How machine learning uses data for predictive analytics

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Data, data, and more data

Companies in virtually all industries possess more data than they know what to do with. These vast troves of data present challenges: How should the data be stored? Can it be kept secure? What data should be kept and for how long? Most importantly, how do you use it to create actionable insights and translate them into meaningful decisions and changes?
Most companies store and track data, but struggle to put most of that data to work for their business. Yet, 6 of 10 C-level executives are convinced that AI applications will soon be able to use this massive amount of data to develop competitive advantages. These “data lakes” are like vast sums of money sitting in a low-interest savings account while business leaders research where to best invest them. But how can companies use AI and ML to make their data work for them?

ML, AI and evolving business culture

Whether in the healthcare, education or entertainment industries, data and ML are causing shifts in the workplace and its culture. Many companies have already shifted employee responsibilities toward creative and human-interactive tasks that computers cannot do because the more mundane and rote responsibilities are handled far more efficiently by ML applications.
Business cultures have also shifted toward community models, where teamwork is coordinated by AI. Decisions are becoming more data- and probability-based due to data analysis, which allows managers to be more flexible as ML applications demonstrate the need for strategic changes. This leads to a more fluid approach to business.

Many C-level executives believe that AI, specifically ML, are essential elements of their business models today and will have a growing impact tomorrow. As AI increasingly handles administrative tasks, managerial roles will evolve to include more strategic decision making, interaction with direct reports, and creative innovation.
Managers who are merely good at managing administrative tasks may struggle, while those with soft skills, strategic mindsets, and innovative outlooks will grow more valuable. Gartner estimates that by 2021 AI will generate $2.9 billion in business value and save 6.2 billion hours of worker productivity.

How ML helps businesses use data

For business leaders, ultimately the billion-dollar question is, “How can ML use this data to help them improve solutions, boost revenue, and increase customer satisfaction?” ML advancements in the healthcare industry provide stunning real-life examples.
The Sarah Cannon organization, a cancer care network for HCA Health, uses an AI application called Digital Reasoning to help its cancer care navigator team enhance the patient experience. Cancer care is a complex process, leaving most patients feeling overwhelmed. Cancer care navigators help them find the right provider, book appointments, and choose treatment options.

Prior to AI, navigators had to spend blocks of time manually reviewing patient data to determine which patients they needed to call. Now, Digital Reasoning does this for them, freeing navigators to spend more of their time focusing on their most important job- interacting with and helping patients.

The AI revolution is well underway. Changes in how people work and in the skills companies need will continue to disrupt industries, but mostly in a positive way. A future of data-centric decision making and freedom from rote tasks stands to increase productivity and foster economic expansion.

Need help determining how AI and ML can enhance your business’ strategy, culture, productivity and consumer offerings? Contact us. To learn more about machine learning in healthcare, download the complimentary ebook.



How machine learning uses data for predictive analytics