Predictive Intelligence or Intelligent Predictions?

The business world seems to be awash with buzzwords and hyperbole regarding the capacity for data analytics to provide valuable insights into operations. Terms such as ‘Big Data’, ‘Artificial Intelligence’ are being thrown around with great abandon as organisations begin to investigate whether the true value of these new technical advancements matches the hype.

From financial institutions to automotive giants, big pharmaceuticals to the retail sector, the electronic pursuit for a ‘magic wand’ to predict the outcome of every commercial scenario seems as relentless as the pursuit for success in business generally.

Sherlock Holmes once said "It is a capital mistake to theorise before one has data”. Noting the virtually endless supply of data available to organisations in the modern era, it can be just as serious a mistake to build theories from the wrong data or allow our theories to become confused by this digital abundance.

Predictive models and “expert systems” designed to tell rather than inform are becoming more frequent. The methods applied by these models can often be opaque, and with limited information on their rate of success in many business contexts, it is still the case that using models to become “informed” will often be preferable to relying on a system to simply tell us what to do. As it ever has, the real business value still resides in the availability, accessibility, relevance and reliability of the underlying data. Data possessing these characteristics, combined with the inimitable tacit knowledge of an expert stakeholder, is a truly powerful way to predict forecast with confidence.

At Shield Docs, we have been working on cutting edge techniques aimed at creating an analytical reporting environment that provides these key characteristics. One of our key ongoing development goals is to ensure that highly relevant data, at the right level of detail, is made available for key decision makers to make predictions and forecasts with ever increasing degrees of confidence.

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