Creating Value from Data Assets
During the last decade, we’ve watched Airbnb transition from a little-known “couch” renting service to a behemoth in the hospitality space, putting global hotel brands on notice. We’ve seen Walmart become a one-stop shop for, well, everything. And anyone who follows financial services news knows that MasterCard has driven phenomenal growth during the last few years.
[Related Article: Why Fast Data Matters for Business]
What’s one thing these disparate companies have in common?
They use their own data in smart and innovative ways to drive business decisions. In fact, they operate on the fundamental principle that data is the business. Airbnb uses the data it collects to tailor users’ search experience based on demographics. Walmart uses its own data to manage supply chain, improve the shopping experience, and inform pricing strategies; for example, by analyzing customer preferences and shopping patterns, Walmart is able to personalize mobile “Rollback” deals. Mastercard’s application of their own data has helped the company grow revenues from less than $1B in 2010 to $2.4B in 2017; in just the last two years, MasterCard stock has surged 104%, according to Barron’s.
What companies like these clearly prove is that data is both a competitive differentiator and a critical operational asset. Gartner predicts that by 2022, 90% of corporate strategies will rely on information as a fundamental enterprise asset and analytics as an equally essential competency.
The questions for companies are: How do we structure or transform our internal data to accelerate performance? How do we access and harness external data and then integrate that data in order to improve business functions? And, how do we transform into a business where effective use of data is core to every critical function and decision?
Getting Value from Data Assets: How to Find, Organize, and Use Your Data
Data can no longer be thought of as a byproduct of business processing. Instead, we must think in radically new ways about the information we can collect or access — we must think of it as an existing asset that helps us set goals and objectives. The first step is to know what kinds of information you’re sitting on. The next step is to make sense of what you’ve got. But the reality is that for many companies, there are significant functional and business unit data silos to overcome. In fact, fully 72% of companies have yet to forge a data-driven culture and only slightly more than half (52%) are actively competing using data and analytics, according to Harvard Business Review.
The stakes are significant: leading organizations in every industry are wielding data and analytics for competitive advantage. Wherever you are on the data and analytics maturity curve, take heart: increasingly more tools and technologies are available to help companies work smarter with the data they already have. And being able to buy rather than build solutions democratizes the field for companies of all sizes.
Having access to the latest AI-driven solutions that use natural language processing, entity extraction, and predictive analytics to organize and cleanse even the most complex, unstructured data in order to make it actionable. Here’s an example. Let’s say you’re a software company managing orders for bars and restaurants. Your customers might want to know the top-selling beer in the United States. And they might want to know how much other bars and restaurants in their cities charge for said beer. But that’s where things get messy.
There are, for example, five different varieties of Budweiser’s Bud Light. So how do we know if Bud Light is really the most popular beer, or if it’s Bud Light Lime? Or Bud Light Platinum. Couple that with waitstaff who record Bud Lite, Bud Light, BL Can, etc., and you can see how it becomes difficult to know what the most frequently ordered beer actually is. But using artificial intelligence and machine learning, it’s now possible to reconcile all those variations and determine popularity and pricing that is useful to bar and restaurant owners and marketers.
Likewise, a bank or an insurance company might have a database of prospects — both people and businesses — they’d like to approve for accounts, but they need to make sure all the information they have is organized correctly so they’re evaluating the right person or business. There are solutions that make it possible to efficiently tag, enrich, deduplicate and cleanse data, which ultimately makes it possible to make more strategic decisions.
[Related Article: Practical Ways to Integrate Data Science Into Your Organization]
The bottom line is every piece of data an organization owns is a strategic asset. It can help you strengthen relationships — like the software company that can tell its food and beverage customers how to price competitively or the bank that is able to sell additional products to prospects it knows are worthy. There are plenty of companies that have achieved wild monetization by leveraging their own data. Who doesn’t want to be one of those?