1) The document discusses how predictive analytics can help businesses overcome short-sightedness or "myopia" by more accurately predicting future outcomes and trends. 2) It outlines several techniques for predictive analytics, including data mining, text mining, complex event processing, statistical simulations, and business process simulations. 3) These techniques are most powerful when combined together to model specific business scenarios, allowing companies to make proactive, knowledge-driven decisions.

1. Predictive Analytics – The Cure for Business Myopia
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.

2. 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.