Millions of asthma sufferers across the globe depend on Aerocrine monitoring devices. Aerocrine is a Sweden-based medical technology company that provides tools to measure airway inflammation that helps doctors diagnose, monitor and optimise therapy for patients. Since the devices are sensitive to small changes in ambient environment, Aerocrine started using a cloud analytics solution based on Microsoft Azure, which allows device data to be seen real time. This not only helps the company relay valuable information to end users in real time, but it also enables them to predict when consumable sensors will need to be replenished.
One of our own customers, the world’s largest provider of healthcare and manufacturing technology, was looking to monitor hand hygiene compliance to reduce transmission of Hospital Acquired Infections (HAIs) to patients using an IoT-based solution. HAIs have resulted in 99,000 deaths each year and cost $3-4 billion in healthcare. Using a cloud-based hospital hygiene system, individual staff interactions with a hand-sanitizer dispenser were captured and recorded using Real-time Location System (RTLS) technology attached to employee badges and sanitizer dispensers. This data was then utilized to accurately understand the compliance levels of hospital staff, help monitor and modify behavior. This improved the compliance levels by 25% on an average, thus saving the lives of thousands of patients.
These are just two examples, but there are many more to show how cloud + analytics is one potent combination.
How cloud can solve the Data problem
Today’s connected customers engage with your brand using multiple channels, in addition to stores. There is an exponential growth of data arising from various channels – transactional and social, which could be in the form of text or voice. But to be truly useful, customer analytics is only as effective as the authenticity of the results, which in turn often hinges on the quality of data available.
There are 4 major parameters to handle big data– Volume, Velocity, Variety and Veracity. The good news is that cloud computing can support big data and analytics in all these areas.
Cloud expands your ability to add storage dynamically, increase computing power on demand and allows for use of global distributed data centers for localized processing.
Cloud networks can facilitate data collection with a very low latency. It also enables real time event processing as well as distribution of notifications and alerts in a timely manner.
Cloud supports relational, No SQL and Blob data and gives the ability to process and enrich all kinds of data using Hadoop. It also enables the combination of relational and non-relational data in one service.
Cloud supports usage of cleansing tools to clean and filter data. At the same time, it also allows the cleansed and enriched data to be loaded into a data warehouse.
Customer Analytics has been evolving rapidly from reactive to proactive. What started off as a process to analyze past performance rapidly moved to predictive analysis aided by complex statistical models, often powered by the cloud. Today, we can analyze past and real-time structured and unstructured data to build complex prescriptive analytics models that are of value not only in marketing but also in service, sales, IT and operations.