Big Data Analytics Can Benefit Manufacturers

Big Data Analytics Can Benefit ManufacturersA recent Honeywell survey of manufacturing executives indicates that a majority of respondents (67 percent) plan to use data analytics to address issues with equipment.

The survey shows that anaytics are becoming core to what Honeywell refers to as the industrial internet of things (IioT) as a way for companies to save money on equipment maintenance and repair by providing insight into when machines need service.

The survey, conducted by Honeywell Process Solutions (HPS) and KRC Research, polled more than 200 North American manufacturing executives. The study, entied “Data’s Big Impact on Manufacturing: A Study of Executive Opinions,” showed that a majority of respondents said they are already investing in data analytics technology.

In addition, the survey indicated that manufacturing executives view unscheduled downtime and equipment breakdowns as the biggest obstacles to maximizing revenue. Yet, more than a quarter of respondents said they don’t plan to invest in data analytics in the next year. These respondents cited inadequate resources and lack of understanding as key reasons for their lack of investment.

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3 Ways to Solve App Performance Problems with Transaction Tracking

3 Ways to Solve App Performance Problems with Transaction TrackingTransaction Tracking can provide tremendous insight into the performance and behavior of applications. However, this can be challenging when transactions traverse platforms and technologies.  Often it can be similar to tracking someone in the old Westerns where you follow the trail to the river and then lose track of where they went next.  Tracking MQ transactions can have this same hurdle to overcome with MQ running on diverse platforms spanning multiple locations.  MQ transactions typically interact with other platforms such as IBM Integration Bus (Broker) and IBM DataPower.  Visualizing a dynamic flow of transactions across all of these environments is well worth the effort as it greatly simplifies the problem detection process at the same time reducing the mean time to resolve problems (MTTR).

Concepts of Transaction Tracing

Package tracking is an analogy that can be used to explain the concepts behind transaction tracking.  A package is sent from location A to location B with tracking notices generated to let the sender know where the package is in-transit, when the expected time of arrival is and when it actually arrived.    Package Tracking is a combination of disjoint technologies, similar to the middleware environment.  The process of transaction tracking can be complex with cost and timeliness of delivery of major concern.  No matter how fast you deliver a package, someone always wants it quicker. The same problems affect MQ transactions, but instead of a package, it is a message that never seems to get to its destination fast enough.

There are a set of common questions users have about package tracking.  For the customer it might include:   where is my package or is my package progressing as planned? If you work for the shipping company, you are more concerned about: where the bottlenecks are in the system, how to solve a problem, stopping a problem from recurring and what issues can I expect tomorrow. As a technician, you would want to know where your failures are occurring and where you can make improvements.

Package tracking involves: delivering   packages, scanning them and exporting the events from the scanners into a database for later analysis.  The key to tracing anything is to create tracking events that capture the key events such a pick, pack or ship and what time these occurred

Transaction Tracking for MQ

There are a common set of  behavior patterns for MQ.  Each set of patterns can be unique.  Typically, you have senders and receivers as well as queues being processed with one or more queue managers communicating with each other. With multiple applications running on different servers, as in the package tracking example, every time a message gets sent or received, the details about that message and its processing should be captured and sent  to a central location.  This will provide the ability to understand what is occurring. If the transaction is stuck or slow, we know that we can react to it or produce warnings if the transaction takes too long.  We can also gather statistics along the way to see the duration of each step.  Capturing raw metrics about message flows and then correlating them together into a big picture can help the user solve performance problems faster.

Whether you are a corporate manager, Line of Business owner, application support group or IT infrastructure team, you need end-to-end visibility into the transactions that are relevant to you.

Stay tuned for the next installment in this series, “3 Ways to Solve App Performance Problems with Transaction Tracking”.

 

To learn more, watch the TechTalk here

jQuery Templates: Should You Use Them or Not?

jQuery Templates: Should you use themWhen it comes to web design today, you have many options to go with. One of the most unique solutions is using the jQuery templates. With these templates, you will be able to display and manipulate the data in the browser. For example, you can use this template to format and display a set of records from your database that you retrieve using the Ajax call. Continue reading

Big Data And A Shocking Waste Problem

Big Data And A Shocking Waste ProblemIt’s a shocking fact that in the 21st century more than three quarters of a billion people do not have access to enough food to keep themselves healthy, while 30% of the food produced around the world goes to waste.

It’s partly a problem of logistics – it’s expensive to keep food fresh and get it to the people who need it most. This of course means its ripe for tackling with technology and Big Data.

Recently I had the chance to speak to one of the founders of Food Cloud – a social enterprise which harnesses the power of crowd sourcing in a bid to cut down on food wasted by retailers.

Food Cloud made headlines recently thanks to a partnership with Tesco , the UK’s biggest grocery seller. The simple premise is that it matches retailers that have surplus food stocks with charities that have the facilities to deliver it to those who need it.

Of course even a concept this elegantly straightforward requires some fairly advanced technology if it’s going to be rolled out at the sort of scale necessary to bring about social change. And plans are underway to operate the scheme in 1,000 Tesco stores by the end of the year.

Co-founder Iseult Ward told me “Our data driven approach is what differentiates us from a lot of traditional food surplus redistribution organizations.

“We’re able to collect all sorts of data on all of the food which is donated, and the organizations it is donated to, which allows us to do things like provide full sourcing and measure the performance of the stores and charities.”

 

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Advanced Analytics 101: Beyond Business Intelligence

Advanced Analytics 101: Beyond Business IntelligenceTrying to make sense of data is nothing new. Companies have been applying analytical methods since before the computer was invented. Once computers and storage became reasonably cheap and powerful, in the late 1980s, companies started using Business Intelligence (BI) software to try to find meaning in their data. Today’s Advanced Analytics go beyond the capabilities of Business Intelligence.

Business Intelligence is driven by an end user who knows the questions they want to ask and has deliberately collected data to support their inquiry. Advanced Analytics let users find answers when they don’t even know what they should be asking by identifying meaningful patterns. Rather than just looking backwards at the data to understand what has happened, Advanced Analytics use the data to look to the future and understand what is likely to happen. While both Business Intelligence and Advanced Analytics are used to improve decision making, the two techniques have different goals and use different methods.

Three Kinds of Analytics


There are three basic kinds of Analytics, increasing in both complexity and power.

  • Descriptive Analytics: Like Business Intelligence, it looks backward to understand what happened. The techniques used in these Analytics are the least complicated, mostly summarizing data through counts, aggregations of metrics, and simple calculations such as averages. These Analytics may also use data mining to find correlations between variables and help identify reasons for previous success or failure.
  • Predictive Analytics: Uses statistical models and other methods to predict what might happen based on previous events. These Analytics use statistics, data mining, Machine Learning methods, and business rules to make probabilistic predictions of the results of certain actions.
  • Prescriptive Analytics: Gives recommendations on what should be done, including for questions that weren’t specifically asked. These Analytics extend upon Predictive Analytics through the use of optimizations and simulations to evaluate possible actions and the potential impact of each option.

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What every CEO/MD needs to know about Big Data & Data Analytics

What every CEO/MD needs to know about Big Data & Data AnalyticsIn order to remain competitive, and viable, businesses now have to deal with a vast and rapidly growing sea of what has been termed ‘Big Data’.  They need to be able to transform this raw data, often in real-time, into more meaningful insights about their markets, customers, competitors, and to measure and manage their performance more accurately using using techniques such as ‘Data Analytics’. In many cases this represents a paradigm shift from their comfort zone of approaches based more on experience, guesswork, or painstakingly constructed models of reality.

It used to be that Big Data and Data Analytics were the preserve of large global corporations but consider this definition for Big Data: When volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big Data is a relative term. Every organization will rapidly reach a point where the volume, variety and velocity of their data will be something that they have to address.

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Patient experience, personalized medicine, big data analytics garner top digital health investment in record-setting first half of 2016

Patient experience, personalized medicine, big data analytics garner top digital health investment in record-setting first half of 2016Funding for digital health companies reached new heights in the first half of 2016, totaling $3.9 billion invested in 155 deals for seed and Series A rounds, according to a report by StartUp Health.

The five leading investment groupings were patient experience with $958 million, wellness with $854 million, personalized medicine with $524 million, big data analytics with $406 million and workflow with $328 million.

“Personalized health has become an explosive winner while patient experience maintains strong and steady growth,” the report said.

In 2015 early stage investments for the first half of the year amounted to 3.5 billion, reaching $7 billion for the year. In 2015, $2.9 was invested in the first half, and the year-end total was $6 billion.

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How to Boost Customer Loyalty Using Analytics and Actionable Insights

Returns data offers companies priceless insights that improve the customer experience.Returns data offers companies priceless insights that improve the customer experience.

As ecommerce grows, so do return rates: About one in three online purchases is returned. For online apparel purchases, the rate is closer to 40 percent. That has triggered the growth of the returns-management software marketplace, offering retailers solutions aimed at making the processing and dispensation of returned goods faster and more efficient.

That’s important. The more quickly and efficiently a return is processed, the better the outcome: The consumer gets his or her refund faster. The retailer minimizes reverse-logistics costs. And the company offering the product recoups the maximum recovery value.

Transformation of Real User Monitoring Tools in the Industry

Transformation of Real User Monitoring Tools in the IndustryWith online viewership and sales growing rapidly, enterprises are interested in understanding how they analyze performance to positively impact business metrics. Deeper insight into the user experience is needed to understand why conversions are dropping and/or bounce rates are increasing or, preferably, to understand what has been helping these metrics improve.

The digital performance management industry has evolved as application performance management companies have broadened their scope beyond synthetic testing that simulates users loading specific pages at regular intervals to include web and mobile testing, and real user monitoring (RUM).  As synthetic monitoring gained popularity, performance engineers realized the variations that exist from real end users were not being captured. This led to the introduction of RUM – the process of capturing, analyzing and reporting data from a real end user’s interaction with a website. RUM has been around for more than a decade, but the technology is still in its infancy.

What features should you look for in a RUM solution?
Knowing that you need a RUM solution is the first step.   The second step is identifying what features are required to meet your business needs.  With a variety of solutions available in the market, identifying the must-have and the nice-to-have features is important to find the best fit.

Real-time and actionable data
Most RUM tools display insights in the dashboard for the user in near real-time.  This information can be coupled with near real time tracking information from business analytics tools like Google Analytics. Performance data from RUM solutions should be cross-checked against metrics such as site visits, conversions,user location and device/browser insights. Many website operators continuously monitor any changes in the business metrics since they are indicative of problems in performance; further, it enables them to minimize false positives or isolated issues in performance.

 

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