Myopia = Shortsightedness. Theodore Levitt published his landmark paper titled ‘Marketing Myopia’ in 1960 that led to a paradigm shift in how companies viewed their business models. Marketing Myopia refers to ‘focusing on products rather than customers’, and how such a short-sighted view is bound to eventually lead to business failure.
One reason that short sightedness is so common is that, organizations feel that they cannot accurately predict the future. While this is a legitimate concern, it is also possible to use a whole range of business prediction 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. These techniques, though important in isolation, are much more powerful if they can be combined together for specific business scenarios. There is extensive research being done in Hexaware’s Business Intelligence & 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. Data mining 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 text data. There are various techniques used to derive high quality information from textual data, such as computational linguistics, information retrieval, statistics, machine learning, etc. Various forms of text mining include categorization, classification, clustering, concept extraction, summarization, sentiment analysis, etc.
3) Complex Event Processing (CEP): CEP is used to discover information contained in multiple events happening in parallel and then analyze its impact from the macro level as “complex event” and then help take subsequent action in real time.Primarily an event processing concept that deals with the task of processing multiple events with the goal of identifying the meaningful events within the event cloud.
4) Statistical Simulations: Predicting the future involves building mathematical models that define the relationships between different classes of variables that are important for the organization. Different types of relationships, viz. Deterministic, Stochastic, Empirical, and Heuristic are possible between the variables being modeled. Simulations allow business users and decision makers to execute the models with 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 depends on 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 specialized BPS tools like Powersim, Vensim, etc.
For BI practitioners, it is important to realize that synthesizing these techniques into the BI landscape is critical to deliver full value to their enterprises & customers.
Thanks for reading. Please do share your thoughts.