Q & A with David Green | The HR Tech Weekly®

People Analytics Is Core to the Future of the HR Function: Q&A with David Green

People Analytics Is Core to the Future of the HR Function

Today our guest is David Green, a true globally respected and award winning writer, speaker, conference chair and executive consultant on people analytics, data-driven HR and the future of work.

David is the Global Director, People Analytics Solutions at IBM Watson Talent. He is also the longstanding Chair, of the Tucana People Analytics conference series, the next edition of which – the People Analytics Forum, takes place in London on 29-30 November.

David has spoken at conferences and/or worked with people analytics leaders in over 20 cities in the past year including San Francisco, Sydney, London, Paris, Singapore, New York, Amsterdam, Moscow and Berlin. This affords David with a unique perspective and insight into what’s working, what’s not, and what’s forthcoming in the field of people analytics.

The interview is hosted by Alexey Mitkin, Founder, Publisher and Editor-in-Chief, The HR Tech Weekly® Online Media Co.

1. Hi David, and first of all thank you very much for this interview with The HR Tech Weekly®. The year of 2017 is approaching its end. What made a difference this year in the field of people management and HR technologies?

Thanks Alexey, it is a pleasure to speak with you. For me, 2017 has been a pivotal year in the field as the realisation that people analytics is core to the future of the HR function has become far more widespread. In one of his recent articles (see here), Josh Bersin described people analytics “as the lynchpin of success for HR in the next few years”, and I have to say I completely agree – although that probably doesn’t surprise you!

We still have some way to go in terms of widespread adoption and just as importantly in embedding analytics and data-driven decision making within organisational culture, but the acceptance that this is core rather than peripheral is a welcome momentum shift.

Elsewhere, the move from many companies to develop programs and technologies that personalise the candidate/employee experience in areas such as talent acquisition, onboarding, learning and mobility is also positive. It’s about time that we have rich and personalised experiences at work similar to those we already enjoy as consumers. Data and analytics plays a foundational role in this.

2. People analytics is an area of profound interest to business leaders. What do you see as the main trends in the people analytics space?

You are right to highlight the heightened interest levels in people analytics Alexey. I’d summarise the main trends as follows:

  • More and more organisations getting started with people analytics – 2017 seems to have been the year that the talking about when to start analytics stopped and the actual hard work in creating capability began for many organisations. So, the number of organisations in the early stages of their people analytics journeys is on the increase and many will face similar challenges in terms of data quality, skills and capabilities, stakeholder management/education and project prioritisation. Our recent IBM Smarter Workforce Institute research on HR Analytics Readiness in Europe demonstrated though that most organisations still have a long way to go.
  • Developing an analytical culture: this is key for organisations that want to develop sustainable capability in people analytics. This means exciting, equipping and enabling HR Business Partners, and clearly demonstrating and communicating the impact of people analytics initiatives within the organisation. This is the focus of many companies that have built initial capability and success in people analytics.
  • Ethics and privacy concerns: this continues to be the most important and challenging aspect for practitioners. Research from Insight222 reveals that 81% of people analytics projects are jeopardised by ethical and privacy concerns. With the EU GDPR legislation coming into effect in May 2018 and the emergence of new employee data sources, focus on this area will continue to be high.
  • The consumerisation of HR – as per my earlier point, many organisations that have developed people analytics capability are looking at ways to understand and improve the employee experience. In addition to the personalised machine-learning based technologies referenced earlier, this includes efforts to understand and analyse employee sentiment. You can’t do either of these things without analytics so those organisations that have already developed people analytics capability are in pole position to take advantage here.
  • Organisational network analysis (ONA) – interest in ONA has exploded in 2017 as organisations seek to better understand team effectiveness and productivity. Practitioners interested in this burgeoning area of people analytics should check out the work of Rob Cross, recent articles by Josh Bersin and vendors like TrustSphere, Humanyze and Worklytics. Expect interest in this area to continue to soar in 2018.

3. On the eve of People Analytics Forum 2017 could you slightly open the curtain on what makes an ideal agenda in modern HR analytics, workforce planning and employees insights then?

I always enjoy chairing the Tucana People Analytics World and People Analytics Forum events as the agenda is always cognisant of the fact that the diversity of delegates in terms of where they are with analytics varies widely. As such, the three tracks: Start (for those getting started), Grow (for those building capability and looking for deeper insight) and Advance (for advanced practitioners and those exploring new data sources) means there is something for everyone. This is hugely important as in my experience the people analytics community is highly collaborative and there is a mutual desire amongst practitioners for shared learning. The Tucana events provide this in spades.

4. It was heard that some attendees of conferences recently formed a viewpoint that the slow adoption of analytics has been because of a lack of practical cases delivered by speakers. Your point of view on the problem will be of great influence.

I haven’t really heard this viewpoint from many. I would argue the contrary in fact that most of the conferences I attend feature numerous and diverse case studies from practitioners. I think you need a balance of speakers from the practitioner, consultant, vendor and analyst communities as each provides a slightly different perspective – indeed much of the innovation in the space is coming from the vendor community. As such, at the conferences I chair, speak and attaned there is normally much to inspire delegates whatever their maturity level when it comes to people analytics. Of course, there is a distinction between being inspired and immitation as each organisation faces different business challenges and has unique cultures. If I could offer one piece of advice to practitioners, whatever their maturity level, it is to channel their efforts on the key business challenges that have the biggest impact within their organisations.

5. What new data-driven HR solutions are on your watchlist and why?

As I mentioned before much of the innovation in the people analytics space is coming from the vendor community and I always recommend to practitioners to keep abreast of the latest developments here. Data-driven companies to look at include: TrustSphere, Alderbrooke Group, Aspirant, Glint, Visier, Crunchr, Workometry, Peakon, OrgVue, Headstart, Worklytics, Humanyze, Qlearsite, One Model, hiQ Labs, Cultivate and StarLinks; and those are just the ones I can remember off the top of my head!

If you’ll forvive the self-promotion, I would like to add that IBM is also doing some groundbreaking work in this space through bringing Watson to HR, particularly in the talent acquisition and the employee experience areas – see more here.

6. What advice would you give to HR professionals looking to boost their careers within the people analytics space?

Well, firstly you should get yourself along to the People Analytics Forum and read my articles on LinkedIn!

Seriously, analytics is a core capability for the future HR practitioner and it won’t be long before the likes of CIPD and SHRM build this into their educational programs. Until then, find some courses (like the Wharton School course on Coursera), attend some conferences, read some books (like The Power of People and the Basic Principles of People Analytics), and seek to learn from analytics professionals both in and outside of HR.

For me, HR is one of the most exciting places in business to work in at the moment and the increased use of analytics and data-driven decision making is one of the reasons why I believe this to be the case.

Advertisements
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

How to Protect HR from Ransomware | Featured Image

How to Protect HR from Ransomware

How to Protect HR from Ransomware | Main Image

Companies have HR departments that are responsible for storing confidential information such as an individual’s social security number, payroll information, health information as well as employment history.

Because of enormous amount of sensitive data collected on individuals, HR departments opt to store data in a digital format, thus, making it susceptible to cyber-threats. Furthermore, since HR departments receive more email that any other department in a company, they are even more vulnerable to such threats. One of the most challenging form of cyber-attacks that HR departments face today is ransomware.

Ransomware is a type of malware that encrypts data and restricts access to a computer system. Often malware is sent through an email in the disguise of a resume or cover letter. When the email is opened, then the malware infects the computer and the entire network. The next time a user tries to gain access to the computer system, he or she is required to pay a monetary ransom in the form of Bitcoin to remove the restriction. WannaCry is one commonly known name for the recent ransomware attack that affected many companies.

Ransomware not only steals an individual’s personal information, but it damages a company’s reputation and financial status as well. The good news is that there are steps that HR departments can take to prevent ransomware attacks.

Basic Security Measures

It is imperative that HR departments work closely with the IT department to implement strong web filters and spam controls as a basic security measure. Next, the IT department should have Endpoint analytical tools to immediately detect, quarantine and shut down ransomware invasions.

Finally, always have a working data backup plan that is not connected to the company’s network so data cannot be infected.

Latest Operating and Software System

The IT department should make sure that the company’s operating system and software is up-to-date. It is extremely important that security updates are installed on all machines as they are released to protect all computers on the network.

If the company uses Microsoft Office software, it is recommended that macros are turned off. In addition, remove plugins if using Adobe Flash, Adobe Reader, Java or Silverlight since these plugins can run a risk of having embedded malware attached to them upon installation.

Employee Training

It is essential for companies to train employees on their information security policies. Employees must understand that technology alone is not enough to protect sensitive data and that there are cybersecurity threats that can bombard them.

Employees need regular training sessions in learning how to use technology as well have an understanding that technology is not always foolproof. There should be employees training in the do’s and don’ts of data protection. Since HR employees receive numerous emails daily, they need to know what types of files are safe to open.

Finally, employees need to know how to respond, and to whom they should report a cyber threat if the unthinkable happens.

Network Segmentation and Separate Work Stations

The IT department needs to ensure that the company’s most sensitive data is not stored all on one network. This is done through network and database segmentation. A restriction should be in place where only certain authorized individuals can access sensitive information. For example, make one person the administrator for the system.

The administrator should only log into the system as absolutely deemed necessary and use a regular account for everyday use. Furthermore, the IT department should assign dedicated workstations to employees responsible for reviewing resumes and monitor workstation usage.

Outside Testing

To ensure the validity of the company’s security, it is a good idea to hire an outside firm to test the vulnerability of its IT security. By hiring an outside firm, the company can understand where hackers can possibly penetrate the system, and take necessary steps to make data more secure.

To conclude, HR departments have access to massive amounts of sensitive data and the employees are typically not very well educated in knowing how to protect themselves from data breaches. Therefore, they are an easy and lucrative target for hackers.

It is easy to see why HR departments are prone to such cyber-attacks. However, when the HR staff works more closely with the IT department, preventive steps can be taken to reduce ransomware attacks. Precautionary steps such as implementing basic security measures, installing the latest operating system and software, setting up network segmentations and dedicated workstations, training employees and having outside testing to check for security breaches can save a company’s reputation and financial status.

About the Author:

Josh McAllister

Josh McAllister is a freelance technology journalist with years of experience in the IT sector, and independent business consultant. He is passionate about helping small business owners understand how technology can save them time and money. 

Josh is a contributor of a number of digital outlets, and well published including DZone, IoT World News, and Rabid Office Monkey.


If you want to share this article the reference to Josh McAllister and The HR Tech Weekly® is obligatory.

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

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

10 Most Popular Articles of 2016 About HR, HR Tech, Recruitment and Beyond

Human Touch in Digital

The HR Tech Weekly® is happy to present you the list of 10 most popular articles in our blog in 2016. The entire rank is made based on the number of views and social shares. The competition was severe but fair. Some of our favorite article got behind. But there were only ten slots available.

We did not include in the list our own listicles and third parties ranks like Top 10 HR Tech Influencers on Twitter and some others despite of they were quite popular among the audience.

Some great articles from the beginning of the year did not compete well with those from the second half. We treat it as a technical error as the blog itself was less popular that time.

Nevertheless, after careful consideration and precise calculation we’ve got the following list of readers choices, and here we go:

  1. Recruiting Secrets LinkedIn Doesn’t Want You To Know, by Ninh Tran

Today, only pockets of the tech industry still enjoy significant growth and hiring volumes, for example, autonomous vehicles, augmented and virtual reality, artificial intelligence, and deep learning. To satisfy hiring teams, talent acquisition professionals must find better and more creative ways to reach premier talent and generate their interest for the right opportunity. Can LinkedIn be an excellent recruiting channel to connect the right people with the right roles?

  1. What you need to know about Agile Performance Management, by Yatin Pawar

Agile performance management is a collaborative, continuous feedback and development practice that is steadily replacing traditional performance management.

Traditional performance management has proven to be insufficient to assess and enhance an employee’s contribution. Its primary focus is setting up a series of processes to measure the employee’s performance over the whole year. These processes end up having an unanticipated effect of managers focusing on employee’s weaknesses.

  1. The Future of Recruiting and Hiring with AI, by Noel Webb

The buzz around artificial intelligence this year is being shrugged off by many as just a new word HR got ahold of, but what would happen if AI was actually embraced by the recruiting and hiring world? What could it do to further practices and solve problems?

  1. HR Tech Is So Dynamic and Still Has Very Much a Work in Progress, Q & A with Bill Kutik

As for being on the other side of the table… being a good interviewer means taking second chair to the person being interviewed. Teasing out and highlighting what they know. Since much of what I do are interviews and panels (except for my columns), I don’t get to do much of the talking. So I love whenever the roles are reversed!

  1. The HR function is in the middle of a process which will change it forever, by Marco Pastore

The New Way of Working (NWoW) is rising and the reasons behind this are in the latest trends in HR: Autonomy, Accountability, Flexibility and ICT.

Most companies are following or are planning to follow this trend, and for good reasons! But before speaking about the benefits, it is better to understand what this trends mean with some examples.

  1. What LinkedIn’s Buyout with Microsoft means for the Talent Acquisition Technology Ecosystem, by Brian Delle Donne

While critics point to Microsoft acquisition failures like Nokia and Yammer, neither one of those companies open up the ability for increased ad revenue, user interaction data or video conferencing abilities.

  1. Building a Culture of Confidence, by Lisa Feigen Dugal

Confidence and competence: Two invaluable characteristics to possess in today’s professional environment. While these traits have different meanings, they are inextricably linked. Consistent research findings show men tend to overestimate their competence while women underestimate it, yet research has also shown that women tend to be more effective, and more competent, leaders.

  1. 5 Reasons Why Big Data Analytics Degrees Are Worth It, by Lauren Willison

Due in large part to the rapid growth of science, technology, engineering and mathematics (STEM) fields, big data analytics is approaching new heights. Students who pursue a degree in big data analytics learn how to effectively analyze large sets of data and identify patterns, connections and other pertinent details revealed by data. Companies are increasingly turning to data analytics to harness customer insights, and ultimately, produce better business decisions. As a result, big data analysts are in high demand and the data analytics field is showing no signs of slowing down.

  1. Great Companies Are Built Around Great People, by Annie Jordan

There is a lot of truth in the saying that great companies are built upon great people. However, the reality is, of course, more complicated than that. The world’s leading companies are a powerful blend of people, vision, capability and culture. These things work together like the mechanics of a rocket, generating and maintaining irresistible momentum.

  1. How You Can Improve Customer Experience With Fast Data Analytics, by Ronald van Loon

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

Featured articles:

The 30 Most Influential People To Follow In The #hrtech World • Recruitee Blog

50+ Online Sources for HR Managers, Part 1

50+ Online Sources for HR Managers, Part 2

Top 10 Articles of 2015 in HR Tech, Recruitment, Startups and Around

The new exciting year is ahead and we are looking forward to serve you with the best content. We wish you useful and productive reading with us! Stay tuned and we’ll be back…


If you want to share this blog post the reference to Alexey Mitkin and The HR Tech Weekly® is obligatory.

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

Breaching The Big Data Barrier : Moving HR Towards Analytics

big-data

Investment in big data has risen in 2016. That’s according to tech consultants Gartner which reveals that 48% of companies invested in big data this year, an increase of 3% compared to 2015. Planned investment in the next two years is predicted to fall, however. The issue, according to Gartner, is not so much the data but how it is used. 85% of companies who invest in big data remain in the pilot stage as projects fail to progress beyond the initial commitment.

That is certainly the case for the UK which is ranked 14th in the world for digital adoption. As candidate availability falls and the digital skills shortage spirals towards a critical point, big data is HR’s path to navigating through the complex issues affecting the workforce. Breaching HR’s innate big data barrier to move towards analytics requires a clear strategy. Here’s how to achieve that:

Evaluate your current position : Understanding the maturity of your current recruitment process will provide a base from which to evaluate progress. Deloitte’s Global Human Capital Trends Survey 2016 found that one third (32%) of businesses felt ‘ready or somewhat ready’ for analytics while 8% believed they were ‘fully ready’ to develop a predictive model. Know your starting point.

Aim for quick wins : PwC’s 2016 Data and Analytics Survey reports that UK executives want more data driven decisions but are held back by their organisations and culture. Demonstrating the benefits with some quick wins will help to overcome internal resistance to big data. Aim to provide insight and solutions into ‘roadblocks’ within your hiring process. For example, a lengthy application process deters candidates from completing application forms, while recruitment analytics identifies the source of your best applicants. Begin with HR technology covering key hiring metric which extracts information from live data within your Excel spreadsheets. Getting the right data is the key, whether ‘big’ or ‘small’.

Collecting data : Most companies have a wealth of data available. Collecting, analysing and understanding that data is the biggest challenge. For example, most hiring teams have access to a wealth of information available from sources such as social media, in-house surveys and LinkedIn. That data provides a starting point and may include:

  • Performance management reviews.
  • Personal data, including medical history and employee attendance levels.
  • The hiring sources of your most successful people..
  • Employee participation in surveys and candidate referral schemes.

Utilising analytics : Big data helps to shape your understanding of the online habits of your talent pool, through tracking their digital footprints. It assists evaluation and targeting of job posts and facilitates engagement with people who possess the skills critical to your business. That information helps to create focused candidate personas in order to target future recruitment at relevant talent pools. Analytics evaluates the demographic profile of potential hires, coupled with their educational background, career history and typical salary. Advanced analytics can predict talented employees who may be a ‘flight risk’. When high risk people are identified, HR can adopt a more effective and aggressive retention strategy, focusing on areas such as career development, in-house training and flexible working.

Minimising bias : Data helps to reduce ‘confirmation bias’, broadly defined as a pre-existing belief we may hold which we look for evidence to support. In hiring, this may present itself in repeatedly recruiting applicants from the same social or education backgrounds. The Social Mobility Commission’s newly released State Of The Nation Report 2016 reveals the extent of the problem in the UK, noting that only 4% of doctors, 6% of barristers and 11% of journalists are from working-class backgrounds. Confirmation bias leads HR to eliminate talent from interview selection. Hiring algorithms in big data help to prevent that. As a prime example, Google’s re:Work platform operates on the principle of ‘unbiasing’, which it states begins with ‘education, accountability, measurement and more’.

Don’t over-invest : Big data must work for your business. Scalable HR technology enables your business to expand as employers analyse and interpret the data available. Recruitment software without integrated analytics that provide live and instant data will hinder, not help your hiring process. It should also be mobile friendly and equipped with social collaboration tools.

Ensure ethical use of data : Confidentiality and privacy must be a priority for employers collecting data on candidates which includes personal information. The UK government has accepted a recommendation to create a council of data science ethics to address concerns over the misuse of big data. Establish ground rules for the use of talent analytics within your business to ensure compliance. Choose technology that complies with the Data Protection Act and offers a full audit trail.

Treat big data as your ally

Big data is here to stay. The Economist Intelligence Unit reports that, while cyber-security and web/mobile development are the highest ranked competencies today, big data will replace them by 2018.

Big data is HR’s ally. Utilised effectively it augments recruitment and selection decisions by providing objective data that highlights disruptive elements in the hiring process. No data is perfect but it provides an indication of activity and progress in your talent management strategy. Create a story and positive message around your technology to empower HR. It isn’t about statistics. It’s about enabling your business to create stronger talent pools, and a more robust hiring process.

HR must develop familiarity with and insight into data to communicate its benefits confidently and ensure that it aligns with performance objectives. Adopting a predictive talent model is your goal but breaching that big data barrier is the first step.

Advorto‘s recruitment software provides workflow and structure across the entire hiring process, offering a dynamic database of candidates and analytics. Used by some of the world’s leading organisations, it provides a straightforward first step into HR analytics and big data.


If you want to share this article the reference to Kate Smedley and The HR Tech Weekly® is obligatory.

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

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

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

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

Great Leadership Drives Big Data

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

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

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

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

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

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

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

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

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

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

CEO at O2MC I/O Prescriptive Computing

 

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

Co-author, Director at Adversitement

 

 


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