Around the turn of the century, we experienced a similar moment of euphoria when retail outlets opened 'virtual stores' and sold products to online buyers. A famous IBM TV ad once depicted an overwhelmed young company whose products went from a few online orders a day to hundreds of thousands. In many respects we have come full circle and are back at the starting gate of yet another era of unprecedented growth only this time instead of millions of orders, the focus is on zillions of data points.
In 2000 CEOs focused primarily on IT integration and supply chain strategies to fulfill a surge of orders. Their managers implemented the latest e-commerce packages, leveraged the cloud to reduce costs, broadened and compressed their global supply chains, and trained their workforce to adapt new work flows. Success was determined from a customer's positive experience, measured primarily by the number of accurate and timely deliveries.
Today, the paradigm has shifted away from a transaction centric one to customer centric. Companies no longer wait for customers to buy but instead develop sophisticated algorithms that can compare a specific customer’s purchase history with multiple data sets including credit rating reports, recent purchases, and most extraordinarily, their genuine propensity to buy based upon the web pages they most commonly visit. Surprisingly, web behavioral data has become a powerful data complement that can offer unprecedented efficiency benefits to both the merchant and the consumer. Customers receive compelling suggestions, while stores inventory the products their customers will most likely purchase. It's a win-win for both. Issues of privacy remain a sticking point for some individuals, but, as the benefits to the consumer improve, even these issues are expected to become less significant.
Striking the optimal balance will be tricky especially when the journey also involves flogging through mounds of unstructured web data. One approach being talked up within academic circles is systems thinking.
MIT’s SDM Conference - (sdm.mit.edu (http://sdm.mit.edu/
At a recent Systems Design Management (SDM) conference at MIT called “A Systems Approach to Big Data: Going Beyond the Numbers” (http://sdm.mit.edu/
Morrison divided 'Systems Thinking' into various key areas. First off was 'Dynamic Complexity', which evaluates reactions when a smooth-running assembly line becomes inadvertently interrupted;
Another key area is 'Stocks and Flows', which Morrison dubbed humorously as 'Bathtub Dynamics'. Similar to balancing the water level in a bathtub with running water, systems thinking can help calibrate inflows (i.e. inventory-build up) versus outflows (i.e. sales). The depth of the bathtub is determined by a company’s internal competitive advantage. These advantages vary widely but with regards to the alignment of systems thinking with big data, Morrison focused on skills training as a key differentiator. He highlighted his points with a case study from a US motorcycle manufacturer, Harley Davidson.
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