InsightaaS: Deloitte University Press is a source of deep, thought-provoking material on a wide range of technology and management issues. The site’s mandate is to publish “original articles, reports and periodicals…to draw upon research and experience from throughout our professional services organization, and that of coauthors in academia and business, to advance the conversation on a broad spectrum of topics of interest to executives and government leaders.”
This post features one such co-author, Tom Davenport, who is described as “an independent senior advisor to Deloitte Analytics,” but is better known as the author of several books on analytics (“Big Data at Work,” “Keeping up with the Quants“) and as a professor at Babson College and a Fellow at the MIT Center for Digital Business. In the post, Davenport looks at how a theory of “Ten Types of Innovation” that forms the core of Deloitte’s Doblin Group can be applied to analytics, and finds that there are ways that analytics can be applied to each of the ten innovation areas described in the theory.
Surprisingly, given the richness of Davenport’s reputation, there is little unique insight contained within his descriptions of the ten categories. In some cases (spinoffs from existing companies focused on data-driven products, the inclusion of analytics capabilities in products such as fitness wearables), the examples are generally understood. The discussions in other categories appear to be based on the general notion that deeper understanding is beneficial to business decisions, which would hold with or without a specific connection to analytics. There are also some contentions, particularly with respect to IoT (e.g., “no single company can create an IoT initiative on its own; it must collaborate with other firms”) that aren’t necessarily self-evident, and would benefit from some supporting proof.
However, focusing on the individual categories misses the value of Davenport’s post, which is to point out that analytics can be applied to many different important management objectives. And although, aside from a quick concluding remark (“if you’re not using analytics for all 10 types [of innovation], you may not be optimizing your analytical capabilities”) Davenport doesn’t call out the point, it’s important to recognize that the value of embracing analytics isn’t contained in its ability to solve whatever point of pain motivated its adoption, but rather, in the potential to extend analytics across multiple areas of the business. Like other leading edge technologies (including, and notably, cloud), analytics will deliver the greatest benefits to firms that build on the skills and insights that they capture by viewing analytics adoption in terms of a roadmap rather than as an initiative. Viewed through this lens, Davenport’s post is very useful: it helps define the points that can be connected via an enterprise-wide analytics strategy.
A few weeks ago, I heard an interesting presentation by Larry Keeley of Deloitte Monitor’s Doblin Group, a company that consults on innovation. I had seen Doblin’s “Ten Types of Innovation” before, but hadn’t really paid enough attention to it. Keeley’s presentation reminded me that I thought it was the most complete listing of how companies can be innovative. It also made me wonder how many of the 10 types of innovation might involve analytics in some way.
So I started going through the list, one by one. I didn’t know how many might result in a hit–a link to analytics–when I started. Through the magic of ex-post-facto editing, I now know how many. I won’t spoil the secret, but here’s a hint: This essay is pretty long.
Profit model: Profit model innovation involves new ways to monetize a company’s offerings and assets. There is certainly an analytics spin on this form of innovation, in that many companies in both online and offline businesses are attempting to make profits with new data and analytics-based products and services. GE, Monsanto, and several large banks are among the traditional businesses that are exploring profit model innovation with analytics.
Network: Network-oriented innovations involve new products, services, or processes that are delivered across a business network or ecosystem. In analytics terms, this might involve delivering analytics to suppliers or partners in order to help them make better decisions….
Read the entire post on the Deloitte University website: Link
