Organizations that successfully implement new technologies focus on business solutions that make effective use of technology. The critical challenge for these organizations is coordinating the complex issues of centralized data and technology resources with organizational business expertise that is generally distributed throughout the organization.
The proliferation of analytic software continues to develop ever-increasing capabilities. Pragmatic organizations understand that project success is largely determined by the implementation of technology, not by the technologies themselves. No piece of software, and no algorithm, understands the domain of the decision process or the project team’s unique performance metrics.
Organizations that effectively leverage analytics for competitive advantage focus on goal-driven analysis of data, combined with effective implementation strategies, to enhance business performance. To achieve success, analytics efforts are pushed to the functional decision makers to ensure that the project design captures the nuances of the business problem. Centralized IT and quantitative specialists provide support to these decision makers to enhance the capabilities of the project team.
Big Data and other Technology Resources
Big data is a real problem…for IT departments faced with the need to capture, store, organize and distribute that data in an ever more complex environment. But big data is not a problem in most analytics efforts.
Additional records give the analyst the potential to develop higher levels of precision. Models interpolate between known points in training data sets. Additional records simply mean those known points are closer together. Forex trading predictions and platforms
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Typically, the organizational challenge is the application of a developed model across a large set of data, not the analysis itself. The analyst generally requires only a small subset of the data for successful model development.
Distributed Organizational Expertise
Analytics is performed in an effort to enhance the decision making of organizational domain experts. Organizational decision processes are decentralized. Analytics must also be decentralized to respond to the unique and varying needs of the domain experts within the organization.
The only effective way to measure the value of any analytics project is in the context of the performance metrics of the organizational domain. Business does not measure its effectiveness on the basis of standard analytic metrics. There is no one “right” set of metrics to be applied across projects.
Fat Data: The Domain Expert’s Challenge
The problem for analytics projects supporting business domain experts is “fat data.” Fat data is the increasingly larger number of attributes that describe individual business relationships. The challenge for the analyst is determining which of this growing array of attributes gives insight into future outcomes and the potential to develop enhanced decision processes.
Attributes that influence an outcome provide the decision maker with a propensity to anticipate a future event that impacts business performance. It is relatively straightforward to examine these influences when dealing with a small number of attributes. However, the complexity of dealing with a large number of attributes simultaneously is challenging. Organizations that master the complexity of fat data achieve both enhanced performance and competitive advantage.
What is obvious is that these propensities change relative to the decision process under consideration. There is simply no one correct way to capture these insights in a centralized perspective. The only effective way to evaluate analysis is in the context of the specific performance metrics used in each decision process across the organizational environment. As we are talking about The Power of Distributed Development have a look at forex trading predictions and platforms to check the predictions which is closely linked to Analytics for Competitive.
The goal of a decision maker is to incrementally develop enhancements to decision processes in a dynamic environment. It is neither necessary, nor desirable, to attempt to develop a comprehensive solution to the entire organizational decision process.
Analytics projects are evaluated based on their return on investment, and by the contribution they make to the enhancement of future decisions.
Successful analytics projects are generally low-risk, low-investment, high-ROI efforts that provide an incremental enhancement to a decision process based on the specific performance metrics of that business unit.
In the increasingly complex and competitive environment of business, it is critical to understand that your data, your software, and your technology will never understand the context in which it operates. It will never understand your decision process. It will never understand how you measure success.
The only value of technology is to make your business more effective in achieving your goals. To achieve and maintain competitive advantage, organizations must provide centralized resources to support distributed analytics development efforts. Only the business decision makers can monitor and adapt to a rapidly changing environment, utilize available technology to enhance future performance, and ensure that we do not get bogged down implementing technology that offers only the hype of great features.
SOURCE: Analytics for Competitive Advantage
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