Tag Archive for Machine Learning

A Digital Transformation – From the Printing Press to Modern Data Reporting

Imagine producing, marketing and selling a product that has only a four-hour shelf life! After four hours, your product is no longer of much value or relevance to your primary consumer. After eight hours, you would be lucky to sell any of the day’s remaining stock. Within 24 hours, nobody is going to buy it; you have to start fresh the next morning. There is such a product line being produced, sold and consumed to millions of people around the world every day. And it’s probably more common than you think.

It’s the daily newspaper.

With such a tight production schedule, news printers have always been under the gun to be able to take the latest news stories and turn them into a finished printed product quickly. Mechanization and automation have pretty much made the production of the modern daily paper a non-event, but it has not always been that way.

150 years ago, the typesetter (someone who set your words, or ‘type’, into a printing press) was the key to getting your printed paper mass produced. With typesetters working faster than your competitors, you could get your product, your story, out to your consumers faster, gaining market share. However, it was still very much a manual process. In the late 1800s the stage was set for a faster method of setting type. One such machine, the Paige Compositor, was as big as a mini-van and had about 15,000 moving parts. (Samuel Clemens, a.k.a. Mark Twain, invested hundreds of thousands of dollars in the failed invention, leading to his financial ruin.) On a more personal scale and at the modern end of the spectrum, we think nothing of sending our finished work, perhaps the big annual report, off to the color printer or ‘office machine’, or upload it to a local printing vendor who will print, collate and bind the whole job for us in a fraction of the time it would take a typesetter to layout even the first page!

So why am I telling you all this? It’s certainly not for a history lesson. The point is that the printed news industry went through a transformation from nothing (monks with quill pens), to ‘mechanization’ (Gutenberg’s printing press), to ‘automation and finally to ‘digitalization.’ And, they had to do so as the news consumer evolved from wanting their printed subscription on a monthly basis, down to the weekly, to the daily and even to the ‘morning’ and ‘evening’ editions. Remember, after four hours, the product is going stale and just about useless. (We could debate whether the faster technologies was what drove news consumers to want information faster, or if the needs of the consumer inspired the advancements in technology, but we won’t.)

Data and reporting has followed the same phases of transformation, albeit not along a much accelerated time span. The modern data consumer is no longer satisfied with having to request a green-bar, tractor fed report from the mainframe, then wait overnight for the ‘job’ to get scheduled and run. They’re not even satisfied with receiving a morning email report with yesterday’s data, or even being able to get the latest analytics report from the server farm on demand. No, they want it now, they want it in hand (smart phones), and they want it concise and relevant. To satisfy this market, products are popping up that fill this need in today’s data reporting market. Products like Microsoft’s Power BI can deliver data quickly and efficiently and in the mobile format demanded due to the industry’s transformation to digital processing. Technologies in Microsoft’s Azure cloud services such as Stream Analytics, coupled with Big Data processing, Machine Learning and Event Hubs have the capabilities to push data in real time to Power BI. I’ll never forget the feeling of elation I had upon completing a simple real-time Azure solution that streamed data every few seconds from a portable temperature sensor in my hand to a Power BI Dashboard. It must have been something like Johannes Gutenberg felt after that first page rolled off his printing press.

Gutenberg and Clemens would be amazed at the printing technology available today to the everyday consumer, yet we seem to take it for granted. Having gone through some of the transformation phases with regard to information delivery myself (yes, I do in fact recall 11×17 green-bar tractor-fed reports) I tend to be amazed at what technologies are being developed these days. Eighteen months ago (an eon in technology life) the Apple watch and Power BI teamed up to deliver KPI’s right on the watch! What will we have in another eighteen months? I can’t wait to find out.

About Todd: Todd Chittenden started his programming and reporting career with industrial maintenance applications in the late 1990’s. When SQL Server 2005 was introduced, he quickly became certified in Microsoft’s latest RDBMS technology and has added certifications over the years. He currently holds an MCSE in Business Intelligence. He has applied his knowledge of relational databases, data warehouses, business intelligence and analytics to a variety of projects for BlumShapiro since 2011. 

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Internet of Things Modern Application Development

Over the past decade modern application development has shifted from mainframe computing to personal PCs, and now to smartphones and cloud services. These shifts required new software languages, new hardware and new application development solutions. The best illustration of this shift came with the 90’s “Internet Boom.”  This shift resulted in application development on mainframe and personal PCs to applications that can run on a web browser. The shift also included new tools such as Visual Studio, new languages like HTML/JavaScript, new Architecture Patterns such as MVC and new application life cycle processes like Agile/Scrum.

Then came the smart phone. This shift from personal PCs to small mobile devices such as iPhones forced modern application development to support multiple screen resolutions, and a need to operate off-line while remaining connected to cloud services. Our next shift is to the Internet of Things (IoT), once again giving a new meaning to modern application development. Now, applications need to be developed to run on different types of devices like thermostats, doorbells and small Bluetooth sensors. The application must be secure, cloud ready and able to perform predictive analysis using machine learning. Below are my thoughts on this latest shift in modern application development:

Devices

The IoT modern application development shift includes a multitude of devices that range from televisions to cameras, to refrigerators, to pretty much any device that is powered into an outlet. One of the more notable products in this space is the Amazon Echo which uses voice recognition as its main interface, and can provide control over your light switches, thermostats and even your music collection. Amazon Echo is an example of an IoT device which breaks away from the previous modern application development, as it uses voice as its interface, is always connected to the cloud, and can connect with other IoT devices. This changes everything about how we think of modern application development. No longer is it about supporting multiple device resolutions, but rather about what data can be captured via the latest IoT devices and how that data can be used to improve our lives. This means we need new software tools, new cloud services, new analysis software and new machine learning algorithms.

These applications do not always include fancy user interfaces, as they are often function specific. For example, an IoT device could capture changes in temperature on a farm, take soil sample readings or even capture images and video of the fields. This data can then be sent to cloud services where it can be analyzed and run through machine learning to produce an easy to understand update on the farm. The data from the disparate “things” needs to be collected in a common format for actionable insights. Of note, most of the “big data” being processed and collected today is machine-to-machine. Cloud services help to aggregate and display this data in ways humans can understand, analyze and take action on the insights delivered.

Cloud Services

Cloud services are at the heart of IoT. Devices are built to perform a simple purpose and leave all complex user interfacing, analysis and thinking to the cloud. Cloud services such as the Azure IoT hub provide both the software tooling and service for a device to talk to the cloud and the device to connect to other devices. For example, in the manufacturing industry, IoT devices using the Azure IoT hub can be developed to monitor the production line and equipment use, which is then submitted to cloud service which then can be interpreted by human intelligence to predict equipment maintenance.

With this shift to IoT modern application development software is developed to capture data from a range of sensors, submit that data to cloud services and then process that data using analytics services such as Business Intelligence dashboards for timely and relevant role based information.

Machine Learning

So what is the point of these IoT devices in our homes, cars and at work, capturing data and sending it to the cloud? Well that’s what machine learning is all about. We now need to develop algorithms that can learn based on data from the IoT. For example: home IoT devices using machine learning will learn the normal patterns in your house and only notify you when there is a disruption such as the lights staying on past a normal pattern or when you leave your windows open while you are away. Machine learning is one of the most important aspects of IoT and without it, all we would have is raw data in a cloud service with no meaningful way to utilize it.

BlumShapiro Consulting is a Microsoft Advanced Analytics partner, with experience building modern IoT apps. 

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About Hector: 

hectorHector Luciano, Jr. is a Consulting Manager at BlumShapiro, a Microsoft Gold Partner focusing on SharePoint, Office 365, mobile technologies and custom development solutions. Hector is an active member of the SharePoint community. His experience reflects a breadth of Microsoft .Net Technologies experience. With a focus on Software Application development, Hector has worked on various projects including architected and designed solutions for web, client/server and mobile platforms. He has worked closely with business owners to understand the business process, then design and build custom solution. Hector currently holds Microsoft Certified Solution Developer (MCSD), Microsoft Certified Professional Developer (MCPD).

 

 

Applied Machine Learning: Optimizing Patient Care in Hospitals, Profitably

Why are so many industries exploring Machine Learning as a means of delivering innovation and value?  In my view, the technology speaks to a primitive urge – machine learning is like having a crystal ball, telling you what will happen next.  For a business, it can convey information about a customer before they introduce themselves.  On a personal level, when I consider what I would like to have information about in advance, the first thing that springs to mind is obvious: my health.  Am I about to get sick?  How can I improve my wellness and overall health?  If you are wearing a Fit-Bit right now, then you probably agree with me.

In my last blog, I shared some real-world examples for how the Hospitality Industry applies Machine Learning.  What about Health Care and Hospitals?  While hospitals have similar challenges, in that they accommodate guests who stay overnight, the objectives in health care are quite different, and changing rapidly.   The Affordable Care Act is driving new business models, incentivizing outcome based reimbursement as opposed to volume based reimbursement.  Unlike hotels, today’s hospitals are interested in ensuring their guests do not have to return, at least not in the short term.   They also need to manage costs in a way they have not been incentivized to do in the past.  Hospitals across the country are considering how predictive analytics can have a  meaningful impact on operations, leading to improved patient health and improving the bottom line.

The cost savings opportunity for health care providers is startling. Here are just three examples:

Reducing Hospital Readmissions – in 2014, Medicare fined 2,610 hospitals $428 million for having high hospital readmission rates. Leaving actual fines aside, industry analysts estimate that the overall cost of preventable readmissions approaches $25 Billion annually. As a result, hospital systems all over the nation are mobilizing to intervene, using ML to identify risk factors which are highly predictive of readmission. Carolinas Healthcare System, partnering with Microsoft, did just that. Using data from 200,000 patient-discharge records, they created a predictive model deliver customized discharge planning, saving the hospital system hundreds of thousands of dollars annually. Read the article in Healthcare IT News.

Clinical Variation Management – Mercy Hospital is partnering with Ayasdi to find the optimal care path for common surgical procedures. Using knee replacement as an example, the Clinical Variation Management software helps hospital administrators find clusters of patient outcomes, then enables the exploration of those clusters in order to correlate a metric (i.e. Length of Stay) with a certain regiment or activity. Watch this video to learn how Mercy Hospital saved $50 million by applying Machine Learning to an extremely common procedure.

Improving Population Health – Dartmouth Hitchcock, a healthcare network affiliated with Dartmouth University, is piloting a remote monitoring system for patients requiring chronic care. 6,000+ patients are permitting the hospital to collect biometric data (i.e. blood pressure, temperature, etc.) in order that nurses and health coaches can monitor their vital signs, and machines can predict good days and bad days. Quite the opposite of hospitality: Dartmouth Hitchcock is trying to keep the guests from needing to checking in! Read more about the Case Study from Microsoft.

Are machines taking over for physicians? No. The Patient – Physician relationship remains (and I think will always remain) central to the delivery of personal health care. However, it seems clear that ACA is providing significant rewards to health care providers who manage population risk better. Machines can help here: through data, machine learning can find risk “hiding in the data”.

Contact Blum Shapiro Consulting to learn more about how Azure Machine Learning can curtail hospital readmissions, identify variations in common clinical procedures and improve patient population health.

Applied Machine Learning – Hospitality and Hotel Management

Machine Learning, or Predictive Analytics, is in common use in the travel and hospitality industry.  Online travel sites such as Expedia, Priceline and Trip Advisor have been putting data to use for over a decade, and they are very good at selling us stuff with Machine Learning.  They start by figuring out what someone will pay (relative to other customers) for a travel service, classifying preferences for all of the travel services they have available to sell, and recommending the next thing a website visitor need.  Flying to Vegas you say?  It’s very likely a hotel or car rental is in your future.  But which hotel offers will keep you on their site, buying from Expedia and not Expedia’s competitors?  Do you expect a premium hotel experience?  Do you need a Fiat or a GMC Acadia?  Are you a fan of Blue Man Group, Celine Dion or Penn and Teller?

And they usually get it right! They are using what is called a Recommendation Engine in order to guide customers through a series of buying decisions, based upon patterns and preferences which are similar to other customers.  Underneath, it’s just math, but the impact is undeniable.  It enables companies to predict what their customers want, and then (this is the critical piece) anticipate and deliver a response to that want, at a time when a customer truly needs it and is willing to pay for it.  Right thing, right place, right time.

Recommender is one of four core Machine Learning problems.  These four problems represent what ML can solve.  Now let’s apply Machine Learning to another business model within the Hospitality vertical: Hotel Management.  Is machine learning relevant to hotels?   If an algorithm can anticipate a guest’s need, what can hotel staff do with that information? The answer is: create a customer for life.

One common misconception about Machine Learning is that, in order to produce any useful insights, you need tons and tons of data (or, Big Data).  Property Management Systems (PMS) used by hotels and hotel groups are proving this wrong: they are using ML on a small scale to manage revenues, improve profitability and delight their customers.  Here are three examples of how hotel and property management groups are using Machine Learning to manage revenue today.

Market Segmentation – Customer segmentation is not a new thing.    We understand the difference between family vacationers and business travelers – those are big segments. But Machine Learning goes further, helping companies discover segments they may not realize existed. Which customers want to be near the pool, and which ones need three morning papers before they can even get dressed in the morning? Armed with this knowledge, hotels understand what matters to guests, at the individual level, enabling them to anticipate their needs immediately. Even more, hotels can understand key characteristics of their most profitable customers, and recognize the next one when they login to the online reservation site.

Demand Forecasting – what kinds of services will be in demand throughout the year? Can a hotel optimize room offerings to suit changing demand patterns, and perform renovations or scheduled maintenance in a way that has no net impact on profitability? While hotels may have limited ability to grow or shrink their inventory of rooms, management can optimize resource needs in order to respond to predicted demand levels.

Price Optimization – airlines understand very well that different customers are happy to pay different prices for the same airplane seat.  The same is true for hotel rooms.  Often, profitable customers are ones who are least price sensitive, but some are quite price sensitive – choosing to spend their money on other hotel services offered.

Machine Learning may seem intimidating, especially if you are assuming you’ll need Big Data and massive computing power in order to yield an investment.  Actually, the key ingredient is knowledge of your industry, and what you, as a services provider, can offer customers.  Most hotels and hotel groups have enough core guest information to begin deriving valuable insights and begin competing for customers in the new world of data.

Contact Blum Shapiro Consulting to learn more about how Azure Machine Learning can deliver these kind of predictive capabilities to your business, leading to sustained customer loyalty and profitable business outcomes.