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program, no racial classi cation is involved. Disparate impact on a particular race or ethnic group is not subject to strict scrutiny unless the classifying trait with that impact was chosen to cause that disparate racial impact. Whites, who have usually bene ted from laws that have had a disparate impact on minorities, have no grounds to complain if in this instance the disparate impact favors groups that have traditionally been disfavored. Yet even if one could show that the legislative motivation or purpose here is to help African Americans, this would be an acceptable motivation. As we have seen, the state may explicitly target the health of African Americans because their health care is as important as that of other persons and, because of genetic and other differences, is likely not to be met equally unless explicit attention is paid to race and ethnicity. As long as the health needs of others are being met, singling out African American health needs as an ef cient way to meet the health care needs of all the people should be acceptable, whether it is done explicitly or by funding disease categories in which African Americans are disproportionately represented. Indeed, there are social bene ts from using disease-speci c rather than racial and ethnic categories in research allocations that might make them preferable to more direct subsidies. Few racial or ethnic groups will have a monopoly on particular diseases, so all groups are likely to bene t to some degree from research funding for speci c diseases. Focusing on disease rather than racial or ethnic identity also draws attention to the underlying physical facts and away from racial correlations and the social baggage that they might carry. Surely such choices are constitutionally acceptable.

Often, successful companies hire a consultant to assist with initial implementation and help plan for growth At the bare minimum, someone in the organization needs to own analytics Tools Most organizations have some Web analytics tool in place Few have gone through the important step of validating the implementation of the tool Similarly, few have explored voiceof-the-customer and competitive analysis tools that can be paired with Google Analytics Unified data Companies that generate direct revenue online are at an advantage in this area, since they can easily connect Web analytics data to direct revenue Another key to success is building trust in the data, which can come only through clear measurement standards and regular audits of key metrics across data sources Process integration This is where the rubber meets the road.

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Subjective probability estimates often have a basis in terms of objective data. The use of such data was touched on in the introduction to this chapter (e.g., tting speci c probability distribution curves to data). On occasion there is a need to make important subjective adjustments to databased estimates to re ect issues known to be important, even if they are not immediately quanti able in objective terms.

Organizations that succeed on this front have combined user training, ad hoc analysis, and formal reporting to continuously inject Web analytics data into strategic and tactical decision-making They ve created a culture that rewards performance based on measurable data, that promotes an attitude of kaizen (continuous improvement), and that uses standardized definitions and terminology to keep everyone on the same page We ve put together the following list of questions to help you think critically about how well your company is suited to using analytics data You can use these questions as the agenda for the rst weekly meeting of your analytics team Don t worry if you don t know the answer to every question This list is meant to get you thinking about the organizational context of using Google Analytics at your company..

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