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Telling the future of energy with data

When Facebook shows you a list of friends you may know, Google letting you know an ETD to be on time for a meeting, and other e-commerce websites giving you recommendations on things to purchase, these are instances where machine learning is carried out on large volumes of data called Big Data.

According to Gartner, the big data market is worth over $250 billion and surely it is here to stay. Businesses of all sizes that deal with various applications have started to adopt these practices.

Companies are now focused on how to store and manage this voluminous data. How should we architect the business’ technology stack to gain value from Big Data in terms of HDFS, complex event processing, NoSQL and machine learning? Store data on prem or cloud?

By means of advanced analytics, and machine learning, companies tap into their insight-rich vein of experience and mine it to automatically discover and generate predictive models to take advantage of all the data they are capturing. Departing from the traditional style of looking into the past for insights, companies can now predict parameters that they want knowledge about.

The value of machine learning is in finding structures that we have never seen before and precisely modelling to assist in decision making.

At​ TTC, we are leveraging these to build intelligent models that can serve our customers recommendations about optimising their usage patterns and first hand information about dynamic pricing for compliant infrastructures. We are developing these models in the energy sector where machine learning is hyper critical

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