Seizing the White Space: Business Model Innovation for Growth and Renewal Review

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Seizing the White Space: Business Model Innovation for Growth and Renewal ReviewNot much gets written about business models. I don't know if it isn't sexy enough or it somehow gets bucketed into the sleepfest of most strategy books. The only book I can think of, in recent years, to broach the subject was the self-published Business Model Generation by Alexander Osterwalder and Yves Pigneur.
Seizing the White Space: Business Model Innovation For Growth and Renewal is a fine exception. Author Mark Johnson co-founded Innosight with author and HBR professor Clay Christensen, and while Christensen has been publishing niche extensions on disrupting education and health care, this book is a wonderful addition to the broader ideas of Innovator's Dilemma and Innovator's Solution. Both of the latter books were incredibly important in explaining how companies lose edge in the marketplace and are often blindsided by innovation, but there were some barriers to understanding exactly how to apply the concepts. Johnson does a great job of taking that next step in Seizing The White Space.
The book is squarely focused on showing companies how to build new businesses in area that are outside their current business models. This could be through replacing an existing business, building new models where there are barriers to consumption, or by filling gaps in the market. The final section delivers roadmap on how to go about the redesign and implementation of your new business model. Cases include Tata Motors and Hindustan Levr (India), Xiameter that grew out of Dow Corning, and Better Place, the Israeli start-up trying to change the automotive business model with electric cars.
Seizing The White Space is surprisingly short at 150 pages which makes the book very accessible. The language and concepts also match that accessibility. The book is worth a quick look, a reminder of the unconscious parts of strategy that you too often take for granted.Seizing the White Space: Business Model Innovation for Growth and Renewal Overview

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Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides) Review

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Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides) ReviewReview--Data Modeling for the Business (March 2010) - review originally appeared in Oil IT Journal - [...].
Oil IT Journal reviews `Data Modeling for the Business' by Steve Hoberman et al. The book outlines a new approach to data modeling and includes a chapter on a major oil company's enterprise architecture.
Someone once said the ideal number of data modelers is one. The book `Data Modeling for the Business' (DMFTB) takes practically the opposite approach, advocating a series of corporate Rolfing sessions and pizza parties to thrash out what should be modeled, how, and for how long information should be retained. If the single modeler approach presupposes a domain specialist who knows all, Hoberman's is rather of journeymen data modelers, perhaps without deep domain knowledge, who can extract all the information required from other stakeholders. The thrust of DMFTB is communication and debate with non specialists. This can be rather labored--as in the first chapter which plods through the analogy of a data model and a blueprint for a house.
Those expecting technology insights and a discussion of tools will be disappointed. We learn from the frontispiece that the graphical models in the text were created with CA's ERwin tool. But the book does not really connect with technology. The subtitle of `aligning business with IT using high level data models' says it all.' This discussion is far removed from databases and SQL and focuses on a bird's-eye view of the enterprise rather than on implementation.
There are `traditionally' four levels of models--very high, high, logical and physical. High level models communicate core data concepts like `customer,' `order,' `engineering,' `sales.' Even the `logical' is model is `a graphical representation of [...] everything needed to run the business.' All of which is a far cry from the Express logical model of Epicentre or ISO 15926!
The body of DMFTN is concerned with business, rather than technical data, examining in depth how for instance the concept of `customer' can be implemented in `hundreds of database tables on a variety of platforms.' Business requirements may mean changing definitions of key concepts like customer. These start at the high level, and ripple down through the model layers. Modelers can then perform impact analysis to see `what changes are required at the logical and physical levels.' Although how such changes are effected across `hundreds' of databases including pre-packaged behemoths like SAP is glossed over.
Of particular interest is a chapter on data modeling in an international energy company. Here an enterprise architecture initiative set out with a vision of a `shared corporate data asset that is easily accessible.' Amusingly, half way through their work, the team found that there was another initiative working on master data management whose goal was also `a single version of the truth.' Such is the nature of the large decentralized beast! The oil company's modelers leveraged industry data models including PPDM, PSDM (ESRI), MIMOSA and PRODML--although exactly how these different circles were squared is not explained!
Despite its technical weakness, DMFTB makes an interesting and perhaps inspiring read for technologists who are trying to engage with their fellow stakeholders.
Comments to info@oilit.com.Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides) Overview

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