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The business value of text analytics is fairly straightforward. The large amounts of text based data most organizations possess, acquired and managed at considerable cost, can be analysed in such a way that insights can be gained to improve the efficacy and efficiency of business operations. Text based data are an untapped resource in many organizations. Structured data (customer details held in a database for example) on the other hand are very well exploited, primarily because they are specifically formatted for computer processing.
While unstructured data, primarily text, is well suited for human communication, there have been significant hurdles to overcome to make it amenable to processing by computer systems. The
se barriers have been slowly eroded to the extent that significant value can now be extracted from text.This is something of an irony since text based data typically accounts for eighty per cent of the data most organizations generate and process. Emails, documents, social data, comments and a wide variety of other text are usually archived, but typically not analysed in any meaningful way. The cost of creating, capturing and managing this information is considerable. In a service based business most employees can easily be categorized as information workers, and the cost of the information they generate is directly related to associated labour costs. Viewed in this way the cost of text data in many organizations is in excess of fifty per cent of all costs. Clearly any technology capable of automating the extraction of useful information from these data should be of interest.
The application of text analytics technologies has grown rapidly with increased regulation, the proliferation of social data, and efforts to record the thoughts and comments of customers. Embedded in the terabytes of unstructured data are patterns which can serve a diverse range of purposes, from flagging when a customer is likely to close their account, through to fraud detection. The value of text analytics is amplified when both structured and text data are combined, and to this end text mining technologies are witnessing significant uptake. In this scenario text data are converted into a form where they can be merged with structured data from transactional systems and are then scrutinized by data mining technologies, whose sole purpose is to uncover hidden structure in data and reveal exploitable patterns. It is then crucial that these patterns can be deployed in a production environment, with full monitoring of performance as scoring is performed on new incoming data. Managers will not be confident unless they can assess the benefits a predictive model is bringing on a real-time, ongoing basis.
To realize value from the very large sums invested in creating text data an organization needs to carefully plan and execute a business led initiative. This involves identification of business processes where text analytics might add value, the creation of text analytics capability, and a feedback loop in which information capture is informed by the outcome of analytics processes. This latter point is crucial, but somewhat surprisingly is often not mentioned by suppliers and consultants in this domain. If a certain type of information generates useful patterns then it becomes important to understand why, and attempt the capture of other information which might amplify the value of the analytics process.
Underlying all of this is some fairly simple economics – the cost of discovering and exploiting information derived from text analytics should be less than the value realized. Fortunately analytics often produces measurable outcomes captured by metrics such as lift. A two per cent increase in lift can mean a very considerable return on text analytics investments in many customer and marketing oriented activities.
Finally it should be noted that the scale and scope of text analytics will be accelerated by the current developments in big data technologies. The most heavily visited topic on the butleranalytics.com web site is text mining. We predict that this will become the largest growth area within the data analytics space, and a key differentiator in the benefits organizations reap from their analytics activities.