Excerpted from this interesting article by Brian Solis:
"While the amount of personal and ambient information churned out by SoLoMo is often inundating or even perplexing, it is this “big” data that will help businesses evolve and adapt in a new era of connected consumerism. More importantly, the study and understanding of relevant big data will shift organizations from simply reacting to trends to predicting the next disruption and adapting ahead of competition—thus, marking the shift from rigid to adaptive business models.
Without interpretation, insight and the ability to put knowledge to work, any investment in technology and resources is premature. But, by investing in human capital to make sense of would be ominous data, organizations can modernize the role of business intelligence to introduce a human touch.
The reality is though that how organizations connected with customers yesterday is not how customers will be served tomorrow. Meaning, the entire infrastructure in how we market, sell, help, and create now requires companies to not only study data and behavior but also change how it thinks about customers.
I refer to the confluence of data and interpretation as the human algorithm—the ability to humanize technology and data to put a face, personality, and voice to the need and chance for change. Data tells a story, it just needs help finding its rhythm and rhyme.
The human algorithm is part understanding and part communication. The ability to communicate and apply insights internally and externally is the key to unlocking opportunities to earn relevance. Beyond research, beyond intelligence, the human algorithm is a function of extracting insights with intention, humanizing trends ad possibilities and working with strategists to improve and innovate everything from processes to products to overall experiences.
The idea of the human algorithm is to serve as the human counterpart to the abundance of new social intelligence and listening platforms hitting the market every day. Someone has to be on the other side of data to interpret it beyond routine..."
Read full original article here:
Via Giuseppe Mauriello