Data granularity. Why it matters.
Most data is collected and extrapolated beyond the needs of the application. This results in bad data, misleading data and at worse unusuable data. The analysts who do this – many and varied – hold a McDonalds view of data – more data calories are better.
Yet by defining data granularity, 2thinknow analysts can focus on the data that makes a difference. Without the costs of collecting the data that doesn’t.
Many analysts convey false confidence in their data, and never define their degree of confidence. Instead they assume an omnipotent always right approach. this is conversely why their economic forecasts and analysis is always wrong.
2thinknow give our select service clients and product customers, data that we can express a degree of confidence in.
Whether assigning a probability, measuring a city for innovation, publishing an analyst report or analysing a industry disruption, 2thinknow express data in terms of degrees of confidence.
By highlighting the degree of confidence uncertainty is conversely minimised.
2thinknow believe this is a more commercial, sensible and useful approach to data, and offers our client a choice of scenarios rather than a single dominant ‘infallible scenario’. This encompasses new proprietary techniques that address unforeseen ‘Black Swan’ events.