Regardless of the ‘how’ resources are hired, one thing is for certain: knowledge workers must represent the interests of the company – what RM Dayton calls an ‘inside looking out’ mindset. With the a ‘less is more’ philosophy, we dare to ask, ‘why not?’ Have 20 resources on a project? Why not cut that to 10 or fewer, laser-focused, where you retain the prized intellectual capital?
A business leader at one of the best-known brands in travel and hospitality, remarked after a top gun resource started, ‘he’s really good, but he knows about many things we are not currently doing.’
Bingo. Over one year later, the resource is indispensable. A predictive model of performance was redesigned from scratch: adding new functionality while reducing error and the time to update and run the model by 2000% compared to the previous version. They’ve made new forays into strategic business simulation, where the model ‘looks at’ business questions in a unique way, to provide answers largely immune to trend and extrapolation models. Then there’s that Deep Learning Model, a long short-term (LSTM) recurrent neural network (RNN) model that will predict spatial, temporal and agent patterns of behavior.
The power of one. Never underestimate it. Just one person can make an insurmountable difference.
Companies that previously understood perhaps only a small percentage of its customers well, at discrete points in time, are getting to the point where they know more about more of their customers on a continuum. Clearly, being on the Big Data sidelines is not an option.
About a dozen years ago, businesses of all sizes began using offshore resources in droves to reduce costs. Initially, companies did save money with a labor arbitrage. Today, some companies are re-thinking this decision, deciding to re-shore, rather than to have much of their prized intellectual capital locked up in minds of behemoth offshore-based IT outsourcing organizations.
Whether companies plunged heard first into the gushing Big Data torrent or dipped their toes in with a POC, these IT outsourcing agreements and resources were largely in place. Naturally, as a convenient convergence of time and circumstance, the hammers were handed to these resources to a large degree to go build some Big Data solutions.
Some business leaders now wonder whether a commodity approach to their most-prized intellectual capital is truly in their best interest – and in the long run, they wonder, what are the true costs of outsourcing prized strategic and operational knowledge?
This introspection is pronounced for big data analytics initiatives, where customer engagement is the goal, and success or failure may well foreshadow the ongoing viability and competitiveness of the organization in certain industry verticals.
The approach of more low-cost resources seemed to work well for outsourcing call centers and technical support – you maybe really did need more bodies just to handle the volume. But for transformative development leveraging Data Science and Big Data, the right elbow reflex of the non-focused IT outsourcing firms – who want to sell you more – may not be the right prescription.
