Machine learning and AI is changing how data science is leveraged

The growth and success of companies including Amazon, Facebook, Google and Uber which operate on digital platforms raise fundamental challenges for executives of established companies.

The digital natives are surfing a wave of massive change, dubbed by some as the Fourth Industrial Revolution, and recognised by Klaus Schwab, Executive Chairman of the World Economic Forum, as change that “is disrupting almost every industry in every country”.

For established companies to thrive, executives must formulate strategies that drive digital transformation, so their customers’ experiences and their costs of operation match those of the disrupters.

Companies that become AI-driven will greatly increase their chance to succeed in these times of change and disruption. Organisations use artificial intelligence, specifically machine learning, to predict what is most likely to happen next based on previous experiences, and then act to drive outcomes that satisfy customers and grow revenues.

Data is digital transformation’s primary resource. Sufficient investment in infrastructure is necessary to ensure that this resource is first harvested, and then fully utilised.

However, there are three major challenges. The first is the shortage of data scientists — the experts with programming skills, statistics and machine learning knowledge, and an understanding of the business.

Finding these experts is proving challenging and this shortage is stifling machine learning initiatives. The second is time to value: machine learning projects can take weeks or months to deliver the predictions needed for digital transformation. The third is ensuring the quality of the resulting model — vital to guaranteeing the actions taken impact earnings.

DataRobot, the pioneer of machine learning automation, was founded in 2012 to address these challenges. The company hired some of the leading data scientists on the planet — Kaggle competition winners — and set about teaching the machines how to do the job of a data scientist.

In doing so, DataRobot also opened the doors to business executives allowing them to use machine learning predictive analytics directly without involving data scientists. But the path to becoming AI-driven involves more than automation.

Success entirely depends on selecting the right projects; and so DataRobot University was launched to teach business people how to select good projects that will have real business impact.

Automation is revolutionising data science. Executives who cannot hire their way out of the data science shortage now use a platform that automates the process of deploying machine learning applications.
 This allows established companies to use their strengths — the data they have about their customers and their people experienced in operating their business processes — to put digital transformation into the hands of these experienced staff.

At the same time innovation in open source analytic languages, such as Python and R, is moving fast. Here, machine learning automation plays a big role in de-risking open source adoption.

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