
<p>Feeling overwhelmed with information? It’s not surprising. Presently, data is gathered and shared at an unprecedented rate and it’s a trend that’s accelerating. Data volume is predicted to <a href="https://www.enterprisestorageforum.com/storage-management/worldwide-data-will-surge-to-163-zettabytes-by-2025.html" rel="noopener noreferrer" target="_blank">increase tenfold</a> by 2025 to 163 zettabytes per year, generated by a growing number of connected devices that will reach 20.4 billion by 2020, <a href="https://www.zdnet.com/article/iot-devices-will-outnumber-the-worlds-population-this-year-for-the-first-time/" target="_blank" rel="noreferrer noopener nofollow">according to analysts Gartner</a>.</p>



<p>That’s mind-boggling, but shouldn’t scare us, because the technologies we use to analyze Big Data are also changing. With the Internet of Things, AI, and cloud technology combining in ways that store and process masses of data, we need to implement smarter ways of analyzing and using Big Data. </p>



<p>It involves fully embracing the results we can get from new data technologies: machine learning and augmented intelligence.</p>



<h2 class="wp-block-heading">Machine learning and augmented intelligence: A new dimension in understanding</h2>



<p><a href="https://www.sisense.com/blog/predictive-analytics-machine-learning-dog-memes/">Machine learning</a> means that devices now do more than simply provide us with raw data. They can provide genuinely instructive results themselves. It’s now possible to interact with devices and learn immediately from them. </p>



<p>Even more revolutionary, intelligent machines can themselves analyze and learn from the very data they generate. This is when artificial intelligence becomes augmented intelligence because the relationship between devices, analytics, and users becomes a positive feedback loop. </p>



<p>The more an intelligent device is used, the more data it generates about its use and the more we can learn from it. <strong>Plus</strong> intelligent devices themselves can learn from their use. The result: we can constantly calibrate and enhance devices’ performance and the performance of the processes in which they participate. <br>Let’s take a look at a couple of examples.</p>



<h2 class="wp-block-heading">Augmented Intelligence: Data so good that we can sleep easy</h2>



<p>Just recently, the health technology giant <a href="https://markets.businessinsider.com/news/stocks/philips-unveils-trilogy-evo-and-growing-portfolio-of-connected-solutions-at-ers-2018-1027535149" rel="noreferrer noopener nofollow" target="_blank">Royal Philips announced new advanced connected solutions</a> for chronic respiratory and sleep conditions such as COPD (chronic obstructive pulmonary disorder) and sleep apnea. </p>



<p>These solutions include devices connected to a cloud-based system so that patients’ conditions can be consistently monitored, and medical professionals and care providers can coordinate care from hospital to home.</p>



<p><strong>Learn how to redefine Big Data analytics with AI:</strong></p>



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<p>Current devices collect data that are used to track patients’ conditions and the progress of their treatment.  For example, respiratory devices like CPAP (continuous positive air pressure) machines contain an SD card slot to collect data. </p>



<p>Medical professionals then need to take the data from the device, so that they can crunch the numbers and alter treatment strategies accordingly. New technology takes data analysis to the next level. It’s more intelligent and more nimble. It monitors patients as they sleep and sends data directly from the machine to caregivers so they can calibrate therapies, prescriptions, and treatment plans far more rapidly as patients’ conditions change.</p>



<h2 class="wp-block-heading">. . . So innovative, we can enhance our child care</h2>



<p>Similarly, for new parents anxious that their babies are sleeping well, augmented intelligence on an IoT device can really benefit both the parents and the infants. A device identified as <a href="https://time.com/collection/best-inventions-2018/5454441/nanit-plus-baby-monitor/" rel="noopener noreferrer" target="_blank">one of 2018’s best inventions</a> is more than just a traditional baby monitor, because it uses computer vision and machine learning to track baby’s movements and its sleeping conditions (temperature, humidity, darkness in the room, etc.) and it informs parents what they can do to make their little one as comfortable as possible.</p>



<p>Parents can even view an analysis of the baby’s sleep habits on a dashboard, with visual indications whether their infant had a good night, and they can watch video clips of the baby sleeping. As the device learns a baby’s sleeping behavior, the insights it gives parents will ultimately help the whole family enjoy that most valuable of commodities: a good night’s sleep!</p>



<h2 class="wp-block-heading">And machines so clever, they can smell</h2>



<p>What’s remarkable about these examples is that enhanced machine intelligence can create new types of datasets. They’re no longer just derived from numerical or visual sources. Movement, sound, and even odor can provide valuable information. </p>



<p>For instance, in the UK, the brainiacs at Loughborough University’s data science team have collaborated with medical experts at the Edinburgh Cancer Centre to develop the technology that uses <a href="https://theconversation.com/ai-is-acquiring-a-sense-of-smell-that-can-detect-illnesses-in-human-breath-97627" rel="noopener noreferrer" target="_blank">AI to identify smells in human breath, which can indicate illness</a>. </p>



<p>Intelligent, deep learning networks were used in the research that learned more and more from each sample used until the team could recognize patterns that revealed specific chemical compounds in the breath.</p>



<h2 class="wp-block-heading">The need for change</h2>



<p>These examples show the extent to which advanced data capture and <a href="https://www.sisense.com/blog/how-machine-learning-improves-data-visualization/">machine learning</a> capabilities can benefit us in new ways. They also show why we’re accessing more sources of data and why we’re handling and analyzing an escalating volume of data. </p>



<p>To do so most effectively requires data classification and processing to be supercharged, otherwise, organizations risk overlooking potentially valuable findings within this growing mass of available data. In order to cope with this, we need to re-think how we handle and analyze data in huge volumes. </p>



<p>We need to redefine Big Data analytics with AI.</p>



<p><strong>To find out more about how to achieve this:</strong></p>



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