AI – The Present in the Making

AI – The Present in the Making

I attended the Huawei European Innovation Day recently, and was enthralled by how the new technology is giving rise to industrial revolutions. These revolutions are what will eventually unlock the development potential around the world. It is important to leverage the emerging technologies, since they are the resources which will lead us to innovation and progress. Huawei is innovative in its partnerships and collaboration to define the future, and the event was a huge success.

For many people, the concept of Artificial Intelligence (AI) is a thing of the future. It is the technology that has yet to be introduced. But Professor Jon Oberlander disagrees. He was quick to point out that AI is not in the future, it is now in the making. He began by mentioning Alexa, Amazon’s star product. It’s an artificial intelligent personal assistant, which was made popular by Amazon Echo devices. With a plethora of functions, Alexa quickly gained much popularity and fame. It is used for home automation, music streaming, sports updates, messaging and email, and even to order food.

With all these skills, Alexa is still in the stages of being updated as more features and functions are added to the already long list. This innovation has certainly changed the perspective of AI being a technology of the future. Al is the past, the present, and the future.

Valkyrie is another example of how AI exists in the present. There are only a handful of these in the world, and one of them is owned by NASA. They are a platform for establishing human-robot interaction, and were built in 2013 by a Johnson Space Center (JSC) Engineering directorate. This humanoid robot is designed to be able to work in damaged and degraded environments.

The previous two were a bit too obvious. Let’s take it a notch higher.

The next thing on Professor Jon Oberlander’s list was labeling images on search engines. For example, if we searched for an image of a dog, the search engine is going to show all the images that contain a dog, even if it’s not a focal point. The connected component labeling is used in computer vision, and is another great example of how AI is developing in present times.

Over the years, machine translation has also gained popularity as numerous people around the world rely on these translators. Over the past year, there has been a massive leap forward in the quality of machine translations. There has definitely been a dramatic increase in the quality as algorithms are revised and new technology is incorporated to enhance the service.

To start with a guess, and end up close to the truth. That’s the basic ideology behind Bayes Rule, a law of conditional probability.

But how did we get here? All these great inventions and innovations have played a major role in making AI a possibility in the present. And these four steps led us to this technological triumph;

  • Starting
  • Coding
  • Learning
  • Networking

Now that we are here, where would this path take us? It has been a great journey so far, and it’s bound to get more exciting in the future. The only way we can eventually end up fulfilling our goals is through;

  • Application
  • Specialization
  • Hybridization
  • Explanation

With extensive learning systems, it has become imperative to devise fast changing technologies, which will in turn facilitate the spread of AI across the world. With technologies such as deep fine-grained classifier and the Internet of Things, AI is readily gaining coverage. And this is all due to Thomas Bayes, who laid the foundations of intellectual technology.

If you would like to read more from Ronald van Loon on the possibilities of AI, please click Follow and connect with him on LinkedIn and Twitter.


Source: AI – The Present in the Making | Ronald van Loon | Pulse | LinkedIn

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Artificial Intelligence

Trends Shaping Machine Learning in 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world is Artificial Intelligence (AI). The two technological concepts, Machine Learning and Artificial Intelligence, are often used as interchangeable terms. However, it is important to understand that both technologies supplement each other and are essentially different in terms of their core functions.

It is often predicted by technology enthusiasts and social scientists that human beings in the workforce will soon be replaced by self-learning robots. It is yet to be seen whether there lies any truth in these predictions or not but for 2017, the following trends have been prominent in the development of Machine Learning.

Giant companies will develop Machine Learning based Artificial Intelligence systems

In 2016, we saw many prominent developments in the domain of Machine Learning, and numerous artificial intelligence applications found a way to our phone screens and caught our attention. In the previous year, companies just touched the tip of the iceberg and in 2017, we will continue to see more developments in the field of machine learning. Big names such as Amazon, Google, Facebook and IBM are already fighting a development war. Google and Amazon launched successful apps, which include Amazon Echo and Google Home, at the beginning of the year, and we have yet to see what these tech giants have in stores for their customers.

Algorithm Economy will be on the Rise

Businesses greatly value data to take the appropriate actions, whether it is to understand the consumer demand or comprehend a company’s financial standing. However, it is not the data alone they should value because without an appropriate algorithm, that data is worth nothing. Peter Sondergaard, Senior Vice President of Gartner Research, says that, “Data is inherently dumb and the real value lies in the algorithms which deduce meaningful results from a cluster of meaningless data”.

Algorithm Economy has taken center stage for the past couple of years, and the trend is expected to follow as we expect to see further developments in machine learning tools. The use of algorithm economy will distinguish small players from the market dominators in 2017. Small businesses that have just entered the transitional phase of embedding machine learning processes in their business models will be using canned algorithms in tools such as BI, CRM and predictive analysis. On the contrary, large enterprises will use proprietary ML algorithms.

Expect more Interaction between Machine and Humans

Google Home and Amazon Echo received an exceedingly positive response from the audience which made it evident that consumers perceive human-machine interaction positively. Innovative technologies embedded with machine learning processes prove to be helpful under various circumstances; for example, helping people with eyesight issues to navigate. But will they completely replace human-human interaction? Maybe 25 years down the road, but we do not see that happening anytime soon. Machine learning has made it increasingly possible for machines to learn new skills, such as to sort, analyze and comprehend. But nevertheless, there are certain limitations to it. Automated cars have frequently been tested, and even with modified algorithms and advanced technologies, the chance of an error is still present. This example alone is enough to convince that machines will not completely replace humans, at least not anytime soon.

Conclusion

Machine Learning and Artificial Intelligence is a promising field with much potential for growth. We have seen some recent developments in the sector which, not long ago, people believed were not possible. Therefore, we cannot give a definite verdict regarding the industry’s potential for growth. However for now, intelligent machines are only capable of handling the repetitive tasks and can follow a predetermined pattern. It lacks the skill to figure out things which are out of the ordinary, and we still require human intervention for keeping the chaos at bay in such situations.

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read Ronald van Loon future posts then please click ‘Follow‘ and feel free to also connect on LinkedIn and Twitter to learn more about the possibilities of Big Data .

This article was originally posted on SimpliLearn

Source: Trends Shaping Machine Learning in 2017 | Ronald van Loon | Pulse | LinkedIn

5 Ways Businesses Can Cultivate a Data-Driven Culture | The HR Tech Weekly®

5 Ways Businesses Can Cultivate a Data-Driven Culture

The pressure on organizations to make accurate and timely business decisions has turned data into an important strategic asset for businesses.

In today’s dynamic marketplace, a business’s ability to use data to identify challenges, spot opportunities, and adapt to change with agility is critical to its survival and long-term success. Therefore, it has become an absolute necessity for businesses to establish an objective, data-driven culture that empowers employees with the capabilities and skills they need to analyse data and use the insights extracted from it to facilitate a faster, more accurate decision-making process.

Contrary to what many people think, cultivating a data-driven culture is not just a one-time transformation. Instead, it’s more like a journey that requires efforts from employees and direction from both managers and executives. In this article, I am sharing five different ways businesses can accelerate their transformation into a data-driven enterprise.

1. Establish a Clear Vision

Establishing a clear vision is essential for putting data into the DNA of an organization. An executive, preferably the CIO or CDO, should present the vision to the workforce and provide the rationale for this shift in culture and in benefits. This, in turn, will set stage for the work ahead and provide an opportunity to clear misconceptions.

 2. Ensure Easy and Secure Access to Data

Data can be truly considered an asset when its accuracy is trusted, its provenance is well established, and its complete security is ensured. On the other hand, optimal utilization of data requires governance and openness. To ensure this, you should consider a layered approach to make data available in a manner for which its security, governance and confidentiality is not compromised.

3. Keep Your Data Clean and Up-to-Date

It’s very hard to analyze and extract something valuable from poorly organized, inaccurate, dated information. Therefore, you should develop clear mechanisms regarding the collection, storage, and analysis of data. Make sure all your data inputs are centralized in a single location for easy integration and regular updates. This way, your employees can gather the most recent information from a single place and make more accurate decisions.

4. Create Agile Multi-Disciplinary Teams

People, not tools drive the culture of a company. Therefore, in order to create a fact-driven work environment, businesses should invest in the skills of their people. Make sure that each team contains at least one member who’s well-skilled and experienced at data analytics.

5. Develop Reward Mechanisms

Sharing data successes is important to inspire others and develop a healthy, competitive, data-driven culture. To share the results achieved by a team or an individual, you can use different communication tools, such as videos and blogs, organize special gatherings, or share the results on your company portal. Make sure that you choose initiatives that are in line with your company’s long-term strategy. For example, if your objective is to penetrate new markets or gather more information about your target customers, you should acknowledge and reward the initiatives that help you make progress towards these strategic goals.

Unless communicated across an organization, data remains worthless. To extract the right information and insights from structured and unstructured data, it is important that you focus your efforts on cultivating a data-driven culture that empowers employees with the resources and skills they need to leverage data and obtain the right information at the right time to make more accurate decisions.

About the Author:

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read more from Ronald van Loon on the possibilities of Big Data and IoT, please click “Follow” and connect on LinkedIn and Twitter.

This article was originally posted on Simplilearn

Source: 5 Ways Businesses Can Cultivate a Data-Driven Culture | Ronald van Loon | Pulse | LinkedIn

Securing Competitive Advantage with Machine Learning | The HR Tech Weekly®

Securing Competitive Advantage with Machine Learning

How to Secure Your Competitive Advantage with Machine Learning | The HR Tech Weekly®

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization and data analytics were no different.

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Here are the four key elements of a comprehensive data management system:

  • Hybrid data management
  • Unified governance
  • Data science and machine learning
  • Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

Want to learn more about the topic? Watch replay of the live session with Hilary Mason, Dez Blanchfield, Rob Thomas, Kate Silverton, Seth Dobrin and Marc Altshuller.

Follow me on Twitter and LinkedIn for more interesting updates about machine learning and data integration.


Source: Securing Competitive Advantage with Machine Learning | Ronald van Loon | Pulse | LinkedIn

Enterprise Journey to Becoming Digital

Do you want to be a digital enterprise? Do you want to master the art of transforming yourself and be at the forefront of the digital realm?

How can you change your business to achieve this?

Derive new values for yourself, and find better and more innovative ways of working. Put customer experience above and beyond everything as you find methodologies to support the rapidly changing demands of the digital world.

Your transformation will be successful only when you identify and practice appropriate principles, embrace a dual strategy that enhances your business capabilities and switch to agile methodologies if you have not done it already.

The journey to becoming a digital maestro and achieving transformation traverses through four main phases.

  • Becoming a top-notch expert with industrialized IT services – by adopting six main principles
  • Switching to agile operations to achieve maximum efficiency – so that you enjoy simplicity, rationality and automation
  • Creating an engaging experience for your consumers using analytics, revenue and customer management – because your customers come first; their needs and convenience should be your topmost priority
  • Availing opportunities for digital services – assessing your security and managing your risks

Becoming a top-notch expert with industrialized IT services

There are five key transformation principles that can help you realize the full potential of digital operations and engagement.

  • Targeting uniqueness that is digitized
  • Designing magical experiences so as to engage and retain your consumers
  • Connect with digital economics, and collaborate so as to leverage your assets
  • Operate your business digitally, customer experience being the core
  • Evolving into a fully digital organization through the side by side or incremental approach

Initially a digital maturity analysis has to be performed, followed by adoption of a targeted operational model. Maturity can be divided into five different levels: initiating, enabling, integrating, optimizing and pioneering, which are linked to seven different aspects: strategy, organization, customer, technology, operations, ecosystem and innovation, of which the last two are the most critical. The primary aim should be to cover all business areas that are impacted by and impact digital transformation.

Before taking a digital leap, the application modernization wheel should be adopted. Identify your targets, which will act as main drivers. Determine application states, and then come up with a continuous plan. This is referred to as the Embark phase, during which you understand the change rationale of your applications, and then improve metrics, which drive changes. During the Realize phase, you analyze ways in which you can change your operations and speed up your delivery. In the process, you have to improve quality, while ensuring your product line is aligned with your business needs. You establish DevOps, beginning from small teams, and then moving forward using new technologies.

The third phase is Modernize, during which you plan and implement your architecture such that your apps are based on API services. The last stage is Optimize in which performance is monitored, and improvements are made when and where they are necessary.

Switching to agile operations to achieve maximum efficiency

Data centers now feature several applications, suitable for the IT, telecommunication and enterprise sectors, but their offered services have to be responsive to the changing trends and demands. Ericsson brings agility into the picture so as to achieve efficiency through automation. This can be made possible with the NFV Full Stack, which includes a cloud manager, execution environment, SDN controllers and NFV hardware. The solution is capable to support automated deployment while providing you flexibility through multi VIM support. Check out this blog post to see a demonstration of a virtualized, datacenter and explore their vision of future digital infrastructure.

NFV’s potential can be fully achieved only when the hybrid networks are properly managed, which dynamic orchestration makes a possibility. The approach taken automates service design, configuration and assurance for both physical and virtual networks. Acceleration of network virtualization is being realized through the Open Platform for Network Functions Virtualization (OPNFV), a collaborative project under the Linux Foundation that is transforming global networks through open source NFV. Ericsson is a platinum-level founding OPNFV member, along with several other telecom vendors, service providers and IT companies leading the charge in digitalized infrastructure.

Creating an engaging experience for your consumers

Customer experience is the central focus when you are in the digital realm. Customer experience should be smooth, effortless and consistent across all channels.

Design a unique omnichannel approach for your customers. This means that you should be able to reach out to your customers through mobile app, social media platforms and even wearable gadgets. Analyze real-time data, and use the results for improving purchase journeys obvert different channels like chatbots and augmented reality. Advanced concepts like clustering and machine learning are used to cross data over different domains, and then take appropriate actions. For instance, if you were a Telco, you should be able to offer a new plan, bundle or upgrade to each customer at the right time. All of the analytics data can also be visualized for a complete understanding through which the customer journey can be identified, and the next best action can be planned out.

Availing opportunities for digital services

Complexity increases when all your systems are connected, and security becomes a more important concern. You should be able to identify new vulnerabilities and threat vectors, and then take steps to protect your complete system. And this protection should extend to your revenues, and help you prevent fraud.

A Security Manager automates security over the cloud as well as physical networks. The two primary components are Security Automation and 360 Design and Monitoring. New assets are detected as security is hardened, which are then monitored continuously.

Additionally the Digital Risk and Business Assurance enable your business to adapt in the dynamic environment while reducing impact on your bottom line. Assurance features three levels: marketplace, prosumer and wholesale assurance. The end result is delivery of a truly digital experience.

Want proof that the above methodologies do work wonders? Two of Ericsson customers, Verizon and Jio, have already been nominated as finalists for the TM Forum EXCELLENCE Awards.

I also encourage you to join and/or follow TM Forum Live this week. If you’re headed to the conference, be sure to check out the Ericsson booth and connect with the team to learn more and discuss your digital transformation journey.

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

Source: Enterprise Journey to Becoming Digital | Ronald van Loon | Pulse | LinkedIn

How Machine Learning is Revolutionizing Digital Enterprises

How Machine Learning is Revolutionizing Digital Enterprises

According to the prediction of IDC Futurescapes, two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today.

If you want to stay ahead of the game, then you cannot afford to wait for that to happen. Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.

In short, ML will connect intelligently people, business and things. It will enable completely new interaction scenarios between customers and companies and eventually allow a true intelligent enterprise. To realize the applications that are possible due to ML fully, we need to build a modern business environment. However, this will only be achieved, if businesses can understand the distinction between Artificial Intelligence (AI) and Machine Learning (ML).

Understanding the Distinction Between ML and AI

Machines that could fully replicate or even surpass all humans’ cognitive functions are still a dream of Science Fiction stories, Machine Learning is the reality behind AI and it is available today. ML mimics how the human cognitive system functions and solves problems based on that functioning. It can analyze data that is beyond human capabilities. The ML data analysis is based on the patterns it can identity in Big Data. It can make UX immersive and efficient while also being able to respond with human-like emotions. By learning from data instead of being programmed explicitly, computers can now deal with challenges previously reserved to the human. They now beat us at games like chess, go and poker; they can recognize images more accurately, transcribe spoken words more precisely, and are capable of translating over a hundred languages.

ML Technology and Applications for Life and Business

In order for us to comprehend the range of applications that will be possible due to ML technology, let us look at some examples available currently:

  • Amazon Echo, Google Home:
  • Digital assistants: Apple’s Siri, SAP’s upcoming Copilot

Both types of devices provide an interactive experience for the users due to Natural Language Processing technology. With ML in the picture, this experience might be taken to new heights, i.e., chatbots. Initially, they will be a part of the apps mentioned above but it is predicted that they could make text and GUI interfaces obsolete!

ML technology does not force the user to learn how it can be operated but adapts itself to the user. It will become much more than give birth to a new interface; it will lead to the formation of enterprise AI.

The limitless ways in which ML can be applied include provision of completely customized healthcare. It will be able to anticipate the customer’s needs due to their shopping history. It can make it possible for the HR to recruit the right candidate for each job without bias and automate payments in the finance sector.

Unprecedented Business Benefits via ML

Business processes will become automated and evolve with the increasing use of ML due to the benefits associated with it. Customers can use the technology to pick the best results and thus, reach decisions faster. As the business environment changes, so will the advanced machines as they constantly update and adapt themselves. ML will also help businesses arrive on innovations and keep growing by providing the right kind of business products/services and basing their decisions on a business model with the best outcome.

ML technology is able to develop insights that are beyond human capabilities based on the patterns it derives from Big Data. As a result, businesses would be able to act at the right time and take advantage of sales opportunities, converting them into closed deals. With the whole operation optimized and automated, the rate at which a business grows will accelerate. Moreover, the business process will achieve more at a lesser cost. ML will lead businesses into environs with minimal human error and stronger cybersecurity.

ML Use Cases

The following three examples show how ML can be applied to an enterprise model that utilizes Natural Language Processing:

  • Support Ticket Classification

Consider the case where tickets from different media channels (email, social websites etc.) needs to be forwarded to the right specialist for the topic. The immense volume of support tickets makes the task lengthy and time consuming. If ML were to be applied to this situation, it could be useful in classifying them into different categories.

API and micro-service integration could mean that the ticket could be automatically categorized. If the number of correctly categorized tickets is high enough, a ML algorithm can route the ticket directly to the next service agent without the need of a support agent.

  •  Recruiting

The job of prioritizing incoming applications for positions with hundreds of applicants can also be slow and time consuming. If automated via ML, the HR can let the machine predict candidate suitability by providing it with a job description and the candidate’s CV. A definite pattern would be visible in the CVs of suitable candidates, such as the right length, experience, absence of typos, etc. Automation of the process will be more likely to provide the right candidate for the job.

  • Marketing 

ML will help build logo and brand recognition for businesses in the following two ways:

  1. With the use of a brand intelligence app, the identification of logos in event sponsorship videos or TV can lead to marketing ROI calculations.
  2. Stay up to date on the customer’s transactions and use that behavior to predict how to maintain customer loyalty and find the best way to retain them.

How Enterprises Can Get Started Implementing Machine Learning

Businesses can step into the new age of ML and begin implementing the technique by letting the machines use Big Data derived from various sources, e.g. images, documents, IoT devices etc to learn. While these machines can automate lengthy and repetitive tasks, they can also be used to predict the outcome for new data. The first step in implementation of ML for a business should be to educate themselves about its nature and the range of its applications. A free openSAP course can help make that possible.

Another step that can bring a business closer to ML implementation is data preparation in complex landscapes. The era of information silos is over and there is an imperative need for businesses to gather data from various sources, such as customers, partners, and suppliers. The algorithms must then be provided open access to that data so they can learn and evolve. The Chief Data Officer of the company can oversee the ML integration process.

To start with completely new use cases for Machine Learning is not easy and requires a good understanding of the subject and having the right level of expertise in the company. A better starting point for many companies would be to rely on ML solutions already integrated into standard software. By that it will connect seamless with the existing business process and immediately start to create value.

Lastly, businesses should start gathering the components necessary for building AI products. Among the requirements would be a cloud platform capable of handling high data volume that is derived from multiple sources. The relevant people are as important to this step as are the technology and processes. After all, they would be the ones who will be testing the latest digital and ML technologies.

If you want more information on SAP Machine Learning, then go here to subscribe to the webinar on Enabling the intelligent Enterprise with Machine Learning.

The presenters include Dr. Markus Noga: VP Machine Learning Innovation Center Network, SAP SE. You can follow him on Twitter. Ronald van Loon is the other presenter for the webinar. Mr. van Loon is counted among the Top 10 Big Data expert and is an IoT Influencer. You can also follow him on Twitter.


Source: How Machine Learning is Revolutionizing Digital Enterprises | Ronald van Loon | Pulse | LinkedIn

How to Build a Data Science Team

Businesses today need to do more than merely acknowledge big data. They need to embrace data and analytics and make them an integral part of their company. Of course, this will require building a quality team of data scientists to handle the data and analytics for the company. Choosing the right members for the team can be difficult, mainly because the field is so new and many companies are still trying to learn exactly what a good data scientist should offer. Putting together an entire team has the potential to be more difficult. The following information should help to make the process easier.

The Right People

What roles need to be filled for a data science team? You will need to have data scientists who can work on large datasets and who understand the theory behind the science. They should also be capable of developing predictive models. Data engineers and data software developers are important, too. They need to understand architecture, infrastructure, and distributed programming.

Some of the other roles to fill in a data science team include the data solutions architect, data platform administrator, full-stack developer, and designer. Those companies that have teams focusing on building data products will also likely want to have a product manager on the team. If you have a team that has a lot of skill but that is low on real world experience, you may also want to have a project manager on the team. They can help to keep the team on the right track.

The Right Processes

When it comes to the processes, the key thing to remember with data science is agility. The team needs the ability to access and watch data in real time. It is important to do more than just measure the data. The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes. They should not be handcuffed to a slow and tedious process, as this will limit effectiveness. Ideally, the team will have a good working relationship with heads of other departments, so they work together in agile multi-disciplinary teams to make the best use of the data gathered.

The Platform

When building a data science team, it is also important to consider the platform your company is using for the process. A range of options are available including Hadoop and Spark. Hadoop is the market leader when it comes to big data technology, and it is an essential skill for all professionals who get into the field. When it comes to real-time processing, Spark is becoming increasingly important. It is a good idea to have all the big data team members skilled with Spark, too.

If you have people on the team that do not have these skills and that do not know how to use the various platforms, it is important they learn. Certification courses can be a great option for teaching the additional skills needed, and to get everyone on the team on the same page.

Some of the other platforms to consider include the Google Cloud Platform, and business analytics using Excel. Understanding the fundamentals of these systems can provide a good overall foundation for the team members.

Take Your Time

When you are creating a data science team for the company, you do not want to rush and choose the wrong people and platforms or not have quality processes in place. Take your time to create a team that will provide your company with the quality and professionalism it needs.

About the Author:

Ronald van Loon has joined as an Advisory Board Member for its Big Data training category. Named by Onalytica as one of the top three most influential personalities of Big Data in 2016, Ronald will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.


Source: How to Build a Data Science Team | Ronald van Loon | Pulse | LinkedIn

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

What Is the Future of Data Warehousing?

Data Warehousing

There is no denying it – we live in The Age of the Customer. Consumers all over the world are now digitally empowered, and they have the means to decide which businesses will succeed and grow, and which ones will fail. As a result, most savvy businesses now understand that they must be customer-obsessed to succeed. They must have up-to-the-second data and analytical information so that they can give their customers what they want and provide the very best customer satisfaction possible.

This understanding has given rise to the concept of business intelligence (BI), the use of data mining, big data, and data analytics to analyze raw data and create faster, more effective business solutions. However, while the concept of BI is not necessarily new, traditional BI tactics are no longer enough to keep up and ensure success in the future. Today, traditional BI must be combined with agile BI (the use of agile software development to accelerate traditional BI for faster results and more adaptability) and big data to deliver the fastest and most useful insights so that businesses may convert, serve, and retain more customers.

Essentially, for a business to survive, BI must continuously evolve and adapt to improve agility and keep up with data trends in this new customer-driven age of enterprise. This new model for BI is also driving the future of data warehousing, as we will see moving forward.

Older BI Deployments Cannot Keep Pace for Success

As valuable as older BI applications and deployments have been over the years, they simply cannot keep pace with customer demands today. In fact, decision-makers in IT and business have reported a number of challenges when they have only deployed traditional BI. These include:

  • Inability to accurately quantify their BI investments’ ROI. Newer BI deployments implement methodologies for measuring ROI and determining the value of BI efforts.
  • A breakdown in communication and alignment between IT and business teams.
  • Inability to properly manage operational risk, resolve latency challenges, and/or handle scalability. While BI is intended to improve all of these, traditional BI is falling behind.
  • Difficulty with platform migration and/or integration.

Poor data quality. Even if data mining is fast and expansive, if the quality of the data is not up to par, it will not be useful in creating actionable intelligence for important business decisions.

Keeping Up with Customer Demand Through New BI Deployments

So how can combining traditional BI, agile BI, and big data help businesses grow and succeed in today’s market? Consider that big data gives businesses a more complete view of the customer by tapping into multiple data sources. At the same time, agile BI addresses the need for faster and more adaptable intelligence. Combine the two, along with already existing traditional BI, and efforts that were once separate can work together to create a stronger system of insight and analytics.

Through this new BI strategy, businesses can consistently harness insights and create actionable data in less time. Using the same technology, processes, and people, it allows businesses to manage growth and complexity, react faster to customer needs, and improve collaboration and top-line benefits – all at the same time.

The Drive for a New Kind of Data Warehousing

A new kind of data warehousing is essential to this new BI deployment, as much of the inefficiency in older BI deployments lies in the time and energy wasted in data movement and duplication. A few factors are driving the development and future of data warehousing, including:

  • Agility – To succeed today, businesses must use collaboration more than ever. Instead of having separate departments, teams, and implementations for things like data mining and analysis, IT, BI, business, etc., the new model involves cross-functional teams that engage in adaptive planning for continuous evolution and improvement. This kind of model cannot function with old forms of data warehousing, with just a single server (or set of servers) where data is stored and retrieved.
  • The Cloud – More and more, people and businesses are storing data on the cloud. Cloud-based computing offers the ability to access more data from different sources without the need for massive amounts of data movement and duplication. Thus, the cloud is a major factor in the future of data warehousing.
  • The Next Generation of Data – We are already seeing significant changes in data storage, data mining, and all things relate to big data, thanks to the Internet of Things. The next generation of data will (and already does) include even more evolution, including real-time data and streaming data.

How New Data Warehousing Solves Problems for Businesses

So how do new data warehouses change the face of BI and big data? These new data warehousing solutions offer businesses a more powerful and simpler means to achieve streaming, real-time data by connecting live data with previously stored historical data.

Before, business intelligence was an entirely different section of a company than the business section, and data analytics took place in an isolated bubble. Analysis was also restricted to only looking at and analyzing historical data – data from the past. Today, if businesses only look at historical data, they will be behind the curve before they even begin. Some of the solutions to this, which new data warehousing techniques and software provide, include:

  • Data lakes – Instead of storing data in hierarchical files and folders, as traditional data warehouses do, data lakes have a flat architecture that allows raw data to be stored in its natural form until it is needed.
  • Data fragmented across organizations – New data warehousing allows for faster data collection and analysis across organizations and departments. This is in keeping with the agility model and promotes more collaboration and faster results.
  • IoT streaming data – Again, the Internet of Things, is a major game changer, as customers, businesses, departments, etc. share and store data across multiple devices.

To Thrive in the Age of the Customers – Businesses Must Merge Previously Separate Efforts

Now that we are seeing real-time and streaming data, it is more important than ever before to create cohesive strategies for business insights. This means merging formerly separate efforts like traditional BI, agile BI, and big data.

Business agility is more important than ever before to convert and retain customers. To do this, BI must always be evolving, improving, and adapting, and this requires more collaboration and new data warehousing solutions. Through this evolution of strategies and technology, businesses can hope to grow and improve in The Age of the Customer.

Examples of the Future of Data Warehousing

And what exactly will the future of data warehousing look like? Companies like SAP are working on that right now. With the launch of the BW/4HANA data warehousing solution running on premise and Amazon Web Services (AWS) and others like it, we can see how businesses can combine historical and streaming data for better implementation and deployment of new BI strategies. This system and others like it work with Spark and Hadoop, as well as other programming frameworks to bring data and systems of insight into the 21st century and beyond.

Want to learn more about BI, agile BI, the future of data warehousing, and all things big data? 

Follow Ronald van Loon on LinkedIn and Twitter

And, if you have any thoughts on the subject, you may share them in the comment to the original post.


Source: What Is the Future of Data Warehousing? | Ronald van Loon | Pulse | LinkedIn

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