People produce a huge amount of data — research firm Frost & Sullivan projects global data traffic to cross 100 zettabytes annually by 2025. Because of this, it is no surprise that big data is transforming industries all over the world, and becoming a top priority for many organizations. According to Frost & Sullivan, more than half of Fortune 1000 firms report having big data initiatives in place across the enterprise. With the right analytics practices in place, organizations can use big data to increase efficiency, cut costs and make better-informed decisions.
To get the most out of big data and analytics, however, organizations need to make several significant investments: digital infrastructure, data science skills and an enterprise-wide strategy. Because of this, many have found themselves behind the curve in adopting big data and analytics, and in seeing results as effective as they could be. As the amount of data in an organization’s possession grows, the more difficult it becomes to extract meaningful insights from it.
This is especially true in the healthcare industry, which produces and stores more data than they know what to do with. From electronic health records to digital scans and patient data tracked by smartphone apps and wearables, a single patient’s full medical history may be comprised of several different types of data. Healthcare organizations strive to turn this deluge of data into something actionable.
Increasingly, healthcare organizations are turning to industrial machine learning (IML) – a scalable solution for ingesting data, building algorithms and generating continuous insights. In using digital platforms to bring data from different sources together and automate data-driven experiments on an enterprise-wide scale, hospitals are better able to get a full grasp of their data and use it to make predictions about costs, efficiencies and patient experiences.
Read the full white paper to learn how tools like machine learning have helped hospitals take control of their data, make care more personal, solve problems like reducing patient recovery time and derive predictive insights faster.
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