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subject to p p0 1 The nonlinear program described above may be dif cult to solve using traditional gradient based approaches, and global search approaches, such as genetic algorithms, may be used to estimate the optimal values of p and p0 Once the values of p and p0 are known, the mean and variance of the binomial distribution can be estimated as mean 10p and variance 10pp0 The author used multiple regressions to study the signi cance of the impact of independent variables on the dependent variable Multiple regressions require that the dependent variable be a continuous variable with the distribution of the dependent variable close to a normal distribution Unfortunately, the dependent variable intensity, as used by the author, is not continuous and does not follow a normal distribution While the author has used a transformation that was used previously in the literature, we believe the previous studies may suffer from similar problems The author used multiple regression for testing hypotheses related to the dependent variable intensity of adoption Since the transformed dependent variable is discrete, the use of multiple regression is inappropriate It is generally accepted that a continuous, interval-scaled dependent variable is required in multiple regression, as it is in bivariate regression Interval scaling is also a requirement for the independent variables; however, dummy variables, such as the binary variable in our example, may be utilized Given the change in the distribution of the dependent variable, loss of information as a result of scale transformation, and the inappropriate use of the technique for data analysis, the set of hypotheses related to intensity of adoption may have errors Conclusions We believe that the author s study has internal validity problems Among the reasons for our conclusion are problems with content validity, a dangling unit of analysis, the use of inappropriate techniques for data analysis, and problems related to scale design and change in data distribution due to the transformation of one of the dependent variables The lack of theoretical grounding of different hypotheses exacerbates the internal validity problem To remove or mitigate these barriers, the journal executives needed convincing that there were aws in their reviewing process Our approach was to demonstrate that data is on a critical path to enterprise performance optimization, and the editorial board could achieve this best with a smart data strategy The principal idea was to focus executives attention on data that is essential to enterprise performance and executives ability to execute their primary responsibilities As a result, the editorial policies of the journal were changed and an optimization of manuscript quality was improved Although the journal executives had IT services and infrastructure available for their reviewers, it was apparent that they needed better analytical capabilities to model and simulate conditions in response to a dynamic environment They needed the ability to predict performance with high certainty in the near term and with increased
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visibility over the longer term, not to continue to propagate their errors by a diffusion of errors that allowed the application of a longitudinal method such as Cox regression to be inappropriately applied to a snapshot survey environment It was only after the editorial executives took ownership for the data methods and algorithms, and realized that they needed to optimize enterprise performance, that they overcame the propagated errors that previously may have been considered to be invasive to the review process The previous editorial review mind-set needed to be overcome in order to disturb the status quo in manuscript information management and reveal where the true meaning of the data methods and algorithms were buried The Ugly Instance 1 Lack of interoperability 2 Lack of optimization 3 Lack of correct methods and algorithms Multiple de ciencies in smart data exist
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33 TOP DOWN STRATEGY The place to begin is with executives at every level of the enterprise recognizing and embracing their data insisting that they have clear visibility into the data that they need to optimize performance, and into the data representing enterprise assets that they must protect Executives need and want a holistic approach to data strategy and data management With the notion of smart data, the data should be engineered such that it addresses executive needs for problem solving, decision making, planning, sense making, and predicting This means that when an executive or manager needs to refer to the data for answers, it is unnecessary to scramble to locate it or to judge its reliability or quality, as executive requirements are anticipated and the response preengineered as in data engineering Better still, the executive does not have to search for the answers because solutions and answers are presented to the executive, anticipating requirements It is accompanied by methods and algorithms representing best analytics Some describe this as an autonomic or self-managing system that is characteristic of smart data Data that is shared throughout the enterprise by multiple users and disciplines must be engineered such that it is available openly for seamless processing to those who are credentialed and privileged While there are a host of issues in the domain of smart grid and smart enterprise services associated with implementation, our focus is on anticipating data requirements and engineering data for use throughout the enterprise Since much data is
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