Sangeeta Oct 07, 2014 2 Comments
Much is mentioned about the way Disruptive Technologies have stormed the field of analytics, and in particular Big Data Analytics.
Although, the term was originally popularised by the Harvard Business School professor Clayton Christensen in his book The Innovator’s Dilemma, referring largely to innovations in technology, one finds an increasing mention in analytics.
So let us examine how technology came to be referred as ‘disruptive’.
When an existing way of doing things was overturned by an innovative technology product, service or usage, it came to be known as ‘disruptive technology’.
Techopedia defines ‘Disruptive Technology’ as
“any enhanced or completely new technology that replaces and disrupts an existing technology, rendering it obsolete. It is designed to succeed similar technology that is already in use.”
This “applies to hardware, software, networks and combined technologies”.
In the domain of analytics, this definition could be extended to a new, unused, unapplied, untested alternative to existing practices which offers functional advantages for better business ROI.
Disruptive technologies in analytics may not necessarily be a cheaper solution. Rather it suggests that on deployment, the technology offers business value and efficacy by solving problems faster. Such technologies encourage continued exponential improvements in performance and core competency, rewriting rules of businesses, creating new markets and generally fostering deep analytics talent.
Technologies that have ‘Disrupted’ Analytics
Why Disruptive Technologies are core to analytics?
Disruptive Technologies shake up the analytics life cycle with ground-breaking products or innovations in applications, computation capabilities and data-driven predictive models to name a few. As many of the disruptive technologies merge seamlessly, analytic applications are also becoming ubiquitous across the various channels and devices.
For instance, banking companies are harnessing cloud computing services for storage and harvesting of data, on-demand BI services and fraud detection with embedded data analytics software; using social media and mobile for CRM and business growth. In retail and digital marketing sectors, innovative algorithms and progressive applications leverage disruptive technologies (cloud, embedded apps and sensors, social media, disruptive online advertising, mobile internet) for predicting demand, improving products and services, targeting customers, increasing sales and business ROI.
Bottomline –Analytics, especially in the era of Big Data, is unthinkable without disruptive technologies. The constant endeavour to improvise, innovate and devise innovation in technology and applications, are important for high granularity of data, high velocity of data and deeper analytic insights.