How Big Is Big Data?

Do you know how big a zettabyte is? It is 1,000,000,000,000,000,000,000, or 10²¹, bytes. Why, you may wonder, do we have need for such a word? This is currently the measuring stick required to describe the world of data. At the time of this writing, EMC, an IT solutions firm, estimated the size of the digital universe to be about 4.4 zettabytes. Between now and 2020, just five years away, this number is expected to grow tenfold to 44 zettabytes.³ Big data indeed.

Even more interesting is that only 5 percent of this data is actually being analyzed today and only 20 percent is accessible by the cloud, or “connected.” In 2020, EMC predicts that up to 35 percent of data will be useful for analysis and 40 percent will be connected.⁴ So, while the overall amount of data will multiply by a dizzying factor of ten, the amount of useful and accessible data could increase by a factor of more than one hundred (see figure 8-1). A key driver of the enhanced usefulness of data being created is the ability to append or tag information to the data.

This is possible thanks to the increasing contribution from so-called embedded systems. The “internet of things” is a now well-established moniker for such systems.⁵ For example, consumer fitness aids and sleep monitors all produce tagged data. They don’t just record measured results like temperature and motion, but also the time, location, and context of that recording, making it far more useful. Applications such as these are just as common in a commercial context: now medical equipment, industrial equipment, mining tools, utilities—even garbage cans—all create and upload useful data.

How to Seize the Big Data Edge Opportunity

Big data, and the ascendency of smart and accessible information, represents an enormous opportunity for virtually any company. The massive enhancement to the visibility of how a given business and its market are performing will empower companies to be even better at delivering on their core offerings. Factories will be more automated and more integrated with marketplaces; supply chains will respond to demand in real time and, eventually, practically run themselves.

Unfortunately, big data also represents a significant challenge for many companies, requiring them to invest in becoming better at generating, capturing, storing, and analyzing data if they are to remain competitive. The internet of things is frequently cited as introducing a disruptive new competitive dynamic that will separate the digital winners from the losers.

The expansion of data and the capabilities for using it also directly increase the size of the opportunity for enterprise edge strategies. As your enterprise increases the amount and usefulness of its data, it will find more and more potential opportunities with companies that could also find value in this data, perhaps even companies in different industries and markets.

Consider how your company will be generating data in five years’ time. What opportunities could this open up for your company to avail itself of an enterprise edge move? Consider your sources of data today and how these will expand over time. Also think about how your investments will make this data more useful by appending information and connecting it to the internet. As with the approach to enterprise edge strategies we detail in chapter 4, identifying data-driven edge opportunities involves similar questions:

• What are your company’s different sources of data?

  – Can you list and describe them all?

• What is the quality of this data?

  – Is it well structured?

  – Is it longitudinal (over time)?

  – Is it customer-specific?

• How could this data be useful to others?

  – To your current customers or suppliers?

  – Who else is affected by this data?

  – Is this data relevant to any industry other than my company’s?

• Who, other than your company’s competitors, would buy this data?
• What value would they derive from the data?

Answering these questions is not easy. It takes a coordinated effort to break this down. What often works best is to assemble a structured working session (or several) with a cross-section of stakeholders, both internal and external to an organization. Include people adjacent to the business, like suppliers and customers, but also people completely perpendicular to your business, such as technology experts, academics, and leaders from analogous industries. Facilitating a workshop to work through these questions and challenge the conventional wisdom can be extremely effective at unlocking avenues for possible enterprise edge opportunities.

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Based on our research and client work, we have made some observations about the expanding opportunity for data-powered edge strategies that can help you identify where you will want to focus:

1. The usefulness of data is indiscriminate.
2. Data fluency, or the ability to analyze and understand data, is increasing.
3. Data is a product.
4. Consumers value data, too.
5. Renting data to other users is easy.
6. The value of data can be disproportionately large for users outside your enterprise.

Usefulness Is Indiscriminate

Our first observation is that the usefulness of data is not unilateral. Big data is not uniquely directed at being useful for your business alone. Making your data smarter and more accessible for internal use makes it more useful for others, too. This expanded connectedness of data is directly applicable to opening up new edge opportunities.

This type of investment also expands the range of other users for which the data could be of value. In the context of enterprise edges, this conclusion is important. As data becomes smarter and more accessible, your opportunities to find enterprise edge opportunities for your data should increase.

In our review of over six hundred of the largest global companies, we found a wealth of examples of data being sold across industries. One company that understands this concept is the Toyota Motor Company.

In 2013, Toyota launched a business solution for municipal and business customers in Japan that allowed its customers to access real-time traffic information and associated analysis. The service leveraged an existing Toyota data asset: the real-time information it collects about car location, speed, and so on from its GPS-enabled vehicles in order to support Toyota’s factory-installed navigation systems. This data, called telematics, is a powerful asset, the value of which is not uniquely aligned to supporting in-car navigation.⁶

Toyota’s big insight was to recognize that the data was of value to other, different customers. And importantly, the strategy acknowledged that allowing others to have access to the data would not affect the current core purpose enabling the navigation service. Toyota’s enterprise edge service takes real-time traffic information generated from telematics data about vehicle location, travel speed, and other parameters to provide a cloud-based information service for a monthly fee starting at nearly $2,000.⁷

There are many different ways in which Toyota’s businesses and government customers use this edge service. Many use the data to study and improve traffic flow, provide map information and routing services, and assist in emergency response efforts. Owners of large commercial vehicle fleets can use the service to track the location of their vehicles and their progress in completing delivery routes. There is even a consumer-focused offering, Toyota’s subscription-based G-Book smartphone app, which uses the telematics information to advise individual drivers on the most efficient routes so they can avoid traffic and other delays.⁸

Data Fluency Is Increasing

A second conclusion is that in this new data-driven world, more and more companies will be ready for analysis. If, as we implied earlier, data management and analysis are fast becoming table-stakes capabilities for competing in your core market, then more companies can find value in data or, more properly, in your company’s data. This increases the number of possible customers you can find for your data offerings. The most immediately interesting cohort to consider is your existing customers. Cargill, the diversified food and agriculture company, recognized this opportunity.

Cargill’s core business is to sell crop seeds and other crop-related products to farmers and then to buy the resulting grain and commodities and either trade them or process and distribute them to food manufacturers.⁹ In 2014 the company unveiled a new data product designed to help farmers increase crop yields. NextField DataRx is a software product that guides farmers on how best to plant their fields using so-called prescriptive planning technology. The software, which Cargill sells separately from its base products and services, takes account of 250 variables, including soil type, environmental condition, and seed performance. Cargill claims that the technology will help boost yields by as much as 10 percent. “We’re trying to help farmers maximize their [return on] investment and the output of their farm,” said Steve Becraft, crop-inputs manager for Cargill’s agriculture business.¹⁰

The company developed NextField DataRx to complement its core service of buying and selling seed, and other agricultural products. The software package leverages the company’s strong foundational assets in agricultural know-how and market relationships. In its core business of seed development and marketing, Cargill accumulated a large database of information detailing how its seeds performed in various types of soil and weather conditions. Turning this information around, the company realized that it would be able to analyze a farmer’s land and provide greater predictive certainty for previously unknown but critically important outcomes like crop yields and pesticide effectiveness. This was the key insight in developing the NextField DataRx software—that Cargill could use its already developed database that supported seed development to advise its customers in their own businesses.¹¹

Once Cargill had developed the software, bringing it to market was only a small incremental effort, but produced highly profitable returns, given the recurring nature of the revenue and its minimal cost of delivery. This strategy demonstrates a number of hallmarks of edge thinking. First, it is consistent with product edge strategy: Cargill found a way to append a value-added service to its seed crop offering. Second, it can be seen as an enterprise edge move because Cargill is availing itself of existing data and knowledge assets in providing the service to farmers.

A critical conclusion from this example is that a key enabler of the opportunity for Cargill was that its customers were ready and willing to use the data.

Data Is a Product

An expanded fluency in data and an increasing ability to make it useful mean that more and more companies and customers have a need for data. The internet of things is affecting not only the consumer world, but also almost every industry, resulting in a business environment where most core product offerings can include a data service of some kind.

Caterpillar, which makes earth-moving vehicles and other heavy machinery, understands this. For several years, it has provided its mining customers with technology that helps improve fleet utilization, productivity, safety, and regulatory compliance. Now, it is starting to offer this package of technology under the brand “Cat Connect” in each of its end markets.¹²

Caterpillar now factory-installs sensors and other computerized equipment in many of its vehicles. It can then offer an optional product-edge data service to its customers, allowing customers to monitor how their vehicles are performing, when they need maintenance, and so on. Most vehicles have the technology installed at the factory. For an additional monthly service charge, Caterpillar customers can unlock the flow of data via a subscription to the Cat Connect service. To many customers, this is a small cost relative to the initial capital investment in the machinery. For Caterpillar, the economic impact is much larger. This data-driven product edge creates a valuable recurring revenue stream for Caterpillar and serves to strengthen its relationship with customers. Furthermore, because the equipment is factory-installed and the software is already written, the Cat Connect service produces a higher margin than the company’s capital-intensive core equipment sales.¹³

Consumers Value Data

Data fluency is empowering not only business-to-business companies. We see a similar dynamic in consumer-facing markets. By 2020, the millennial generation—those born after 1982—will represent the largest consumer-spending group in the United States.¹⁴ This generation and the one after it (Gen Z) have never known a world without the internet; their adult lives are immersed in the world of cellphones and social media. These are truly the technology generations.

Mobile technology, in particular, has empowered these consumers to be ready and willing to interface with data in all shapes and forms. As our consumer-facing clients tell us, understanding and taking advantage of the permission to engage with this generation through technology is a focus in virtually every aspect of their businesses today.

Consumer-facing companies have opportunities to target data-enabled product edges. As you race to invest in technology to connect with your consumer digitally through mobile apps, websites, social media, and onsite technologies such as kiosks and touch screens, consider how a data service can append to your core offer. Certainly there are numerous ways data can enhance a core offering: “the sale is starting now,” “your departure gate has changed,” “your room is ready for check-in,” “a table has opened up in the restaurant,” “the wait time in the line is currently ten minutes,” and so on.

As we detailed in chapter 5 on effective upselling, before you weave these new features into your core offering, pause and consider where you can present these add-ons as options that you can charge for separately. If this seems challenging, ask yourself if you can offer levels for free (for example, a basic information feed) and an enhanced premium service for which you can charge an upgrade fee.

For example, let’s examine LinkedIn, the business-oriented social media service that was launched in May 2003.¹⁵ At the time of writing, LinkedIn claimed to have over 364 million members in over 200 countries worldwide.¹⁶ Membership on LinkedIn is free. The basic membership, like most social media tools, allows a member to build a profile and use it to cultivate and interact with a network of professional connections globally.¹⁷ It is a truly amazing and modern tool for maintaining contact information with both friends and business connections. Millennials don’t own Rolodexes.

LinkedIn was first listed on the NASDAQ in 2011 and now, at the time of writing, has revenues of over $2 billion and a market capitalization of $29 billion.¹⁸ The bulk of this revenue-generating business, what we would consider its core, addresses enterprise customers with talent-sourcing and marketing services. But 20 percent of its revenue comes from its members.¹⁹ LinkedIn recognized that some of its members wanted access to more information and that they would be willing to pay for it. Starting at $29.99 a month, premium members can see the full list of members who viewed their profiles in the last ninety days, send three InMail messages that allow them to contact anyone on LinkedIn, view the full profiles of people three connections away in their networks, and perform advanced searches. The premium product offerings are targeted at specific types of users with special add-ons for each: the Job Seeker, Business Plus, Sales Navigator Professional, and Recruiter Lite.²⁰

This member-generated portion of LinkedIn’s revenues provides a great example of a data-enabled edge upsell. While super-users account for 20 percent of LinkedIn’s revenue, estimates put the number of these premium subscribers at only a tiny proportion of its membership.²¹ To earn this revenue, LinkedIn is leveraging its existing data assets; the information and enhanced functionality are already present in the network. The premium offers are optional add-ons on top of the base service; they exist at the edge of the core product and are targeted at specific but existing customers. And the options generate incremental revenue, critical for an edge strategy.

Renting Data to Others Is Easy

Not all companies are champing at the bit to embrace this new standard of data fluency and analytical strength. For some, possibly many, big data is actually a daunting prospect. Or perhaps equally often, while management teams have the vision and eagerness to access all that big data has to offer, companies are disappointed to realize that their existing equipment and systems are major impediments to achieving their big data dreams. Other companies acknowledge that they simply don’t have the in-house talent and expertise to capitalize on the opportunity.

When faced with these challenges, we see another opportunity for an edge mindset to be valuable. Perhaps a company cannot fully realize the value of its data, but by assessing the possibilities of who would rent its data, the firm may find new ways to capture its value without having to become a data expert. That is, by activating an enterprise edge strategy, the company may be able to monetize a valuable asset (its data) in new and creative ways.

Our view of the post–big data world is therefore less binary than you may have concluded based on the dynamics we described at the beginning of this chapter. Instead of a world where winners are defined purely by their level of data management and analytical prowess, and by contrast, the companies without these competencies are the losers, we envision another path to success for companies that rent their data to others. These data renters will recognize that the power of their data is not limited by how they can use it to enable their own core business; they can also benefit by letting others access its power. And the value may be far greater in these cases; in fact, employing an enterprise edge approach to data is likely to be a far more accessible (and potentially valuable) use of data assets for the majority of companies.

We can see this phenomenon in the example of UnitedHealth that we described in chapter 4. In order to operate its core health insurance business profitably, UnitedHealth collected a vast quantity of information on its subscribers’ health outcomes over time. This database was table stakes for the company’s underwriting business; it would be nearly impossible to earn a profit in its core health insurance division without tracking subscribers’ health outcomes over time.

Despite this, UnitedHealth was certainly not a clinical research expert. To use this type of longitudinal data in a clinical context, UnitedHealth would have needed to develop deep expertise in a number of disciplines unrelated to insurance—biostatistics, chemistry and biology, drug development, and regulatory approval, to name a few. Instead of investing in building these complicated (and expensive) new competencies, UnitedHealth decided to rent anonymized subscriber data to pharmaceutical and biotech companies, which already had deep expertise in these areas.

From this example, we see clear evidence supporting the theory that big data does not produce binary winners and losers, dictated only by analytical sophistication. UnitedHealth was able to rent (and monetize) its data assets without becoming an expert in clinical analytics; furthermore, its customers were able to extract a tremendous amount of value from the rented data.

Data’s Value Can Be Disproportionate

Your company’s data is often more valuable to other companies than it is to you. Many companies consume themselves trying to drive breakthrough insights for their core business from data. The value often comes incrementally through a series of small wins.

However, when you make your data available to outsiders in different industries, you can potentially multiply its value. Your data could be the missing link for a company that has never had access to the side of your customers that only you see or the perspective over time that your business model allows. Even data that may seem trivial to your business, when seen longitudinally over time, could be a prism of insight for another company. For Toyota, simple information about a driver’s trip to the grocery store, once assembled across many similar errands, provides a rich perspective for government agencies focused on improving road safety and reducing congestion.

The asymmetry in this edge opportunity is worth emphasizing. While the attraction of edge strategy is the accessibility and immediacy of opportunities, the benefits, while valuable, are often incremental to your core business. Fortunately, this is not necessarily so with data, creating a compelling reason why data should be one of the first places you look when seeking edge opportunities.

Practitioner’s Notes

• Data is a valuable enterprise asset that readily enables enterprise edge opportunities due, in part, to the fact that it is unconstrained; letting someone else access your data doesn’t diminish its value.
• Data is also one of the easiest tools to use in a product edge opportunity; virtually any product or service can be enhanced with associated data and information. The data could be about how the product is performing or how a customer can make the most of the product or service.
• Opportunities for both enterprise edge and product edge strategies using data should increase because:

  – The amount of data being creating is expanding massively.

  – The portion of data that is useful is also set to increase.

  – Companies are becoming more data literate and reliant.

  – Consumers and products are becoming more technologically enabled.

• Data analysis proficiency is not a requirement of data-enabled edge strategies. Using an edge mindset to find ways to rent your data should help you capture some of its value, even if your company is not strongly analytical.
• The value of data is both indiscriminant and disproportionate. That is, there is no limit to the number of potential customers for your data, and there is no limit to how valuable your data could be to these users.
• We recommend that companies search their data as one of the first places to go for edge opportunities. Look to your customers; they might value your edge offerings. But also look further afield; the key to many enterprise edge strategies (for which data is often a key input) is to consider who, besides your current customers, could find value in your data assets.
• Your data often has disproportionate value outside your enterprise. For most companies, analyzing internal data produces only incremental insights. However, when companies employ an enterprise edge mindset and find new, external uses for data, the value can be enormous, both in terms of insight and financial returns.