Let me share some ways in which you can get a handle on your data
Among the lessons learned by corporations attempting to handle big data in the petabyte range, the most pressing was that to make effective decisions based on this information they must adopt new ways of using Business Intelligence (BI) that conform to new standards set by a burgeoning new world of data gathering.
Business Intelligence as a whole comprises all the learned strategies and techniques that assist organisations in making critical decisions based on their interpretation of the data they have at their disposal. Traditional software applications cannot accurately and efficiently deal with massive amounts of data. Further, these organisations understood that the standard set of techniques currently in use would not adequately handle the vast amount of information flowing into their companies from a variety of sources.
No! No! No!
The use of inexpensive data-gathering devices within the realm of the Internet of Things (IoT) makes data gathering very quick and often overwhelming for entities unprepared for a massive influx of information on a continuous basis. Solutions for operative analytics for enormous amounts of data continue to press CIOs into swift and decisive action.
The CIO must create efficient and timely solutions to handle the massive amounts of data streaming into their organisation. More importantly, however, they must learn to dissect the information to understand what the data is telling them. Today’s database management systems are wholly inadequate to handle big data and require massive investments into hundreds or even thousands of high-end servers to begin to assemble the ongoing, ever-increasing flow of data.
So what’s the answer?
To that end, a function called predictive analytics becomes the best solution to deal with enormous amounts of data efficiently. Within the predictive analytics model, three techniques emerge that serve to find relationships between certain conditions, patterns, and risks that aid in decision making.
Predictive modelling helps determine reasonable conclusions by predicting, based on the use of statistics and input data, the probability of a given outcome, and the use of machine learning creates a study of patterns and in turn, develops algorithms that learn how to make predictions based on data input. Machine learning is a complex subset of the computer science field that is capable of handling large datasets and can make sophisticated predictions.
Imran is a Senior Project Manager – Helping Global/Pan European FTSE 500 companies deliver multi-million-pound IT Transformation Projects. Supporting high-profile and complex strategic change covering Digital and IT Infrastructure. Outside of work and since 2009, Imran has been publishing free articles to help organisations and professionals keep up to date with Project Management, Technology and Innovation trends through his blog CALLTHEPM.COM
#Big Data, #Data Gathering, #Data Modelling, #Data Mining, #BI, #Business Intelligence, #IoT, #Predictive Analytics, #Predictive Modelling and #CIO
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