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Predictive analytics - The cure for business myopia
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Predictive analytics - The cure for business myopia

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An insightful read to understand the Key Techniques to predict future business outcomes.

An insightful read to understand the Key Techniques to predict future business outcomes.

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  • 1. Predictive Analytics – The Cure for Business MyopiaMyopia = Shortsightedness. Theodore Levitt published his landmark paper titled ‘Marketing Myopia’ in1960 that led to a paradigm shift in how companies viewed their business models. Marketing Myopiarefers to ‘focusing on products rather than customers’, and how such a short-sighted view is bound toeventually lead to business failure.One reason that short sightedness is so common is that, organizations feel that they cannot accuratelypredict the future. While this is a legitimate concern, it is also possible to use a whole range of businessprediction techniques currently available to estimate future circumstances as best as possible.Some of the relevant techniques to predict future outcomes are given in this blog post. Thesetechniques, though important in isolation, are much more powerful if they can be combined togetherfor specific business scenarios. There is extensive research being done in Hexaware’s BusinessIntelligence & Analytics Innovation Lab around this theme.Key Techniques to predict future business outcomes are:1) Data Mining: Data mining is the computer-assisted process of finding hidden patterns in data. Datamining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions with respect to future business outcomes.2) Text Mining: Text Mining is the process of deriving high quality information from unstructured textdata. There are various techniques used to derive high quality information from textual data, such ascomputational linguistics, information retrieval, statistics, machine learning, etc. Various forms of textmining include categorization, classification, clustering, concept extraction, summarization, sentimentanalysis, etc.3) Complex Event Processing (CEP): CEP is used to discover information contained in multiple eventshappening in parallel and then analyze its impact from the macro level as "complex event" and thenhelp take subsequent action in real time.Primarily an event processing concept that deals with the taskof processing multiple events with the goal of identifying the meaningful events within the event cloud.
  • 2. 4) Statistical Simulations: Predicting the future involves building mathematical models that define therelationships between different classes of variables that are important for the organization. Differenttypes of relationships, viz. Deterministic, Stochastic, Empirical, and Heuristic are possible between thevariables being modeled. Simulations allow business users and decision makers to execute the modelswith randomized inputs to ascertain the effect on output variables.5) Business Process Simulations (BPS): BPS are a special case of simulations that deal with non-linearity.For example, in a scenario where advertising spend depends on revenue and revenue in turn dependson advertising spend (with a lag), there is no clear line between dependent and independent variables.Such non-linear scenarios are very much prevalent in business and can be modeled through specializedBPS tools like Powersim, Vensim, etc.For BI practitioners, it is important to realize that synthesizing these techniques into the BI landscape iscritical to deliver full value to their enterprises & customers.Thanks for reading. Please do share your thoughts.

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