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Hiltbrand, Troy

Publications and source records attributed to Hiltbrand, Troy.

Watson and Siri: The Rise of the BI Smart Machine

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

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Dealing with the Data Scientist Shortage

Few areas in the economy have generated as much attention as big data and advanced analytics in recent years due to its potential of revolutionizing the way that business function in the coming years. One of the major challenges that organizations face in implementing analytics that have the potential of providing them a competitive advantage in the market is that of finding the elusive data scientist needed to execute on big data strategy. This article addresses what some business are doing to bridge that gap between vision and reality.

99 GENERAL AND MISCELLANEOUS↗

Behavior-Based Budget Management Using Predictive Analytics

Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently under similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.

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