Move over FinTech, SleepTech is on the rise

26

October

2017

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2017 is the year in which smart sleeping became a thing – No, really. Earlier this year, at the Consumer Electronics show, several major electronics companies showcased their sleeping solutions, ranging from snooze-inducing headphones to posture-adjusting mattresses. Reportedly, Americans spent over $43 billion on sleep aids and tech in 2016 alone, with experts predicting even more growth in the years to follow (Hollander, 2017). By 2020, it is expected that the global sleep market will be worth $80bn (Gabbatt, 2017). No wonder tech companies can’t wait to tap into the market!

Tracking your sleeping habits is nothing new; Apple already introduced its bedtime application in 2016, which sends notifications when bedtime approaches and allows users to view their sleep tracking data. Current developments, however, take this one step further as tech companies are developing a range of devices that can be connected to applications or devices such as Amazon’s Alexa, expanding the IoT.

One of these solutions is Matrix’ smart mattress, which tracks your heart rate variability (the inconsistency between your heartbeats). Another solution is duvet climate control. The Smartduvet Breeze makes use of an inflatable sheet between the duvet and duvet cover, and even includes a self-bed-making feature (Hollander, 2017)! And for those really into sleep tracking, there is an app on the market which allows you to record your snoring (including whatever you might say in your sleep). With so many sleeping statistics readily available, however, consumers may feel a little lost with the abundance of data that solutions like these generate.

A psychologist specializing in sleep disorders comments:
“There’s an inherent problem because the consumer world has come up with all these ways to monitor your body signals, but the clinical world didn’t come up with a way to answer all the questions it brings about (Thielking, 2017).”

Devices promise to provide solutions to insomnia, sleep apneas, snoring and other sleep problems, but many are not integrated with medical records yet, or supported by GPs. Consumers simply do not know what conclusions to derive from their sleeping data. SleepTech may be on the rise, but only when the medical world catches up with the developments, large scale adoption will take place.

References:
Gabbatt, A. (2017). Don’t lose your snooze: the technology that’s promising a better night’s sleep. [online] the Guardian. Available at: https://www.theguardian.com/technology/2017/jan/05/sleep-technology-ces-2017-las-vegas-new-products [Accessed 20 Oct. 2017].

Hollander, M. (2017). Best sleep gadgets – Still tired? Smart sleep aids can help improve your snooze. [online] Digitaltrends. Available at: https://www.digitaltrends.com/health-fitness/best-sleep-gadgets/ [Accessed 20 Oct. 2017].

Thielking, M. (2017). From vibrating pillowcases to smart pajama belts, sleep tech is flooding the market. [online] STAT. Available at: https://www.statnews.com/2017/01/06/sleep-tech-science/ [Accessed 20 Oct. 2017].

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Would you care for a Digital Twin?

11

October

2017

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In october 2016, Gartner already predicted that digital twins would be one of the five major strategic trends for 2017. Gartner’s publication resulted in the birth of a new buzzword and a true explosion of online articles, most of which simply repeated Gartner’s findings and providing few new insights. Therefore, the question remains: what does the concept actually entail?

Originally, the concept of a “digital twin” was a virtual representation of a manufactured good, which functioned to compare the original design with the produced asset (Grieves and Vickers, 2001). The virtual twin would consist of a digital model and the required bill of materials (in other words, a blueprint for production) or could be a list of the required steps for production and data on current operational states of the product (measured by sensors).

Over the years, the definition evolved somewhat, so that it now entails a physical product and a virtual product, and the connection of data to tie both the virtual and real product together (Shilova, 2017). The focus no longer lies on accurately executing the design, but shifted to offering simulations of systems and products using real-time data.

According to Oracle (2017), digital twins offer a number of benefits. Most importantly, digital twins provide insights into operations of machines, whether they are stand-alone or part of a larger interconnected system. In addition, by combining the digital twin with mathematical modeling techniques, future states of the machine can be predicted, making it easier to prepare for or prevent breakdowns. The twin can be used for scenario analysis as well, as long as the interface allows for interaction with the model, so that different parameters can be set. If the digital twin is designed to be compatible with backend business applications, such as manufacturing, procurement or logistics systems, the concept could be used to further optimize supply chain operations.

The Internet of Things, the network of interconnected devices, serves as an enabler for the digital twin technology. It is no surprise then that with the recent advances in the (industrial) IoT landscape, digital twins have gained momentum too, despite having been around for a while.

Even though the technology currently mainly focuses on industrial manufacturing, one cannot help but wonder whether its applications will extend to mankind in the future. Using sensors and monitoring patient health “states” is already commonplace in hospitals, and creating a personal digital twin would only take current developments one step further. Applications could also extend to insurance fraud prevention. With digital twins having real-time health data available, checking the legitimacy of insurance claims would be greatly simplified. Of course, as with any data collection technique or application, this would raise safety and security concerns. The future will show whether privacy wins from transparency.

References:

Gartner inc. 2016. Gartner’s Top 10 Strategic Technology Trends For 2017. 26th October. Forbes. [Online]. [5 October 2017]. Available from: https://www.forbes.com/sites/gartnergroup/2016/10/26/gartners-top-10-strategic-technology-trends-for-2017/

Grieves, M. and Vickers, J. 2001. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (Excerpt). n.d. [Online]. [6 October 2017]. Available from: http://research.fit.edu/camid/documents/doc_mgr/1221/Origin%20and%20Types%20of%20the%20Digital %20Twin.pdf

Oracle. 2017. Digital Twins for IoT Applications: A Comprehensive Approach to Implementing IoT Digital Twins. n.d. [Online]. [6 October 2017}. Available from:

Click to access digital-twins-for-iot-apps-wp-3491953.pdf

Shilova, M. 2017. Digital Twin as a Strategic Technology Trend. 29th June. Apium Hub. [Online]. [6 October 2017]. Available from: https://apiumhub.com/tech-blog-barcelona/digital-twin-technology/

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Technology of the Week – BI dashboard solutions (Group 25)

29

September

2017

5/5 (1)

Over the years, there have been many changes in the way companies collect and process data. Moving from large boxes filled with paper files and indices, digital data storage finally emerged in the 1960’s. Punch cards were replaced by mechanical hard disks, and both storage and processing capabilities increased exponentially. A decade later, relational database management systems began to emerge, enabling relationships between different databases and tools that allow for sophisticated pattern recognition. Nowadays, companies increasingly invest in sophisticated business intelligence systems, as data is key to gain better customer insights.

But what do we know about business intelligence systems? We asked Erasmus University students to find out the general opinion. It appeared that most students have either no clue or think it is highly complicated.

So allow us to elaborate: Embedded BI, or embedded analytics, allows companies to add BI software features and integrate them with existing applications. Many software providers now specialise in offering these dashboards and reporting tools to facilitate decision making and evidence-based management. The interactive display of data and key performance metrics allow for real-time reporting and provides valuable insights into business trends. Some of the companies that are using embedded BI successfully are Procter & Gamble, Uber and UPS, but smaller companies are increasingly adopting these solutions as well. Dashboard solutions and similar tools are clear game changers, but why exactly is the technology so disruptive? We asked a BI consultant to find out.

Dr. Beatriz Waltrick, a Business Intelligence consultant at Quintus consultants b.v, explained how data warehousing and BI facilitates collecting data and comparing results over time. For example, she explains how the purchase of yoghurt at the supermarket is being registered and shared throughout the whole supply chain, all the way back to the farmer. Furthermore, she mentioned that BI disrupted the industry by enabling supply to follow real-time customer demand, thereby increasing efficiency and reducing waste, which prevents occurrence of the bullwhip effect. Moreover, Dr. Waltrick predicts that BI can further revolutionise information processing and generate new markets, since companies can sell products internationally according to data on uncovered demand. She expects that its impact will break through the whole production chain from customer to supplier, by combining different information systems into one single dashboard.

Finally, we expect Artificial Intelligence to play a role in the future of BI dashboards. AI allows systems to recognise patterns in a smart and revolutionary way, thereby increasing accuracy in predictions and efficiency in finding relevant insights for businesses. Furthermore, as companies have already been investing in BI systems, we expect network effects to result in an oligopoly industry of information dashboards. However, this increasing integration raises ethical and security-related concerns. Current legislation is not up-to-par with the fast development of BI solutions, thereby posing a threat to privacy. A thorough assessment is necessary to ensure that the right people see the right data, and will continue to do so in the future.

References:

Birst. (2017). Data Driven Decisions: Benefits of Analytics Products. [online] Available from: https://www.birst.com/blog/making-decisions-with-data-the-benefits-of-creating-analytic-products/ [Accessed: 25 September 2017].

Computer History Archives Project. (2015). Computer Punch Cards – Historical Overview. [Online Video]. 4 October 2015. Available from: https://www.youtube.com/watch?v=YXE6HjN8heg&t=71s. [Accessed: 24 September 2017].

Does the future lie with embedded BI?. (2017). [Blog] Sisense. Available from: https://www.sisense.com/blog/future-lie-embedded-bi/ [Accessed: 25 September 2017].

Google. (2013). Google and NASA’s Quantum Artificial Intelligence Lab. [Online Video]. 11 October 2013. Available from: https://www.youtube.com/watch?v=CMdHDHEuOUE. [Accessed: 24 September 2017].

PwC. (2014). Data & Analytics helps executives make business-defining decisions better and faster. [Online Video]. 10 September 2014. Available from: https://www.youtube.com/watch?v=6naW6Kg23q4. [Accessed: 25 September 2017].

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