In my research I tend to talk a lot about the implications of 802.11n  for enterprise IT departments, however,  the potential impact of the technology for smaller businesses is even more profound.

Forrester defines an enterprise as an organization with over 1,000 employees, anything smaller, and we classify it as an SMB. Our most recent Enterprise And SMB Networks And Telecommunications Survey, North America And Europe, Q1 2008 shows that, while only 10% of enterprise respondents have rolled out 802.11n networks, 15% of SMBs have taken the same step. In companies under 100 employees, adoption jumps to 30%! My hypothesis is these organizations' rapid adoption of the new standard is due to their reliance on Wi-Fi for primary network connectivity, a result of its easier and cheaper deployment across a group of users.

I’ve recently returned from SAS Institute’s Annual Analyst Event held June 23-24, 2008 in Monte Carlo. At this event, SAS leadership revealed a roadmap to amplify, with the most effective decision science yet developed, the judgment of professionals in a wide range of industries including retail and consumer goods. Forrester noted specific new science based processes, deployable without restrictions about legacy transaction applications for:

• Merchandise planning. The most critical decision in any consumer goods value chain is which merchandise to stock. But this decision, although ultimately driven in retail by the buyer’s judgment, must draw on data and analytics that evaluate, based on historic demand) the relative likely revenue and margin resulting from different merchandise portfolios, and test the feasibility of the portfolios against constraints such as store space or labor availability or the firm’s available working capital.
• Size optimization. For retailers selling footwear or apparel a statistical understanding of the distribution of sales by size by store is vital in order to meet consumers’ needs and avoid mark downs and stock outs. It’s well known that consumers’ sizes vary from one region or country to another, with Norwegians for example in general being taller for example than Greeks but retailers need powerful sparse data analytics to plan for the differences in populations that visit urban and out of town stores.
• Space optimization. Retailers provide space in stores in proportion to their expect sales and margin for each merchandise item. But the complex tradeoffs between affinity items, with different margins and attracting different promotional funds simply demand an enterprise analytic approach rather than single user planning tools.