Why you should attend the Employee Engagement conference! | The HR Tech Weekly®

Why you should attend the Employee Engagement conference!

Marcus Evans: Employee Engagement | The HR Tech Weekly®

In a world where competitiveness is multiplying, the human factor is now the main differentiating factor. The performance of employees cannot be separated from the company’s.

Otherwise, different factors could turn employees into sources of loss if they are not as involved and especially engaged in their work.

According to the Steel Case and Ipsos study on employee engagement:

“Of the 17 countries studied and the 12,480 participants, 1/3 of the employees are disengaged.”

Germany, UK, the Netherlands, Spain, Belgium and France scored below the world average in terms of the rate of employees being engaged and satisfied with their working environment. Employee disengagement is not limited to a particular industry but affects all businesses. Some companies place more emphasis on employee engagement because they successfully established the link between commitment and performance. This is why they have put in place mechanisms to measure the degree of commitment of employees and try to establish programs enabling the optimization of well-being at work, through various actions targeting motivation, the quality of the working environment, managerial leadership and others, in order to build a culture of sustainable engagement.

There are no sectors that are eradicated or less affected by this scourge. As long as companies work in an environment that is changing constantly, there will always be sources and factors optimizing disengagement. As a result, it will always be necessary to increase the level of vigilance in order to limit the risks of disengagement.

Companies are interested in knowing more about:

  • How to improve the employer branding and communicate about the company’s values to the employees
  • How can we put the company’s culture at the service of employee engagement?
  • The role of leadership in managing employee engagement
  • How to create a sense of belonging among the employees?
  • How to use predictive analytics to improve employee engagement?
  • How to maintain employee engagement after a M&A or a strategic transformation?

Consequently, executives involved in HR, Talent Management, Engagement and Retention, Internal Communication and so on should definitely not miss out on this opportunity to attend the marcus evans‘ Employee Engagement conference taking place on the 27th-29th of September in Amsterdam, Netherlands.

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3 Ways HR Will Evolve in the Future

3 Ways HR Will Evolve in the Future

Believe it or not, automation is changing our entire lives, the way we live, think and work. As quoted by Mr. Abhijit Bhaduri, author of Digital Tsunami, “Humans resist change, machines don’t.” We are at a age where we no longer can nor should resist change. That said, technology is also massively impacting the HR functions. Most of the traditional support functions of HR, such as payroll, attendance etc. are being automated. Adding to the progression, chatbots are further driving more engagement with its personalized attributes, and is further adding up to redefining the HR role.

Ripples in the Water

One might wonder, will the rapid pace of digitization re-define HR? Of course yes, with millennials making up more than half of the current workforce — and predicted to make up 75 percent by 2020 — HR has to embrace technologies to keep at par with employee and business demands.

The Effect of Big Data

A lot of work in HR used to be related to adherence to compliances and therefore, huge amount of work related to paperworks was involved. But, now things have changed. Online portals and platforms provide HR with all the information that they need. Today’s technology gives HR professionals access to the power of Big Data and changes the way businesses understand their customers, build their own brands and communicate to prospective employees.

One of the boons of Big Data is Predictive Analytics. In big corporations, it is very difficult to keep a track of each employee. Predictive analytics enables HR to understand which employee needs an additional training.

High Up in the Clouds

Another technology which is impacting HR in a big way. Gathering and storing of information has always been a major function of the HR department, and the stack of files not only waste office space but are very difficult to trace as well. Can you even imagine, a millennial, who is always glued to his smartphone will have the patience to go through all the piles of paper?

High Up in the Clouds

Thanks to cloud technology, all of this information can instead be stored in the cloud. No longer does an employee need to tick the boxes while filling up a feedback form which again runs the risk of getting lost. All the employee information like tax documents, payroll, feedback etc can be stored online securely.

Cloud-based systems and Big Data go hand in hand. With Machine Learning emerging steadily, all these data will make a lot of sense few years down the line, it all depends how well can one derive relevant information out of it.

Chat with the Bots

There are some information which are very subjective in nature, like how to fill the Form 19 or file for the income tax returns. It makes no sense if the employee walks up to the HR managers for day-to-day queries or any concerns they might have regarding their pay, leaves, performance etc. To narrow down the gap of communication between the employees and the HR, PeopleStrong recently launched India’s first HR chatbot ‘Jinie’. From a transactional interface with employees to a conversational interface, Jinie the India’s first HR Chatbot will be able to provide the next level of experience to its employees.

In the era of smartphones, this will be a great boost in employee engagement.

These are few of the many ways in which the HR domain will change and adapt itself to digitization. With the burden of a lot of paperwork gone from the shoulders and with new data in hand, HR department will be fully equipped to make the employees life much easier and will add more value in business.


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Customer Success | The HR Tech Weekly®

Journey Science in Telecom: Take Customer Experience to the Next Level

Journey Science in Telecom: Take Customer Experience to the Next Level

Journey Science, being derived from connected data from different customer activities, has become pivotal for the telecommunications industry, providing the means to drastically improve the customer experience and retention. It has the ability to link together scattered pieces of data, and enhance a telco business’s objectives. Siloed approaches are becoming obsolete – take call centers as an example – there is only so much that you can do with data from only one system.

By using insights from customer journey analytics, telco businesses can better measure the user experience, and make informed decision for refining it. The data not only allow them to take proactive approach towards customer satisfaction, but enable the prediction of future failures as well. With customer journey analytics, you can evaluate the touchpoints to journeys, and revamp your strategies to better cater to customers’ needs.

In the telecom industry, it is difficult for a business to effectively manage the massive volume of data with the existing systems and technology. There are several aspects where telecom companies need to make improvements, such as reduce costs, improve customer experience, increase conversion rates, and many more. To do so, they need to derive meaning from the collected data by finding connections among them. This linked data is also known as journeys. Journeys provide you with relevant data that enable you to make well-grounded business decisions by looking at customer transactions as a whole, and determining where direct improvements are needed.

Customer Journey Analytics is Transforming Telecommunications

Many leading telco businesses are embracing the Journey Science concept, and deem it to be the best way to make greater impact on the target audience. One good way to better understand digital journeys is through a multi-channel, end-2-end, view. Journey Sciences, at its best, provides enhanced data accessibility and increased analytics agility, and helps in weaving together disparate pieces of data. This makes it possible for telco businesses to link together structured and unstructured data back to their strategic objectives, and quickly modify them to ensure they cope with the evolving customer demands. However, in order to get insight into customer experience through journey analytics, it is critical to focus not only on the individual moments but the customers’ end-to-end experiences as well.

Customer Experience Boost

The main benefit of customer journey analytics for telco companies is that it enables them to better recognize customer needs, and assess their satisfaction level. While most people think Journey Science is all about marketing, it mainly focuses on the services domain. For example, a customer seeking technical support for their device has multiple paths to resolution. Journey Science enables businesses to evaluate each step of the journey experience, and figure out the critical points that could negatively impact customer experience. With this kind of information, businesses can develop strategies to overcome hurdles customers face on all such touchpoints, resulting in improved customer experience.

Improving Customer Journeys through Transparency

Connecting the Dots

For improving customer experience, it is essential to connect all the data down to the individual customer level to fully understand the required changes. For telco businesses to completely understand customer journeys, they must gather data from many different channels, and track the individual journey the customer experiences. Typically, more than 50 percent of customers make multi-channel journeys; meaning, in order to understand their behavior, establishing connection among all the data is extremely important. Because of the deep roots of technology in today’s common lifestyle, many journeys start from digital channels, but eventually go into a human channel for completion.

Utilizing Aggregate and Raw Data

Apart from giving a complete picture of customer journeys, the analytics let you tap into different levels of aggregation, allowing you to view raw data as well. With journey mapping, telco businesses can benefit from both in-depth data points and aggregated data sets. Since a single customer journey can compile hundreds of thousands of data points, having aggregated views makes it much easier to pinpoint and prioritize the problematic areas. On the other hand, some journeys may yield unclear results, for example, unusual behavior of a customer on a webpage. In such a case, having access into the raw data renders the ability to focus on one key area and get invaluable insights.

Making Changes through Data Availability 

Effective utilization of data from customer journey analytics allows telco to revamp their strategy as well as make smaller improvements on a continuous basis. Getting immediate feedback regarding a certain change is critical for understanding its impact. You can determine whether the intended results will be realized, or should you scale-up or sustain the change. However, a manual, project-based approach that only provides an overview of the required data will not be enough to transform journeys successfully. Instead, you should opt for an agile, iterative, analytic approach that uses continuous data availability.

It won’t be wrong to say that all those ad-hoc, manual, project-based approaches using snapshots of data have severe limitations.

Better data accessibility to more than 18 telco raw data sources as a prerequisite 

How the Customer Journey differs in both Fixed and Mobile Telco

Mobile (mobile data usage, subscriptions, charges, and mobile data access)

Several small customer journeys can be linked together to make improvements to a mobile telco operation. One great way is through customer engagement, i.e. moving down to individualized journeys of each customer instead of mass-segmentation. Journey Science opens doors for mobile telco companies to take personalization up a notch, and provide customized recommendations based on the journeys of each customer. You should also utilize real-time context to enhance customer engagement for better results.

Mobile customer experience comprises of several touchpoints where a subscriber interacts with a service provide agent – it can be during retail, billing, customer support, visible marketing campaigns, and others. Consider three customers below that have 3 different journeys to perform the same action.

Fixed line providers (phone, internet, entertainment)

Fixed line providers have an additional interaction channel with field technicians being deployed to customers’ homes for service. These field service appointments are a major part of customer experience and often have significant variability for different customers. Consider the following journey which involves multiple appointments, agent phone calls, and delays:

Improve key journeys for fixed Telco’s

Journey Science is Moving towards Predictive Analytics

The Journey Science concept is increasingly becoming popular across the telco industry, as it greatly benefits by assessing journeys of individual customers and allow them to develop customized strategies. Moreover, it allows telco businesses to anticipate the potential pitfalls leading to negative customer experience and prevent it altogether. By tapping into the data from customer journey, telco can streamline their operations and provide a better, more satisfying experience to their customers.

Derived value from Customer Journey data by Journey Science & Journey Analytics

In today’s world, customer satisfaction is the keystone for success in every industry, including telco. Businesses should turn to the Journey Science movement, and optimize their processes by carefully analyzing customer journeys and making improvements accordingly. Effective utilization of customer journey analytics leads to better redesigning efforts, ultimately reducing costs, enhancing customer experience, and stretching bottom-line.

About the Authors:

Want to talk more about Journey Sciences? Connect with Rogier van Nieuwenhuizen, Executive Vice President, EMEA region at ClickFox, on LinkedIn and join Journey Science movement on Twitter by following @journey_science and the Journey Science’s LinkedIn Group today.

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


Source: Journey Science in Telecom: Take Customer Experience to the Next Level | Ronald van Loon | Pulse | LinkedIn

Maturity of HR Analytics Demands Right Foundation

HR Analytics

Currently, there are lot of discussions, articles, and engagements on Analytics for HR. More precise, the People or Workforce analytics when you are considering talent or workforce.

People and Workforce Analytics are a set of analytics to learn and answer the talent management related questions. It could be on workforce planning, talent sourcing, talent acquisition, performance management, talent retention. Or even on employee wellness, culture fit, and engagement. Making the data-driven decision from the business insights is the key purpose of any analytics.

Most of the organizations are still using the fundamental or basic analytics. They are reports based or using descriptive approach. But the workforce, related challenges are on the high increase. And we need a quantitative or a matured approach for handling these. It is necessary to understand the business insight and competitive advantage from the maturity of their HR Analytics.

Maturity Levels for HR Analytics

Before any beginning, it is important to know the possible maturity levels of the Analytics. As it does not only provide the opportunity to create a roadmap for the future. But also to understand the strengths, weaknesses, and possibilities for the growth.

Analytics Maturity Curve

The Descriptive Approach uses operational reporting based on business needs. More focus is on data exploration, data accuracy, and metrics analysis. Advanced reporting is also used for benchmarking, manual decision making and to generate dashboards.

Today, there is already some impressive engagement with Predictive Analytics in many organizations. The predictive approach uses statistical analysis, forecasting, correlations, and development of the predictive models. It helps in making predictions and for taking smarter decisions like in talent management for the organizations. For this, one needs to explore talent data for predictive models and statistical approach. And also needs to get ready with the proper business questions and specific reasons. Otherwise, they are neither actionable nor add any value to the organizations.

Predictive Analytics is also used to remove the human biases from one organization for taking an important decision. It has more resemblance with marketing behaviors while HR reporting mirrors finance.

The major purpose of an analytics is to have business decisions based on the data. Support in decision making and to help in making proper actions. Prescriptive Approach assists in this with optimization, strategic foresight, and real-time analysis. Prescriptive analytics not only anticipates what will happen and when it will happen. But also tell why it will happen.

Cognitive Approach is just the next level to perspective but both of them overlap to some extent. Actually, there is a bit overlapping among predictive, perspective and cognitive approaches.

The cognitive approach helps also in decision automation and applies cognitive computing. With reasoning, machine learning, natural language processing, and intelligence. According to Wikipedia, cognitive computing combines artificial intelligence and machine-learning algorithms. In an approach which attempts to reproduce the behavior of the human brain.

One of the most important part of this maturity curve is the foundation, in fact, most of the time spent here during any analytics project. The basic building blocks for matured and advanced approaches. One must have the basic understanding and preparation for any HR Analytics approaches. And it is advisable to have a proper planning to achieve the best.

Investments are only worth full with good returns, and for that, we need to study, understand and prepare with the basics.

And for this reason, it is also important to understand the foundation, to get started with People Analytics or HR Analytics. It is important to take it on from the beginning, as it is necessary for the long-term benefits and add significant value to the organizations. So let’s explore it.

Foundation for HR Analytics

There are certain aspects and factors which are necessary to get explored before starting any analytics projects. Organizations should have the insights or answers, for all these aspects and open questions related to them, to get the start in a proper way.

Foundation for HR Analytics

  • Data Preparation: The process of collecting, cleaning, validating and consolidating data into a single repository. The most important factor to get started with Analytics. And it is also necessary to collect the right and relevant data sources to help the workforce and the business. Right data at right time could make things easier for the business and for the organization. Another important aspect could be to gather non-HR data. Like net profits, cost effectiveness, sales revenue, and other important metrics from the organization. To add more relevance in the data preparation.
  • Cultural Readiness: Organizations need to specify the need to adopt the disruptions and it must be able to fit into the company culture. Leaders, managers, and key influencers should share the vision. And ensure readiness to drive the initiative throughout the organization. Without this readiness, it is not easy to understand the real value, and will not add any significant impact to the business.
  • Platform Adoption: Most of the HRIS solutions come up with their own analytics options. But they are valuable confine with their functional perspective only. If they are not relevant for the business insights and decision making, there is no meaning to invest in them. So, there is always an option to build the own analytics solution. Based on some available analytics platforms from the market. Another alternative option is to get a partner with an experienced vendor or having the right expert with the right platform.
  • Business Insights: It is necessary to know the business challenges and metrics which are critical for the organization and work for the workforce as well. Based on the issues which are seeking to address, proper data sources need to get defined. Identifying the critical business question from the business partners is necessary. It is also important to clarify the need of Analytics to have a better competitive advantage.
  • Data Integration: Integration always being an important factor for any changes whether on systems or people or data. Proper data integration is necessary among all different systems, businesses, and technologies. Significant for the data sources. Data security, privacy, and protection are also becoming critical challenges for any organizations. Any analytics project must be compatible with laws, rules, policies, and localizations. A close bonding is necessary among IT, HR and Business in this case.
  • Governance: Data quality is the biggest challenge for most of the organizations, especially when working with data based on people. Data is the most important aspects of the foundation. And it is important to prepare them to gain valuable business insights. Data governance plays a vital role in all these so that the data can be trusted and managed. Governance is also needed in terms of management, support, and sponsorship of the company.

By gathering, analyzing and exploring all relevant data one can not only answer the critical business questions. But also can take necessary actions from the interpretations of the data and context.

During analyzing the data one should look at the bigger picture rather than handle small challenges. It would be good if one can focus on making the best decisions for a workforce and the business as a whole. In most cases, an HR Analytics Leader is needed. The one who leads the analytics projects, involved in all decision-making processes and focus on quantifying the impact of talent investments on a business. And also improves some of the core processes within the organization with People Analytics.

One should also know the aspects which are necessary for the foundation of the analytics. It may vary among the organizations, with respective leaders, stakeholders, and Human Resources units. And thus in most of the cases, there is a need for some brainstorming before preparing for any foundation.

Design thinking process could be a game changer for any organization here.

Aspects necessary for the foundation of HR Analytics which should not be ignored at any cost:

  • Creativity: The creative route has a difference from an analytical route. But it is necessary to take a creative approach to gather relevant information, prepare data, developing the model, interpret the insights and even taking the right decision. One needs to be creative as well, to find the best result and taking actions.
  • Knowledge:  Knowledge is the king and no doubt it is a must for the foundation for analytics as well. Whether it is related to the business processes, people, technology, data, statistics or any skills. Knowledge is necessary everywhere. It is also advisable to update the knowledge as well in certain time periods.
  • Expertise: A proper team should be built and it must include diverse individuals from both business and technical side based on the needs. Business Leaders, Business Analyst, Program Managers and other business people could be there on one side. On another side may have Data Analysts, Data Architect, and Data Scientist. Especially in a case of complex analytics projects. Need to involve those experts, who have strong experience in analytics area.
  • Methodologies: An iterative process is needed as the foundational methodology. Starting from business understanding, analytic approaches, data preparation, modeling, evaluation, and deployment. Feedback is also necessary for a well strategic plan here. The methodology should be independent from the technology. As it is providing many tools, applications, and platforms to perform analytics. And it should also provide a framework for processing methods and processes to get the best results. A value driven approach with agile methodology could be used to having higher success rates in analytics projects.

Once we are ready with the foundation for Analytics, we have already started engaging to HR Analytics or People Analytics. But the journey has just begun. There are tremendous opportunities for exploration based on the matured approaches for any organization. Every organization has its own maturity level. And it’s depending on them to decide their future of analytics, based on their further commitment.

About the Author:

Soumyasanto Sen

Soumyasanto Sen — Blogger, Speaker and Evangelist in HRTech who try to think Out of the Box! Engaging with Companies, Startups & Entrepreneurs in driving Transformation.

Professionally Consultant, Manager, Advisor, Investor in HR Tech. Focusing on Strategies, Analytics, Cloud, UX, Security, Integration and Entrepreneurship in Digital HR Transformation.


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Published earlier in Analytics in HR: Why Mature HR Analytics demands the right Foundation

From New Technology to ‘Purposeful Innovation’ – Three Trends That Can Help Businesses Innovate & Grow in 2017

Written by Himanshu Palsule, CTO at Epicor Software.

From New Technology to ‘Purposeful Innovation’ – Three Trends That Can Help Businesses Innovate & Grow in 2017

In the current climate, operational efficiency and business agility are more important than ever to support modern business innovation. As global markets combine with competitive pricing pressures to place greater stress on maintaining margins, organisations must seek the efficiencies needed to protect market share.

Himanshu Palsule, CTO at Epicor Software
Himanshu Palsule, CTO at Epicor Software

At the same time, global economic forces are opening up opportunities in new markets and organisations of all sizes are looking to take advantage of the changing economic tide to grow their business. The pressure is now on the CIO and his/her team to drive change and enable this high-growth mode. The challenge for many companies is matching technology investments with the rapidly changing needs of the business.

A solid technology strategy should place the onus on innovation with a purpose and going in to 2017, I see three technology trends that have the power to transform businesses by providing the tools to innovate. These technologies have the potential to be central to business success over the coming years.

  1. Enabling cloud-driven change

For some organisations, adopting cloud computing services can be a simple, tactical exercise to meet some immediate infrastructure needs. But for those looking to drive real technology transformation, it can be the catalyst to embracing an entirely new strategy for IT.

Up until recently cloud computing has, for the most part, been used to speed up existing individual processes while reducing costs. It is only now, as the cloud journey grows more mature, that we can begin to see its full potential to transform business models and working practices.

The cloud opens up exciting new possibilities for CIOs, COOs and CFOs to think differently about their IT infrastructure. Adoption of cloud-based enterprise resource planning (ERP) systems, for example, is on the rise because sharing data quickly and efficiently can dramatically reduce costs and increase the speed of production.

There’s also a growing acceptance that cloud adoption is not just for start-up companies. Large enterprises are transitioning their entire infrastructure and data ecosystems into the cloud because these systems have the advantage of taking the burden of upgrades and management, freeing up valuable resources to focus on innovation and business growth.

  1. Extracting value from big data and IoT

According to a recent report by Machina Research, the total number of IoT connections are estimated to grow 16% annually over the next 10-year period from 6 billion in 2015 to 27 billion in 2025. Total IoT revenue opportunity is projected to grow to $3 trillion in 2025, up from $750 billion in 2015.

If you talk to customers in the manufacturing and retail sectors for example, they’ll say they’ve been collecting and tracking data on machines, production, and inventory for years. In retail, for example, smart supply chains enable applications for tracking goods and real time information exchange about inventory among suppliers and retailers.

The next step for us, and our customers, is to take the data that is available and analyse it in context, to make better and more efficient business decisions. However, the challenge for ERP systems has been around how to transform the onslaught of unstructured data into practical information.

As technology develops we can expect to see more integration between ERP, big data and predictive analytics because data is the business resource of the future—both in terms of optimising processes and services, and as a basis for innovative business models.

  1. Mobility drives greater visibility

Mobile and social technologies are enabling new business models and processes but it’s important to remember that mobility can mean many different things to different organisations. For one company, it might be the ability to set up a remote warehouse. For another, it might be the ability to interact and collaborate on social platforms across borders and time zones.

Mobility should be an essential part of the platforms we build as mobile applications provide greater employee visibility and accuracy of information, enabling companies to respond quickly to changing demands with real-time capabilities.

New utilisations of mobile devices and apps are happening every day and drastically changing the way business gets done.

Summary — keeping up the pace of innovation

As companies become more complex and globally dispersed, the need for increased collaboration, visibility and efficiency will continue to accelerate. The world is getting smaller and supply chains are expected to get faster. Having the right technology in place to underpin operations is key to keeping up, regardless of geographic location or industry.

Technology on its own is not a sufficient strategy. But understanding how cloud, big data, social, mobility, analytics and IoT technologies can underpin business models, what we call ‘purposeful innovation’ is central to achieving business growth.


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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