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information-sharing challenges by agreeing on shared vocabularies and exposing and sharing data as a service Krieger said that industry understands the agility and power that separating data from applications represents Consider the Google Maps service and the Google Earth application It is based on a community vocabulary for modeling and storing geographic data called KML (keyhole markup language) By publishing data as a service in KML, the Google Maps service or the Google Earth application seamlessly plots the data on a map or a globe On metadata, Krieger explained how it makes data assets visible or discoverable For example, in digital cameras, where the data is the photographic image, metadata would typically include the date the photograph was taken and details of the camera settings The Department of Defense Discovery Metadata Speci cation (DDMS) speci es how to use metadata to make data assets visible to the enterprise It describes how developers, engineers and users should advertise data assets posted to shared spaces so that others can discover them We didn t make up our metadata standard The DDMS is based on the industry Dublin Core standard, and we added a security leg to it This approach moves people away from hoarding data and increases data visibility and sharing Asked by Bradley how he handles resistance to the information standard, Krieger replied: It helps when the community develops the vocabulary or information exchange semantics This helps both data consumers and producers One-way translation from the community standard at the producer or consumer location is the only requirement Technologies like XML easily enable data wrapping for oneway translations Resistance is reduced because you only need to x one side of any legacy system This is how you can republish data from a legacy system to unanticipated users without rebuilding legacy applications Smart Data Strategy Work The work begins with an assessment of the current situation Are your primary processes modeled and fully attributed as described in the book Have you, as executive, clearly inventoried the questions that you need answering to optimize performance in the enterprise and requirements for planning, decision making, problem solving, sense making, and predicting Following these prerequisites, your management team, including information technologists, can address the enterprise performance optimization system CONOPS and OA Then you will implement with system development and operational improvements in executive support Smart Data Strategy Staf ng Is your organization properly staffed for the initiative Review the skills, knowledge, and experience of your staff to ensure they have what it takes to implement the strategy
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CEO S SMART DATA HANDBOOK
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TABLE 52 Twenty Outcomes to Expect from Smart Data Strategy Outcomes 1 Enterprise performance optimized Measures
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2 Executive planning, problem solving, decision making, sense making, and predicting optimized
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3 Enterprise resource management optimized
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4 Stakeholder relationships optimized
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5 Enterprise processes optimized by design
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6 Regulatory, legal, and business rule compliance improved
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7 Performance visibility improved
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8 Quantitative methods more readily applied
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Key performance indicators and metrics commercial enterprise Key performance indicators and metrics government enterprise Adoption of arti cial intelligence methods and algorithms Data mining of business intelligence to produce smart data Actionable data support key responsibilities in a more timely, accurate, and complete manner, at an optimal rate, frequency, cost, and quality Real time versus batch processing: Business at the speed of thought As a subset of #1 Utilize cost accounting and activity-based costing to outperform traditional accounting methods Consistently positive feedback from stakeholders for enterprise management Utilize negative feedback as a means for continuous improvement and BPR Signi cantly improved metrics from aggregate and granular performance as re ected in cost and time, among other process-speci c indicators Follow an iterative spiral development process for design by focusing on planning and analyzing both the physical and conceptual design schemas necessary for implementation and maintenance Deviations minimized, cost to comply lowered Eternal vigilance by gathering business intelligence to reengineer outdated processes and implement an SOE using an SOA Executives see real-time results and can simulate scenarios Adoption of AI methods, improved algorithms and modeling and simulation techniques such as Monte Carlo applications Actionable data is available on which to apply improved methods and algorithms
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