Updated: Sep 22, 2021
From the “recalibration” periodic repeat business model to the “real-time analytics” ongoing subscription business model
This is the last entry in the Heybrook West blog series discussing Four ways that Big Data, Data Science, and Predictive Analytics must evolve and innovate. Revisit all Age of Disruption series entries: (1) | (2) | (3) | (4) | (5)
It may sound ironic, but as things get less predictable, our clients will need predictive analytics more than ever. We must transition from a one-off consulting model, from a repeat-business recalibration model, to an always-on subscription-based model.
The best service providers will offer subscription services that monitor data as it gets created. This involves two stages: (1) helping clients set up the pipelines that move data from the generators into the data warehouses, then (2) writing, refining, and updating the algorithms that make that data into reportable facts.
Most importantly in the Age of Disruption, our value-add proposition will be the immediate alert that a disruption is arriving. There’s a lot of criticism out there about how February 2020 was a “wasted” month when the needs of the oncoming pandemic were obvious. Manufacturers could have gotten to work on ventilators, but they didn’t. They could have scaled up their PPE operations to have gloves and gowns and masks ready, but they didn’t. In the next disruption, it’s unlikely to be ventilators that we need: perhaps portable electrical generators or water purifiers or daily medications will be in dire shortage.
Information coming from smart-phone locator beacons, security barriers and checkpoints, plus the upcoming mass deployment temperature and health scanners, will need to be processed as close to real-time as possible. Early detection of a threat to the health of employees and customers will be key to protecting public health. Early detection of the first signs of an oncoming disruption will be key to staying in business during the Age of Disruption – but it requires strong analytics to identify what those signs are and be able to see them as soon as they start happening.
Real-time analytics means staying on top of these rapid changes as they emerge. It means avoiding the phase of knowing what will probably be needed but waiting for someone else to give the go-ahead. The data will show us where things are pointing and give us enough of a reason to start, without waiting for others. Big data analysts will need to support their clients daily, making sure that data is flowing through the pipes properly. An ongoing (subscription) service model will make more sense than a project-by-project business model.
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