By 2015, companies will spend nearly $17 billion on Big Data technologies, and the total Big Data market is predicted to approach $50 billion by 2017.
The implications of those massive investments are still being sorted out, but thanks to predictive and prescriptive analytics, we confidently can say that Big Data is well-positioned to deliver a compelling ROI.
But before you dive head first into this new world, you need to get a handle on Big Data and its applications. Here are five principles regarding Big Data that successful companies understand.
1. Companies that embrace Big Data will outperform competitors in virtually every financial metric
Mastery of Big Data applications will enable you to create a significant competitive advantage in your marketing programs. According to Gartner, companies that invest in Big Data more than their competitors do outperform those competitors by 20% in every major metric.
Big Data and data science help you uncover new patterns of customer behaviors and identify the segments most likely to buy that product or service at that time. And Big Data applications don't just highlight a goal or destination. They give you prescriptive instructions—integrated with your enterprise applications—with step-by-step instructions (often automated) to help you reach your new goals.
For instance, suppose a major distributor has 100,000 customers. With Big Data applications, the chief marketing officer can apply data science to predict which of those customers are most likely to buy in the next 30 days. That's an indispensable advantage leading to better sales growth and increased market share.
2. Big Data is ideally suited to drive sales growth through more effective marketing
Properly structured, Big Data applications can have value almost anywhere in your operations. If you're investing in Big Data, tying the initiative into applications, processes, and activities that improve marketing effectiveness and drive sales growth is essential.
For instance, you can segment customers and look for outliers against comparable customers. You can look for the subtle signals that are solid predictors of churn. And most importantly, you can take action.
Money is not made from navel-gazing insights or brainstorms. Money is made on the front lines when your sales team engages with customers and prospects. Big Data applications help you identify the right prospects and present them with the right offers and prices that have the highest probability of closing a successful transaction.
3. Big Data is about connected data
Marketing makes connections—but how do you make those connections in today's complex markets? The overlooked truth about Big Data is that what we really want is connected data. When we think about promotions, campaigns to build market share, lead-generation programs, pricing changes, and other marketing disciplines, we want and need the ability to piece together multiple, disparate data points to identify patterns that help marketers predict outcomes and prescribe actions.
Doing all that is not possible if we only accumulate vast stores of data without the right applications to bring it all together. It's not unlike the Moneyball philosophy that has taken hold of professional sports. For years, old-school baseball scouts relied on a few basic metrics to evaluate players. Today, the explosion of statistics in baseball helped many teams find skilled players whose talents may have previously been overlooked.
In the fast-changing business world, relying on the same metrics that were used five years ago may be unwise. The world changes... and marketers need ways to stay abreast of those changes.
4. Actionable insights for marketing yield better outcomes
The unpleasant fact: Big Data is a Big Waste if you can't use it to execute actions that lead to better outcomes.
Big Data alone isn't enough. What's needed is more than a chin-stroke-worthy set of slides showing analytical trends at a board meeting—which may lead to actions that improve the business. That's merely first-generation analytics, which is rapidly becoming a basic commodity.
Those analyses won't yield even one dollar of return if they're not tied to execution. The value is achieved when Big Data analyses are actionable and tightly tied into execution applications. That means more than data-sharing; it requires functional integration. By definition, a Big Data application lets you execute—not just analyze.
5. You need data science, from insight to outcome
Big Data science outcomes can only be derived from Big Data applications thoroughly rooted in data science. Data science—the sophisticated statistical routines and data-mining algorithms that uncover the hidden, statistically significant correlations and patterns—elevates Big Data applications from "reporting on steroids" to catalysts for improving your business.
For instance, with data science, you can sift through your billions of data points to uncover the top 100 (or 1,000) prospects with the highest probability of buying your highest-margin product in the next quarter.
Data science also dispels myths and erroneous assumptions.
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The opportunities in Big Data are too compelling for any business to ignore. By blending Big Data and data science into execution applications, companies can outperform competitors and create significant advantages by selling more product, optimizing prices, reducing attrition, identifying high-value prospects, developing winning offers, and closing deals faster.
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