Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers “learn” without explicitly being programmed. Through machine learning, computers can develop the ability to learn through experience and search through data sets to detect patterns and trends. Instead of extracting that information for human comprehension and application, it will use it to adjust its own program actions.

What does that mean for your business? Machine learning can be used across industries, including but not limited to healthcare, automotive, financial services, cloud service providers, and more. With machine learning, professionals and businesses in these industries can get improved performance in a number of areas, including:

  • Image classification and detection
  • Fraud detection
  • Facial detection/recognition
  • Image recognition/tagging
  • Big data pattern detection
  • Network intrusion detection
  • Targeted ads
  • Gaming
  • Check processing
  • Computer server monitoring

In their raw data, large and small data sets hide numerous patterns and insights. Machine learning gives businesses, organizations, and institutions the ability to discover trends and patterns faster than ever before. Practical applications include:

  • Genome mapping
  • Enhanced automobile safety
  • Oil reserves exploration

Intel has worked relentlessly to develop libraries and reference architectures that not only enable machine learning but allow it to truly take flight and give businesses and organizations the competitive edge they need to succeed.
In fact, according to a recent study by Bain [1], companies that use machine learning and analytics are:

  • Twice as likely to make data-driven decisions.
  • Five times as likely to make decisions faster than competitors.
  • Three times as likely to have faster execution on those decisions.
  • Twice as likely to have top-quartile financial results.

Machine learning is giving businesses competitive advantages.

In other words, predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today’s market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed.

Background on Predictive Analytics and Machine Learning

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB [2]. With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics.

As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses. Now that machine learning is entering the mainstream, the next step along the path is predictive analytics, which goes above and beyond previous analytics capabilities.

The Path to Predictive Analytics

With predictive analytics, companies can see more than just “what happened” or “what will happen in the future.”

Machine learning is a part of predictive analytics, and it is made up of deep learning and statistical/other machine learning. For deep learning, algorithms are applied that allow for multiple layers of learning more and more complex representations of data. For statistical/other machine learning, statistical algorithms and algorithms based on other techniques are applied to help machines estimate functions from learned examples.

Essentially, machine learning allows computers to train by building a mathematical model based on one or more data sets. Then those computers are scored when they may make predictions based on the available data. So when should you apply machine learning?

There are a number of times when applying machine learning can give you a competitive advantage. Some prominent examples include:

  • When there is no available human expertise on a subject. Recent navigation to Pluto relied on machine learning, as there was no human expertise on this course.
  • When humans cannot explain their abilities or expertise. How do you recognize someone’s voice? Speech recognition is a deep-seated skill, but there are so many factors in play that you cannot say why or how you recognize someone’s voice.
  • When solutions change over time. Early in a rush-hour commute,the drive is clear. An hour later, there’s a wreck, the freeway comes to a standstill, and side streets become more congested as well. The best route to getting to work on time changes by the minute.
  • When solutions vary from one case to another. Every medical case is different. Patients have allergies to medications, multiple symptoms, family histories of certain diseases, etc. Solutions must be found on an individual basis.

These are just a few of the uses that you’ll find across industries and institutions for machine learning. Not only is the demand for machine learning growing, though, but there is now an evolving ecosystem of software dedicated to furthering machine learning and giving businesses and organizations the benefits of instantaneous, predictive analytics.

An evolving ecosystem of machine learning software.*

In this ecosystem, Intel is the most widely deployed platform for the purposes of machine learning. Intel® Xeon® and Intel® Xeon Phi™ CPUs provide the most competitive and cost efficient performance for most machine learning frameworks.

Challenges to Adoption of Machine Learning

There are a few barriers to adoption of machine learning that businesses need to overcome to take advantage of predictive analytics. These include:

  • Understanding how much data is necessary
  • Adapting and using current data sets
  • Hiring data scientists to create the best machine learning strategy for your business
  • Understanding potential needs for new infrastructure vs. using your existing infrastructure.

With the right machine learning strategy, the barriers to adoption are actually fairly low. And, when you consider the reduced TCO and increased efficiency throughout your business, you can see how the transition can pay for itself in very little time. As well, Intel is dedicated to establishing a developer and data science community to exchange thought leadership ideas across disciplines of advanced analytics. Through these articles and information exchanges, we hope to further help businesses and organizations understand the power of predictive analytics and machine learning”.

What is your opinion and how do you apply data analytics and machine learning? Let us know what you think.

About the Authors:

Nidhi Chappell is the director of machine learning strategy at Intel Corporation. Connect with Nidhi on LinkedIn and Twitter to find out more about how machine learning can give your business a competitive edge.

 

Ronald van Loon is director at Adversitement. If you would like to read more from Ronald van Loon on the possibilities of Big Data please click ‘Follow’ and connect on LinkedIn and Twitter.

 

 

Intel, the Intel logo, Intel Xeon, and Intel Xeon Phi are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation.

Sources:
[1] http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
[2] http://www.mkomo.com/cost-per-gigabyte-update

Source: Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

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Are CEO’s Missing out on Big Data’s Big Picture?

Big data allows marketing and production strategists to see where their efforts are succeeding and where they need some work. With big data analytics, every move you make for your company can be backed by data and analytics. While every business venture involves some level of risk, with big data, that risk gets infinitesimally small, thanks to information and insights on market trends, customer behaviour, and more.

Unfortunately, however, many CEOs seem to think that big data is available to all of their employees as soon as it’s available to them. In one survey, nearly half of all CEOs polled thought that this information was disseminated quickly and that all of their employees had the information they needed to do their jobs. In the same survey, just a little over a quarter of employees responded in agreement.

Great Leadership Drives Big Data

In entirely too many cases, CEOs look at big data as something that spreads in real-time and that will just magically get to everyone who needs it in their companies. That’s not the case, though. Not all employees have access to the same data collection and analytics tools, and without the right data analysis and data science, all of that data does little to help anyone anyway.

In the same study that we mentioned above, of businesses with high-performing data-driven marketing strategies, 63% had initiatives launched by their own corporate leaders. Plus, over 40% of those companies also had centralized departments for data and analytics. The corporate leadership in these businesses understood that simply introducing a new tool to their companies’ marketing teams wouldn’t do much for them. They also needed to implement the leadership and structure necessary to make those tools effective.

Great leaders see big data for what it is – a tool. If they do not already have a digital strategy – including digital marketing and production teams, as well as a full team for data collection, analytics, data science, and information distribution – then they make the moves to put the right people in the right places with the best tools for the job.

Vision, Data-Driven Strategy, and Leadership Must Fit Together

CEOs should see vision, data-driven strategy, and leadership as a three-legged chair. Without any one of the legs, the chair falls down. Thus, to succeed a company needs a strong corporate vision. The corporate leadership must have this vision in mind at all times when making changes to strategy, implementing new tools and technology, and approaching big data analytics.

At the same time, marketing and production strategies must be data-driven, and that means that the employees who create and apply these strategies must have full access to all of the findings of the data collection and analysis team. They must be able to make their strategic decisions based directly on collected data on the market, customer behaviour, and other factors.

To do all this, leadership has to be in place to organize all of strategic initiatives and to ensure that all employees have everything they need to do their jobs and move new strategies forward.

Have you implemented a digital strategy for your business? What’s changed since you’ve embraced your strategy, and what are your recommendations for strategy and data-driven technology for business owners and executives like yourself?

Let us know what you think and how you’ve used your digital strategy to set your business apart from the the competition.

To learn more about the world of Entrepreneurship & Data Science follow Bob Nieme on Twitter or connect with him on Linkedin

CEO at O2MC I/O Prescriptive Computing

 

Connect with author Ronald van Loon to learn more about the possibilities of Big Data

Co-author, Director at Adversitement

 

 


Source: Are CEO’s Missing out on Big Data’s Big Picture?

How You Can Improve Customer Experience With Fast Data Analytics

In today’s constantly connected world, customers expect more than ever before from the companies they do business with. With the emergence of big data, businesses have been able to better meet and exceed customer expectations thanks to analytics and data science. However, the role of data in your business’ success doesn’t end with big data – now you can take your data mining and analytics to the next level to improve customer service and your business’ overall customer experience faster than you ever thought possible.

Fast data is basically the next step for analysis and application of large data sets (big data). With fast data, big data analytics can be applied to smaller data sets in real time to solve a number of problems for businesses across multiple industries. The goal of fast data analytics services is to mine raw data in real time and provide actionable information that businesses can use to improve their customer experience.

Fast data analytics allows you to
turn raw data into actionable insights instantly

Connect with Albert Mavashev
Co-author, CTO & Evangelist at jKool

Analyze Streaming Data with Ease

The Internet of Things (IoT) is growing at an incredible rate. People are using their phones and tablets to connect to their home thermostats, security systems, fitness trackers, and numerous other things to make their lives easier and more streamlined. Thanks to all of these connected devices, there is more raw data available to organizations about their customers, products, and their overall performance than ever before; and that data is constantly streaming.

With big data, you could expect to take advantage of at least some of that machine data, but there was still an expected lag in analysis and visualization to give you useable information from the raw data. Basically, fast data analytics allows you to turn raw data into actionable insights instantly.

With fast data analytics services, businesses in the finance, energy, retail, government, technology, and managed services sectors may create a more streamlined process for marketing strategies, customer service implementation, and much more. If your business has an application or sells a product that connects to mobile devices through an application, you can see almost immediate improvements in how your customers see you and interact with your business, all thanks to fast data analytics.

Consider a few real-world examples of how fast data analytics have helped companies across business sectors improve their performance.

A Financial Firm Monitors Flow of Business Transactions in Real-Time

The world of finance has always been fast-paced, and today a financial firm can have many millions of transactions each day. There’s no way to spare the time or effort to constantly search for breaks and/or delays in these transactions at every hour of the business day. However, with fast data analytics, they found that they could consistently monitor the flow of business throughout the day, including monitoring of specific flow segments, as well as complete transactions.

With the right fast data analytics service, the firm was able to come to a proactive solution in which they could monitor the production environment using their monitoring software’s automated algorithms to keep a constant eye on transaction times. The software’s algorithms determined whether transaction flows were within acceptable parameters or if something abnormal had occurred, giving the firm the ability to respond immediately to any problems or abnormalities to improve their customer experience and satisfaction.

Monitored transaction flows using jKool data analytics solution

A Large Insurance Firm Ensures Faster Claim Processing

In another case, a large health insurance provider with over three million clients was in the process of a massive expansion. As the firm expanded, though, they noticed a disturbing trend. Over the span of a single month, the average processing time for claim payments had increased by a dramatic 10%, but only for a single type of transaction. While they had the tools necessary to analyze the operating system problems, the servers’ hardware, application servers, and other areas where the problem could be originating, they were dealing with monitoring tools that were half-a-decade old.

Thanks to these outdated monitoring tools, the insurance provider had a very expensive problem on their hands, as finding the solution was taking up over 90% of their tier-three personnel’s time and energy. Not only that, but customers were actually finding the majority of their application problems before the provider’s IT support could detect them.

To immediately diagnose the problem and get ahead of it, the insurance firm deployed a fast data monitoring service that immediately diagnosed what was causing the delays in claims processing transactions. The solution was found promptly – one claim type was sitting in a queue long enough that it would time out – and that the addition of new branch locations was causing over-saturation of their architecture design. By reconfiguring their middleware, they were able to accommodate the load increase and solve the problem without taking up valuable employee time and resources.

Just a few of the benefits of deploying this service were:

⦁ A 40% decrease in the mean-time-to-repair for the software problem.
⦁ A 60% decrease in the time spent by third-tier personnel to solve the problem.
⦁ A 35% decrease in the number of open tickets at help desk.
⦁ More than 30% improvement in the average processing time for claims.

A Securities Firm Ensures Dodd-Frank Compliance

Enacted in 2010, the Dodd-Frank Wall Street Reform and Consumer Protection Act is a US federal law that was enacted to regulate the financial industry and prevent serious financial crises from occurring in the future. Securities firms and other financial institutions must ensure that they are Dodd-Frank compliant in order to stay in business and avoid the risk of serious litigation. One such securities firm implemented fast data monitoring and analysis for Dodd-Frank compliance for all of their SWAP trades.

To be Dodd-Frank compliant, firms must report all SWAP trades “as soon as technologically possible”. Within a few minutes of the execution of a trade, a real-time message and a confirmation message must be reported, as well as primary economic terms (PET). If a message is rejected for any reason, it must be resubmitted and received within minutes of execution.

Without monitoring of their reporting systems, the securities firm found that they were in danger of being found non-compliant should anything go wrong within their internal processes. Fast data analytics solution gave them the real-time monitoring they needed to stay compliant.

How Can Fast Data Analytics Help Your Business?

As you can see from these examples, fast data analytics makes it possible for businesses to quickly turn raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. With the ability to identify and solve these issues faster and more efficiently, fast data analytics services can significantly improve any business’ customer experience.

These processes can all be monitored in real-time, giving you access useful analytics and insights for time-sensitive activities. Fast data analytics can help you stay compliant with government and/or industry regulations, avoid preventable losses and it improve your personnel’s efficiency by pinpointing errors and problems without taking up a lot of employees’ time and energy.

How do you want to use fast data analytics to improve your customer experience? Let us know your experiences!

Connect with the authors


Connect with co-author Albert Mavashev to learn more about the world of fast data and all that it can do for you
Co-author, CTO & Evangelist at jKool | Follow on Twitter


Connect with author Ronald van Loon to learn more about the possibilities of Big Data
Co-author, Director at Adversitement

 


Source: How You Can Improve Customer Experience With Fast Data Analytics | Ronald van Loon | LinkedIn

How Strategy – Not Technology – Is the Real Driver for Digital Transformation

Business owners and executives today know the power of social media, mobile technology, cloud computing, and analytics. If you pay attention, however, you will notice that truly mature and successful digital businesses do not jump at every new technological tool or platform.

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Ronald van Loon, Director at Adversitement

While they do not sit and wait for months or years to create social media pages or to take advantage of new analytical services, they do approach every piece of technology that they use with a solid strategy. Why? Marketing, production, and brand management require concrete planning to be effective and coherent. Implementing new technology without a set strategy is a recipe for failure – or, at the very least, for ineffective use of an otherwise powerful tool.

The Importance of Digital Strategy and Vision

To make the most use out of the technologies and tools available to your business today, you must have a coherent and cohesive digital strategy. Companies that have good digital strategies are said to be “digitally mature” and are more likely to embrace the most strategic technologies as they are developed, rather than casting about, trying everything, and failing to use most of it to their advantage.

A good digital strategy is born out of a vision for the company. Savvy leaders will understand that they must first envision the form they want their business to take, the presence they want it to have online and in the physical world, and the brand tone and voice they will use to engage with customers across all media. This is the basis for a strong strategy that will carry you through software and hardware updates, new tools, social media platforms, and much more.

Technology Gives You Analytics – Strategy Shows You How to Use Them

Now, we are not saying that technology is unimportant. In fact, without data streams and analytics, you would have a much more difficult time collecting the information you need on your customers, website traffic, and the market in general. Without analytical tools like these, you would have a much harder time finding the data to make your next strategic move.

How Strategy – Not Technology – Is the Real Driver for Digital Transformation

However, you might think of your analytics and data streams as the tools to fix your car and your strategy as your mechanic’s knowledge and experience. You could have all of the tools necessary to change the struts on your wheels, replace the alternator, or do anything else to repair your car, but those tools will do nothing for you if you don’t have the knowledge and experience necessary to perform those jobs.

With a solid strategy, you’ll have a guide for how to use the tools that technology gives you. You’ll see how your business can embrace these tools and platforms, how it will change and evolve, and how to continue to use them in the future as they become a part of your business. Without strategy, you might get lucky and choose the right platform, the right analytics tools, and the right interpretations of the data in front of you…but it’s highly unlikely.

Businesses that put strategy before technology and then use that strategy to embrace and fully utilize that technology show a digital maturity that will drive them into the future and help them to maintain sustainable growth and success.

Have you implemented a digital strategy for your business? What’s changed since you’ve embraced your strategy, and what are your recommendations for strategy and data-driven technology for business owners and executives like yourself?

Let us know what you think and how you’ve used your digital strategy to set your business apart from the competition.

Read more


Source: How Strategy – Not Technology – Is the Real Driver for Digital Transformation | Ronald van Loon | LinkedIn

IoT – How the Internet of Things Is Driving a Knowledge Revolution

IoT

Think about a few things in your life right now. It really doesn’t matter what they are, as long as you interact with them daily. They could be your phone, your shoes, your watch, your car, your refrigerator, your garage door opener… you get the idea. What do all of these things have in common (besides you, of course)? Well, at the moment, they may not have much of anything in common, but within the next decade, you can expect every last one of them to have Wi-Fi connections to the Internet.

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Ronald van Loon, Director at Adversitement

The Internet of Things is an interesting concept because, on one level, it’s still largely theoretical, but on another it’s already a network that you use every single day. The strict definition of the Internet of Things (IoT) right now is, per the Oxford English Dictionary, “A proposed development of the Internet in which everyday objects have network connectivity, allowing them to send and receive data.”

As much as it’s still a “proposed” development, though, we’re seeing a lot more than proposals in the IoT. As just one example, are you one of the millions of people around the world using fitness trackers to check their daily activity and caloric output? Fit bits, heart rate monitors, and other activity and fitness tracking devices were arguably some of the first “things” in the Internet of Things to come into widespread use.

Just a few years ago, if you wanted to shed a few pounds, you might go on a diet or start running a few miles a week. Now you can download an app to track your intake and track calories out with your fitness tracking device. And, because everything is connected, you can get all of the data you need in one place. Instead of counting calories and guessing at how much you need to run, swim, bike, or lift, you have all of the information you need in your pocket at any time, and your activities get logged automatically.

“How the Internet of Things Is Driving a Knowledge Revolution”


Data, Information, and Knowledge

So how is your Fitbit going to drive a knowledge revolution? Well, when you connect your fitness tracker to your diet app, the two can work together to automatically tell you how much and what you need to eat to stay on track with your goals. They do this by recording data and parsing it into information that you or I can understand. Then, when that information is put in the context of a fitness plan, you have a lot more knowledge about your current fitness level, your goals, and your progress than you had before, all without doing any research on your own.

Now, imagine taking this example to a whole new level. As more and more of our devices and “things” are connected, they share more and more data, translate it into information that we can understand, and deliver it to us in contexts that create more knowledge than we’ve ever had access to before. With knowledge about our fitness and diet needs, the energy efficiency of our homes and cars, and much, much more, we will not be constrained by our natural memories or the time it takes to research these topics.

Instead, we can use the knowledge that’s being pushed to our fingertips on our phones, laptops, tablets, and smart watches to work faster, exercise more effectively, and enjoy more time with our friends and family.

So what do you think of the IoT and the coming knowledge revolution? Has it already affected you in some ways?

Let us know about your current experiences and predictions for the future of knowledge and the Internet of Things.

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Source: IoT – How the Internet of Things Is Driving a Knowledge Revolution | Ronald van Loon | LinkedIn