’s relatively unknown, growing by over 20%, and has a lot of loyal customers. I also like that it’s a pure-play big data stock—it will either succeed or fail based on how it does in this market. And it has forged strategic partnerships with many larger companies that could eventually buy it out. Most importantly, I think the stock will go up!
Tyler Laundon is chief analyst of Cabot Small-Cap Confidential and Cabot Early Opportunities. The circulation of Small-Cap Confidential is strictly limited because the undiscovered stocks with sky-high-potential that Tyler recommends are often low-priced and thinly traded. Don’t share these recommendations!Learn More