Data for Good - Enabling Innovation Like Never Before

April 5, 2014

We live in an ever-growing ocean of data. Our networked world is a data-producing machine, and increasingly businesses and governments are recognizing the great potential for groundbreaking innovation stemming this much-championed “Big Data.” Yet, innovations do not on their own bubble out of all this information. How exactly does data drive innovation and what are the tools that enable us to harness that data? 

As noted in the previous Data for Good installment, the opportunities afforded through data are unlocked by deep analysis, looking for revelations in unlikely places. This innovation challenge is daunting, but the resources available for insightful data gathering and innovation optimization have never been greater. Data-driven innovation is facilitated through:

A massive increase of digital data

This includes IT interoperability and open protocols for data capture and exchanges across multiple sources and parties. There are also troves of research published on the Internet and databases containing patent filings. Organizations can also look to Web services for transactional monitoring of data, such as exchange rates, weather, and financial systems, and they can also look to unstructured and semi-structured data, including social media data, open but de-identified medical records, and more. There is the “Internet of Things,” with sensing and controlling devices feeding mountains of new data, and organizations can even look to their own Sales Force Automation Systems that can be ready sources of front-line insights. 

New technologies for revealing insights

Cloud services are enabling more and more organizations to analyze and glean insights from myriad data sources at a fraction of the historic costs of building high-speed processing systems. Businesses can use rules engines, which are able to formulate event triggers to rapidly isolate opportunities or affirm or refute hypotheses. The science and technology of business intelligence and analytics is also accelerating, as is the capacity to leverage longitudinal assessments of approaches to innovation and IP in respective sectors and spaces. 

Methods for fast-tracking the innovation process 

Technologies (like 3D printing, Mobile Human Machine Interfaces, open IT protocols, cloud-based VoIP services) and software services (such as CAD CAM) enable rapid prototyping, elicit market receptivity, and allow faster testing of product inventions and new service offerings. When it comes to funding the innovation process, there are consumer and enterprise crowd-funding venues and technologies for securing financial contributions, which can foster innovation competitions and deployments. Crowdsourcing sites, online chat rooms and collaboration hubs in use across leading corporations and their ecosystems foster faster and more robust innovation, and with today’s mobile devices, businesses can engage, collaborate with, serve and mobilize people like never before. 

The good news is that there are also many experts and organizations providing these essential tools and capabilities. They can support organizations in upping their innovation game to meet today’s challenges. 

In the field of technology-enabled data gathering and innovation conceptualization, for example, an innovation consultancy that’s been recognized as a World Economic Forum Technology Pioneer is Imaginatik, which provides enterprise-wide, crowd sourcing and collaboration Software as a Service (SaaS) to drive and manage the innovation process. Imaginatik’s “Innovation Central,” “Discovery Central,” and “Results Engine” platforms can pull insights from diverse and varied participants to help organizations assess, prioritize, and realize opportunities. 

The innovation specialist, Doblin, a division of Deloitte, has amassed longitudinal data on all kinds of innovations across what they view as “10 Types of Innovation” and across multiple industry sectors and landscapes. This baseline information can support innovation insight gathering, assess and corral possible convergences, identify new innovation spaces to occupy, affirm distinct value in an innovation hypothesis, or even refute proposed propositions as insufficiently distinct in creating value. Doblin has historically noted that the rate of the typical innovation’s success is usually well below 10%, and that by managing across all 10 types and dimensions of innovation, success rates can be boosted many fold. 

Another innovation consultancy, Optimity Advisors, has looked at collaboration from a social perspective and is now advising on harnessing massive networks via Wiki Management. The firm notes that “the power of networks is reshaping both the work we do and the way we work.” They suggest that “designing organizations for mass collaboration demands a new and very different model – Wiki Management.”

Business Models Represent the Latest Innovation Opportunities 

Regarding the all-too-critical business plan development process for go/no-go determinations and product investment prioritizations, there’s plenty of regulatory, investment filing and population data (and plenty of industry trade articles on the Web) that can be assessed and pieced together to project potential sources of revenue, transactional volumes and costs, and to develop a traditional, compelling business case. 

However, while rapidly creating new products is so very necessary, if a company’s strategic plans and business cases focus primarily on new product developments, it’s quite likely that the company will miss a major new source of sustainable innovation: the business model itself. 

The GE Innovation Barometer notes that “fifty-two percent of senior executives believe that the development of new business models will contribute the most to their company’s performance going forward, representing a six-point increase when compared to how it has traditionally contributed to their innovation portfolio.” 

The potential value in developing new business models is affirmed in a Doblin report, which emphasized the potential untapped opportunities in expanding from molecules to new business models for biopharmaceutical companies. The report states that “new commercial models represent the biggest change in the way bio­pharmaceutical companies have been engaging with stakeholders since the rapid expansion of the direct-to-consumer marketing channel in the late 1990s.” 

In its analysis of more than 1,500 publicly announced innovations in the pharmaceutical industry during 2010, Doblin observed a biopharma industry focus on innovating for core processes, product performance, networking, and channel. Doblin concluded that what the industry had not done as much, however, was seize opportunities to “create new customer experiences, offer value-added services, or define truly new business models.” 

Given this, Doblin cautions that even more data will be needed when assessing and forging new business models. The report notes: 

“To transform existing commercial models and create new models, companies can benefit by taking an expanded view of what innovation can mean—includ­ing new services, experiences, and business models…Greater value can come from innova­tions that differentiate a company’s relationship with customers and other stakeholders by, for example, building a commercial model that gives physicians information when, where, and how they want it delivered. Other innovations could emphasize new services, such as simplifying patients’ access to products or improving patients’ adherence to treatments.” 


There’s no question: organizations today have at their disposal new tools, technologies, and processes for enabling innovation in products, services, and business models. But while these instruments can reveal deep insights gleaned through data and dramatically advance innovation, the core element in the innovation process that needs to be better understood and nurtured is the human element. Driving innovation requires passionate people to pursue insights and push through adversity, persevering despite the setbacks and failures that occur in the innovation process – a topic discussed in the next installment of this series. 


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This is the second post in a series on "data for good." Read the first post here.