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New Ways to Leverage Data to Drive Business Value

Tina Liu
Director, Product Marketing

No longer the exclusive domain of tech companies or IT departments, data has become a business driver in a just about every industry and line of business. Data drives business value in two ways: It grows top-line revenue as companies create innovative new products and services — even entirely new business models. And it increases bottom-line profitability — not just by streamlining processes, but also by automating the management of assets and end-to-end supply chain through the Internet of Things (IoT). As Gartner’s Andrew White stated in a recent blog post:
 
"Data and analytics is not about a dashboard or delivery of an analytic to the point of a decision! That is so yesterday. Data and analytics is more about reimaging the decision in the context of an outcome.”
 
To look at it another way, data has become currency, and businesses are finding innovative ways to monetize it. In fact, BCG estimates that big data and advanced analytics could unlock more than $1 trillion in value annually by 2020.

Using Data to Drive Top-Line Revenue

Perhaps the most visible way that data can drive revenue is in the data-driven disruptors that have risen since the dawn of the internet, from Amazon to Uber and beyond. These companies use data to create new business models that have reshaped their industries.
 
But data doesn’t have to mean disruption. It can be leveraged by any company to target customers more accurately, gain a clearer understanding of the customer journey, and enhance the customer experience. None of these approaches are likely to upend an industry, but the better a company can process customer data the greater an edge it can gain over competitors; whether tracking online activities or using location-tracking to better anticipate and meet customer needs.
 
In addition, many companies are offering data as a service, or layering additional services on top of their data as a value-add. Even traditional manufacturers are differentiating their products with enriched data and analytics, a practice that Barbara H. Wixom and Jeanne W. Ross, principal research scientists at the MIT Center for Information Systems Research, call “wrapping information around core products and services.”
 
My colleague Anant Kadiyala, Director of Industry and IoT Solutions at Oracle, highlights an additional nuance in the data/revenue equation, arguing that alongside the B2C solutions above, companies must also deploy data in a B2E (business-to-employee) model as well, in order to align employees and business processes to enhance customer engagement. To cite just one example: data-rich employee dashboards that permit rapid and efficient escalation of customer-side issues in real time.

Leveraging Data to Improve Bottom-Line Profitability

The second way in which data can drive business is by improving operational efficiencies to impact profitability. We’ve seen this for years now as companies automate business processes. However IoT has added an entirely new dimension to automation, with more to come. Consider the robotics that dominate industries such as auto manufacturing: As sensors proliferate and the computing power to analyze large volumes of data becomes more affordable, these same robots will become more intelligent, with the ability to self-align and self-predict rather than merely respond to a program.
 
We’re already seeing some aspects of this, for example, with Oracle’s Digital Field Service, which combines IoT, real-time predictive analytics, mobile scheduling and routing to manage the service lifecycle of equipment. The system can predict, for instance, when a piece of equipment is likely to fail, and schedule a maintenance visit before failure occurs. Short of failure, it can offer guided assistance in resolving critical issues remotely.
 
Data can also squeeze efficiency from one end of the supply chain to the other. We’ve already seen connected supply chains that can place orders automatically based on point-of-sale data. But with more data comes greater efficiency. Imagine, for example, a tomato farm. When it’s time to harvest, farmers may traditionally pick as much as they can to fill a truck. Built into this process is the cost that a portion of the harvest that may go bad during transport. Now, however, they can instrument the trucks, and analyze humidity, weather, and fuel use to the point where farmers will know exactly when to stop loading trucks in order to minimize waste and assure the highest product quality upon delivery.

The Monetization of Data: What Are the Implications?

Given its ability to drive both revenue and profitability, data is clearly valuable. But exactly how valuable remains a point of significant debate. As Dante Disparte and Daniel Wagner note in the Harvard Business Review, “definitions for what constitutes EvD (Enterprise Value of Data), and methodologies to calculate its value, remain in their infancy…Many attempts to do so have proven to be flawed—even for some of the largest and best known firms in the world.”
 
As the authors point out, this ambiguity has consequences when it comes to assessing and managing risk, but perhaps the more salient question is how to protect valuable data, preserve its integrity, comply with regulatory requirements, and preserve the privacy of customers. Any data solution that an organization adopts will have to ensure continuous, end-to-end security.
 
Data monetization is an imperative for any business hoping to thrive in the Data Capital Economy. Organizations can deploy their data assets in two broad strategies: driving top-line revenue growth and growing bottom-line profitability. The key to both approaches is protecting data as the valuable asset it has become. Investing in best-in-class data collection and analysis not only offers returns measured in higher revenue and greater operational efficiency. Data also appreciates in value when properly “banked” for future analysis in ways we can only begin to imagine.

To learn more about how your company can flourish in the Data Capital Economy, read the MIT and Oracle collaboration on the Rise of Data Capital.

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Comments ( 2 )
  • Gonzalo Thursday, July 27, 2017
    It's actually a great and useful piece of info. I'm
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  • Alex Truedman Thursday, July 12, 2018
    Agree with Gonzalo. However, I would add a point. Data today is so diverse and heterogeneous so in order to get relevant insights and make sure the decision based on these insights are focused on the desirable outcome, initial data should be complete and clean. Therefore, simple telemetry or user data is far not enough. We need tools to collect and leverage all available data on the subject, and this means dealing with massive data sets from multiple sources including social media, customer insights, sensor-data, etc. Here https://www.digiteum.com/leverage-big-data/ we talk about unstructured and semi-structured data and ways to handle these assets. Would appreciate your thoughts. Thanks!
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