“If you cannot measure it, you cannot improve it.” - Lord Kelvin, 19th century scientist and mathematician.
Kelvin’s observation was originally made about theoretical physics, but its relevance has only expanded since. He’d certainly be pleasantly surprised by its broad applicability today, especially to the power of analytics and big data to help companies measure, evaluate, and define plans to optimize their operations.
“Big Data”, by comparison, is a much more recent term, and it’s really about aggregating and analyzing large amounts of data to reveal patterns, trends, and associations. Two great contemporary examples of Big Data in action are Netflix’s recommendation engine, which uses a variety of complex variables to rate the likelihood you’ll enjoy a show, and Facebook, which relies on your profile and behavioral data to serve up targeted posts and advertisements.
In pharmacy, for example, a medication’s journey from manufacturer to the bedside generates a huge number of transactional data points that make manual tracking time-consuming and difficult. The great news, though, is that technology and cloud computing advances can help manage this data and turn doses into trackable, knowable items that contain valuable context about patients, inventory, and more.
In the same way that your movie choices help improve future recommendations, empowering pharmacy with insights into medications’ history and journey can help them better predict future needs and improve outcomes. The ability to apply Big Data — across any industry (not just pharmacy) — depends upon the combination of cloud-based intelligence, interoperable platforms and analytics support to ensure your data is consistent and reliable.
Cloud Computing Connects Data
Cloud-computing makes it easier to interconnect data sets, which can be run through analytics pipelines to provide valuable insights to consumers of data. This helps improve visibility and provide more actionable insights across the operation. For example, mapping unique identifiers across disparate data sets allows better across systems and lets features like barcode-assisted inventory management operate with improved veracity.
Consistently mapped identifiers also allow systems to talk in a more similar language. When the inventory is modified in one system, the update should take place in other systems without delay. If connected systems can’t translate across identifiers, it creates confusion between systems; this leads to delays, duplicate data entry, and a higher risk of inaccurate data. Fortunately, cloud-built intelligent layers normalize, combine, and aggregate medication data across EHR (electronic health records) instances, sites, and health systems to leverage potential of this data.
The adoption of cloud-based intelligence is one of the critical tools applied to ensure data quality. Cloud-based platforms provide data and analytics capacity that enable many tools, such as machine learning, to evaluate data. And the real-time connectivity ensures results are relevant and reliable and allows teams to make quick, accurate decisions.
With big data, there’s no end to the possibilities. But cloud intelligence, interoperable systems and analytics support are the foundation for managing data and making informed decisions to maximize business operations.
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