IoT GUIDE

I recently read an MIT Sloan Management Review article by Clayton Christensen’s recent book titled “The Hard Truth About Business Model Innovations.” While the article is full of great observations about business model transformation, the most important motivation for business model transformation is found at the end:

“..our understanding of the business model journey allows us to see that, over the long term, the greatest innovation risk a company can take is to decide not to create new businessesthat decouple the company’s future from that of its current business units.”

We use the Big Data Business Model Maturity Index as a vehicle for engaging with our clients about how they can leverage data and analytics to transform their business models (see Figure 1).

              Figure 1: Big Data Business Model Maturity Index

In light of the Big Data Business Model Maturity Index, the article really got me thinking about how the entire data technology industry is posed for a business model transformation. Let me explain.

Across a number of industries, we are seeing business models under attack. It may have started with the digital media industries (advertising, entertainment) but it has quickly spread into physical product and service industries such as retail, telecommunications, financial services, healthcare, education, travel, hospitality, transportation, distribution and manufacturing. Organizations within these industries are leveraging data and analytics to disrupt existing business models (and disintermediate customer relationships) as the boundaries that separate industries evaporate (see Figure 2).

           Figure 2: Big Data Driving Business Model Disruption

I believe that same business model disruption (and subsequent customer disintermediation) is going to happen to the technology industry that spawned all of this transformation, and no technology industry has the opportunity to be a business model disrupter more than the data technology industry – the industry that gathers, stores, secures, provisions and analyzes the growing wealth of transactional, social, mobile, wearables, embedded, machine generated and publicly available data.

So I searched for a proxy that might provide some insights into the business model transformation of the data industry, which took me to the bottled water industry transformation.

Bottled Water Business Model Transformation

For over three decades, water was simply something that came out of the tap in your home or the water fountain. While water was essential to life, its relative financial value was very modest. Then things changed.

In 1977, Perrier launched a successful advertising campaign in the United States, launching the popularity for bottled water. Bottled water is currently the second largest beverage category by volume in the United States, behind carbonated soft drinks (CSDs). The Beverage Marketing Corporation (BMC) predicts that bottled water will surpass CSDs to become the number one beverage in America by early 2017[1] (see Figure 3).

        Figure 3: Increasing Demand for Bottled Water (per Person consumption)

Despite having one of the best municipal tap water systems in the world, American consumers flocked to commercial bottled water for four key reasons[2]:

• First, consumers have been bombarded with advertisements that claim that their tap water is unsafe, or that bottled water is safer, healthier, and more hip.
• Second, public drinking water fountains have become increasingly hard to find. And the ones that exist are not being adequately maintained.
• Third, people are increasingly fearful of our tap water, hearing stories about contamination, new chemicals that our treatment systems aren’t designed to remove, or occasional failures of infrastructure that isn’t being adequately maintained (see Flint, MI water disaster).
• Fourth, some people don’t like the taste of their tap water, or think they don’t.

A couple of key lessons that we can take away from the bottled water business model transformation:

• Through a business model transformation effort focused on value and not just functionality, consumers willingly pay 2000x more for bottled water than they do for basically the same functionality coming out of the tap or water fountain.
• The vendor didn’t create a new product – it changed the economic value of their product (water), through effective marketing, which spawned an industry.

Future of the Data Industry

The same phenomenon is starting to occur in the data technology industry, and the potential to change the financial valuation of data technology players could be stunning! The challenge is how do these data technology vendors turn what has historically been a commodity by-product (data) into a premium product (business outcomes)?

Organizations have traditionally treated data as a legal or compliance requirement, supporting limited management reporting requirements. Consequently organizations have treated data as a cost to be minimized. The financial valuation of data technology companies has been based upon those perceptions and relationships. The below chart from Gartner summarizes the business model transformation challenge (see Figure 4):

Data technology companies tend to sell to the part of the organization where data is a cost to be minimized and the sales processes focuses on negotiating with Procurement on price, margin, terms and conditions, instead of engaging with the part of the organization where data is a corporate asset to be exploited for business value, and discussions focus on time-to-value and de-risking projects.

It would seem that anything that the data technology companies could do to increase the perceived and actual value of data to its customers and the market could dramatically increase their financial valuation. This includes leveraging data to deliver business outcomes such as:

• Optimize key business processes,
• Uncover new monetization or revenue opportunities,
• Reduce security, regulatory and compliance risks,
• Create a more compelling customer and partner engagement, and
• Enable new business models

This would require a business transformation for technology companies in order to focus more on delivering business outcomes and less on selling technology piece parts with “some assembly required.”

Data Industry Call To Action

In much the same way that Perrier’s introduction of bottled water to the United States in 1977 led to the creation of a new industry, the data technology industry sits at a similar business transformation point. But business model innovation and transformation will not be achieved with a “business as usual” approach. As Clayton Christensen points out:  “organizational interdependences developed to optimize existing business processes dooms the creation of a new business model within the existing business model.” It will require these companies to think differently about how they structure their organization, how they go to market, how they build products and how they incent their customer engagement teams in order to create value for their customers.

There is much that we can learn from other organizations that have transformational business models. Companies such as Netflix, Amazon, Uber and GE have created business models by focusing on the Customer Journey; by meeting customers “where they are and taking them where they need to be” and focusing on delivering business outcomes (or decisions in data science vernacular), not just technology piece parts.

But this needs to be done at scale; something a data technology company is uniquely qualified for, with the ability to create pre-engineered solutions and “collaborative value creation” platforms that couple hardware, software, and consulting to support the customer journey.

Data technology companies can be the pioneers in transforming their business models; however, they will not drive business model transformation by selling data as a commodity like tap water. Instead, data technology companies can exploit product development, sales, marketing and consulting to focus on delivering customer business outcomes at scale; helping customers to leverage and monetize their data assets to optimize key business processes, reduce security and compliance risks, uncover new monetization opportunities and drive a more compelling customer engagement[3].

This article was originally published on LinkedIn.