Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data. Its goal is to find, learn, and recognize patterns in complex data, for example in images, speech, biological pathways, the internet.
The fallacy in many companies is that the accumulation of data represents wealth as opposed to the exploitation of the data. Successful companies are going to look past the empirical data and look at data through patterns and correlations – driving more valuable insights and actions.
By combining a deep pattern and contextual analysis of your data with a deep pattern and contextual analysis of one or more targeted external data sets you can reveal valuable intersections of those collections of data, yielding opportunities and business value – exposing specific opportunities you were not aware of.
Pattern detection and contextual analysis create the ability for a much larger percentage of the company to take advantage of data to make decisions – no longer limiting access and value creation to Data Science experts. Pattern detection and contextual analysis makes the data consumable by all, and can be used to augment the imagination of everyone in the workforce.
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