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Machine Learning Fortifies Digital Transformation for Analytics

Organizations are adding machine learning to their analytics tools as digital transformation calls for a way to use and analyze collected data.

machine learning, digital transformation

Source: Thinkstock

By Elizabeth O'Dowd

- Data volume continues to be a challenge for healthcare organizations as they go through digital transformations. Machine learning is one of the most intriguing emerging technologies entities are utilizing to deal with data and analytics.

Artificial intelligence (AI) solutions such as machine learning take a lot of IT infrastructure planning before they can be implanted successfully. Unplanned and unsuccessful deployments can cost organizations a lot of time and money to fix.

Gartner closely ties machine learning to data science in its latest magic quadrant report as machine learning has influenced and improved data science over the past year.

The analytics market is expanding rapidly across all verticals, including healthcare, because of the advancements being applied to the technology. The platforms are gaining more intelligent capabilities as deep learning tools becomes more affordable and accessible.

By 2020, Gartner predicts that 40 percent of enterprises will have invested in advanced analytics tools.

The research firm defines a data science and machine learning platform as, “a cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products.”

Machine learning is used mostly for image scans. The AI tools can read images and apply that data to situations much like the human brain would.

These tools are available both on-premises and in the cloud. Organizations also have the option to choose healthcare specific tools that are built to deal with the different kinds of medical data.

Connected medical and IoT devices generate more data that can be used to gain further insight into individual patient health, as well as contribute to population health. This calls for more scalable solutions that can take a hybrid cloud and on-premises approach to combining data.

Cohesively integrating data science and machine learning into a single platform is key to making the tool feel interoperable from data gathering to data use. The platform supports data scientists performing tasks, including data access and ingestion, data preparation, interactive exploration and visualization, testing, training, and advanced modeling.

“Many organizations are awaiting the arrival of new platforms that will enable them to extend their current infrastructure and work well with their other technology,” Gartner report authors explained. “Users must be aware of the rapid changes in this market and monitor how vendors and other organizations are responding to those changes.”

“They should regularly assess the state of the market and the ability of their current vendor to respond and adapt,” report authors continued. “In addition, they should consider extending their analytic capability to include descriptive, diagnostic, predictive and prescriptive capabilities in a cohesive manner.”

Before deciding on a data science platform, healthcare organizations need to be sure that is fits with their current IT infrastructure. Many entities are looking into analytics tools that include machine learning for the first time. Adding in a completely new technology can cause interoperability issues.

“Data, people and process must also be addressed,” Gartner advised. “Data science and machine-learning approaches require increasingly accurate data to build models representative of the real world. Information management therefore has an important role to play by helping to ensure that models are based on sound inputs and practices.”

Healthcare AI is still in its beginning phases. While many organizations do have some type of AI tool in their health IT infrastructure, there are still more ways it can be applied in the future.

Analytics and data science is a good place to start for organizations interested in pursuing AI because of the amount of unstructured data being collected and the demand for a way to make that data actionable.


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