In the age of convergence, customer churn is a concern for service providers, challenging most retention techniques...
Data has been existent since time immemorial and human brains have been processing and analyzing data from cradle to grave in varied forms – on finger tips, abacus, tally, excel sheets, computational algorithms et al. I presume other living things around us do analyze data as well – a leopard hunting a gazelle at high speeds, formation flight of migratory birds, the precision swoop of an eagle to grab its prey etc. Does anyone out there know what technologies or algorithms they use?
Ancient civilizations predicted weather, seasons, complex astronomical and celestial events. Occult sciences like astrology, palmistry, numerology etc. have studied and analyzed patterns to predict the planetary influence vis-à-vis almanac through the medium of mathematics. While the validity of information is not scientifically proven, the fact remains that analytics has been existent in myriad forms.
Explorations of minerals, gold, oil, natural gas have all started with extracting intelligence from data. The quest to dig for intelligence in data has increased with the advent of technology, ease of collection of data and dynamic sea change in storage capabilities.
Historically, human intuition and experience have been influencing decision making. Till recently, though the fact that data contained intelligence was well known, cynicism and fear of the ‘unknown ‘ acted as an inertia and impaired the organizational quest for analytics.
Enterprises have a comfort level of dealing with ‘known-known’ and willing to invest in ‘known-unknown’ with a little bit of skepticism. When it comes to discussing ‘unknown-unknown’ there is an inherent fear. An old adage says “ Many great ideas were lost since the people who had them couldn’t stand being laughed at” ! As an example, if Apple would not have given us the experience of ‘touch’, it would have continued to be an ‘unknown-unknown’.
In my experience of speaking to various senior executives around the globe, I see an inherent complacency and resistance to look for things better than the existing business intelligence. It is well known that any pathology report (akin to BI reports) after a blood test gives an individual his/her value of various parameters and the standard range. If this data could help the common man predict or diagnose, then we would be putting the clinicians and pathologists out of their jobs.. We need experts to analyze when multiple parameters and complex interactions are involved.
Similarly Big Data or Enterprise level data needs expert analysis to predict fruitful business outcomes, intelligent decision making, avoid waste, fraud etc. With globalization and business being done in multiple languages, Natural Language Processing (NLP) becomes a key element to normalize the field.
We at iQGateway use cutting edge tools and various state-of-the-art techniques to build customized analytical models to address specific problems of the industry.