Archive for Internet of Things

Our 5 Rules of Data Science

In manufacturing, the better the raw materials, the better the product. The same goes for data science, where a team cannot be effective unless the raw materials of data science are available to them. In this realm, data is the raw material which produces a prediction. However, raw materials alone are not sufficient. Business people who oversee machine learning teams must demand that best practices be applied, otherwise investments in machine learning will produce dubious business results. These best practices can be summarized into our five rules of data science.

For the purpose of illustration, let’s assume the data science problem our team is working on is related to the predictive maintenance of equipment on a manufacturing floor. Our team is working on helping the firm predict equipment failure, so that operations can replace the equipment before it impacts the manufacturing process.

Our 5 Rules of Data Science

1. Have a Sharp Question

A sharp question is specific and unambiguous. Computers do not appreciate nuance. They are not able to classify events into yes/no buckets if the question is: “Is Component X ready to fail?” Nor does the question need to concern itself with causes. Computers do not ask why – they calculate probability based upon correlation. “Will component X overheat?” is a question posed by a human who believes that heat contributes to equipment failure. A better question is: “Will component X fail in the next 30 minutes?”

2. Measure at the Right Level

Supervised learning requires real examples from which a computer can learn. The data you use to produce a successful machine learning model must demonstrate cases where failure has occurred. It must also demonstrate examples where equipment continues to operate smoothly. We must be able to unambiguously identify events that were failure events, otherwise, we will not be able to train the machine learning model to classify data correctly.

3. Make Sure Your Data is Accurate

Did a failure really occur? If not, the machine learning model will not produce accurate results. Computers are naïve – they believe what we tell them. Data science teams should be more skeptical, particularly when they believe they have made a breakthrough discovery after months of false starts. Data science leaders should avoid getting caught up in the irrational exuberance of a model that appears to provide new insight. Like any scientific endeavor, test your assumptions, beginning with the accuracy and reliability of the observations you started with to create the model.

4. Make Sure Your Data is Connected

The data used to train your model may be anonymized, because factors that correlate closely to machine failure are measurements, not identifiers. However, once the model is ready to be used, the new data must be connected to the real world – otherwise, you will not be able to take action. If you have no central authoritative record of “things”, you may need to develop a master data management solution before your Internet of Things with predictive maintenance machine learning can yield value. Also, your response to a prediction should be connected. Once a prediction of failure has been obtained, management should already know what needs to happen – use insights to take swift action.

5. Make Sure You Have Enough Data

The accuracy of predictions improve with more data. Make sure you have sufficient examples of both positive and negative outcomes, otherwise it will be difficult to be certain that you are truly gaining information from the exercise.

The benefits of predictive maintenance, and other applications of machine learning, are being embraced by businesses everywhere. For some, the process may appear a bit mysterious, but it needn’t be. The goal is to create a model which, when fed real-life data, improves the decision making of the humans involved in the process. To achieve this, data science teams need the right data and the right business problem to solve. Management should work to ensure that these five questions are answered to their satisfaction before investing in data science activities.

Not sure if you have the right raw materials? Talk to BlumShapiro Consulting about your machine learning ambitions. Our technology team is building next generation predictive analytics solutions that connect to the Internet of Things. We are helping our clients along each step of their digital transformation journey.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

Using Real Time Data Analytics and Visualization Tools to Drive Your Business Forward

Business leaders need timely information about the operations and profitability of the businesses they manage to help make informed decisions. But when information delivery is delayed, decision makers lose precious time to adjust and respond to changing market conditions, customer preferences, supplier issues or all three. When thinking about any business analytics solution, a critical question to ask is: how frequently can we (or should we) update the underlying data? Often, the first answer from the business stakeholders is “as frequently as possible.” The concept of “real time analytics,” with data being provided up-to-the minute, is usually quite attractive. But there may be some confusion about what this really means.

While the term real time analytics does refer to data which is frequently changing, it is not the same as simply refreshing data frequently. Traditional analytics packages which take advantage of data marts, data warehouses and data cubes are often collectively referred to as a Decision Support System (DSS). A DSS helps business analysts, management and ownership understand historical trends in their business, perform root cause analysis and enable strategic decisions. Whereas a DSS system aggregates and analyzes sales, costs and other transactions, a real time analytics system ingests and processes events. One can imagine a $25 million business recording 10,000 transactions a day. One can imagine that same business recording events on their website: login, searches, shopping cart adds, shopping card deletes, product image zoom events. If the business is 100% online, how many events would that be? The answer may astonish you.

Why Real Time Analytics?

DSS solutions answer questions such as “What was our net income last month?”, “What was our net income compared to the same month last year?” or “Which customers were most profitable last month?” Real time analytics answers questions such as “Is the customer experience positive right now?” or “How can we optimize this transaction right now?” In the retail industry, listening to social media channels to hear what customers are saying about their experience in your stores, can drive service level adjustments or pricing promotions. When that analysis is real-time, store managers can adjust that day for optimized profitability. Some examples:

  1. Social media sentiment analysis – addressing customer satisfaction concerns
  2. Eliminating business disruption costs with equipment maintenance analytics
  3. Promotion and marketing optimization with web and mobile analytics
  4. Product recommendations throughout the shopping experience, online or “brick and mortar”
  5. Improved health care services with real time patient health metrics from wearable technology

In today’s world, customers expect world class service. Implicit in that expectation is the assumption that companies with whom they do business “know them”, anticipate their needs and respond to them. That’s easy to say, but harder to execute. Companies who must meet that expectation need technology leaders to be aware of three concepts critical to making real time analytics a real thing.

The first is Internet of Things or IoT. The velocity and volume of data generated by mobile devices, social media, factory floor sensors, etc. is the basis for real time analytics. “Internet of Things” refers to devices or sensors which are connected to the internet, providing data about usage or simply their physical environment (where the device is powered on). Like social media and mobile devices, IoT sensors can generate enormous volumes of data very, very quickly – this is the “big data” phenomenon.

The second is Cloud Computing. The massive scale of IoT and big data can only be achieved with cloud scale data storage and cloud scale data processing. Unless your company’s name is Google, Amazon or Microsoft, you probably cannot keep up. So, to achieve real-time analytics, you must embrace cloud computing.

The third is Intelligent Systems. IBM’s “Watson” computer achieved a significant milestone by out-performing humans on Jeopardy. Since then, companies have been integrating artificial intelligence (AI) into large scale systems. AI in this sense is simply a mathematical model which calculates the probability that data represents something a human would recognize: a supplier disruption, a dissatisfied customer about to cancel their order, an equipment breakdown. Using real time data, machine learning models can recognize events which are about to occur. From there, they can automate a response, or raise an alert to the humans involved in the process. Intelligent systems help humans make nimble adjustments to improve the bottom line.

What technologies will my company need to make this happen?

From a technology perspective, a clear understanding of cloud computing is essential. When evaluating a cloud platform, CIO’s should look for breadth of capability and support for multiple frameworks. As a Microsoft Partner, BlumShapiro Consulting works with Microsoft Azure and its Cortana Intelligence platform. This gives our clients cloud scale, low cost and a wide variety of real time and big data processing options.

CIO Article 1

This diagram describes the Azure resources which comprise Cortana Intelligence. The most relevant resources for real time analytics are:

  1. Event Hubs ingest high velocity streaming data being sent by Event Providers (i.e. Sensors and Devices)
  2. Data Lake Store provide low cost cloud storage which no practical limits
  3. Stream Analytics perform in-flight processing of streaming data
  4. Machine Learning, or AzureML, supports the design, evaluation and integration of predictive models into the real-time pipeline
  5. Cognitive Services are out-of-the-box Artificial Intelligence services, addressing a broad range of common machine intelligence scenarios
  6. Power BI supports streaming datasets made visible in a dashboard context

Four Steps to Get Started with Real Time Analytics

Start with the Eye Candy – If you do not have a dashboard tool which supports real-time data streaming, consider solutions such as Power BI. Even if you are not ready to implement an IoT solution, Power BI makes any social media or customer marketing campaigns much more feasible. Power BI can be used to connect databases, data marts, data warehouses and data cubes, and is valuable as a dashboard and visualization tool for existing DSS systems. Without visualization, it will be very difficult to provide human insights and actions for any kind of data, slow or fast.

Get to the Cloud – Cloud storage costs and cloud processing scale are the only mechanisms by which real time analytics is economically feasible (for most companies). Learn how investing in technologies like Cloud Computing can really help move your business forward.

Embrace Machine Intelligence – To make intelligent systems a reality, you will need to understand machine learning technologies, if only at a high level. Historically, this has meant developing a team of data scientists, many of whom have PhD’s in Mathematics or Statistics, and open source tools like R or Python. Today, machine learning is much more accessible then it has ever been. AzureML helps to fast track both the evaluation and operationalization of predictive models.

Find the Real-Time Opportunity – As the technology leader in the organization, CIO’s will need to work closely with other business leaders to understand where real-time information can increase revenue, decrease costs or both. This may require imagination. Start with the question – what would we like to know faster? If we knew our customer was going to do this sooner, how would we respond? If we knew our equipment was going to fail sooner, how would we respond? If we knew there was an opportunity to sell more, how would we respond?

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

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Building Meaningful Relationships Through IoT Driven Customer Engagement

We all know the importance of building meaningful relationships with our customers. Consumers want to know the products they buy are reliable. They want to see value from the services they pay for. They want to know the companies they’re doing business with are trustworthy, helpful and honest. Whether it be through social media, sending out newsletters and surveys, hosting webinars and seminars, or other activities, most (if not all) companies employ some sort of process or platform for engaging with their customers. With the emergence of new technology and the rise of smart devices companies are now positioned to take customer engagement to the next level.

How Can the Internet of Things (IoT) Help Your Customers?

What if you could help solve your customers’ problems before they have them? What if you could make meaningful recommendations and provide targeted guidance to your customers based on how they’re using your products? What if you could always be connected to your customers? You can, with the Internet of Things.

Based on data from the State of the Market: Internet of Things 2016 report from Verizon, IoT is having a large impact on how businesses are engaging their customers. Here are a few key points taken from the report.

  • 72% of organizations feel IoT is critical to their competitive advantage.
  • 76% of early movers in manufacturing say IoT is increasing insight into customer preferences and behavior.
  • 54% of early movers in healthcare are using IoT to enrich products and services with information
  • 81% of early movers in the public sector believe their citizens increasingly expect them to offer enhanced services using data from IoT.
  • 77% of retailers are seeing IoT change the customer experience.
  • 84% of retail early movers say their customers value exchanging information to improve their experience.
  • 83% of early movers in financial services say customer relationships are increasingly driven by ongoing service agreements rather than transactional product sales.

For me, one of the more interesting statistics was that in the public sector 81% of early movers believe citizens increasingly expect them to offer enhanced services using data from IoT. Using smart devices and sensors, data can be processed practically in real-time so that decisions can be made on the fly to enhance services. This information can then be relayed back to cloud servers to provide insight and drive policies to further enhance services while cutting costs. Soon customers will come to expect these types of enhancements, and companies that don’t follow suit may lose customers to competitors that are early movers. With more and more private sector companies across an array of industries leveraging IoT to engage their customers, are consumers going to begin expecting services enhanced by technology from all businesses? I believe many already do and that the trend is on the rise.

About Matt:

As a senior in BlumShapiro’s Technology Consulting Group, Matt has over 7 years of experience with Microsoft .NET software application development, including solutions for web, client/server and mobile platforms.

 

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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).