Find the Best Jobs in Technology & Data

Advance your career! Find real jobs posted by real employers on our award-winning platform.

Job Seekers

Post your resume and get noticed
by industry-leading companies.
Add Resume

Employers

Advertise your job to reach the
best talent in Techology and Data.
Post a Job

Latest Insights

Human Resources feels pressure to begin using predictive analytics Today’s business executives are increasingly applying pressure to their Human Resources departments to “use predictive analytics”. This pressure isn’t unique to Human Resources as these same business leaders are similarly pressuring Sales, Customer Service, IT, Finance and every other line of business (LOB) leader, to do something predictive or analytical. Every line of business (LOB) is clear on their focus. They need to uncover predictive analytics projects that somehow affect their bottom line. (Increase sales, increase customer service, decrease mistakes, increase calls per day and the like). Human Resources departments have a different, and somewhat unique, challenge not faced by most other lines of business When Human Resources analysts begin a predictive analytics initiative, what we see mirrors what every other line of business does. Somehow for HR, instead of having a great outcome it can be potentially devastating. Unless the unique challenge HR faces is understood, it can trip up an HR organization for a long time, cause them to lose analytics project resources and funding, and continue to perplex HR as they have no idea how they missed the goal of the predictive initiative so badly. Human Resources’ traditional approach to predictive projects Talent Analytics’ experience has been that (like all other lines of business) when Human Resources focuses on predictive analytics projects, they look around for interesting HR problems to solve; that is, problems inside of the Human Resources departments. They’d like to know if employee engagement predicts anything, or if they can use predictive work somehow with their diversity challenges, or predict a flight risk score that is tied to how much training or promotions someone has, or see if the kind of onboarding someone has relates to how long they last in a role. Though these projects have tentative ties to other lines of business, these projects are driven from an HR need or curiosity. HR (and everyone else) needs to avoid the “Wikipedia Approach” to predictive analytics Our firm is often asked if we can “explore the data in the HR systems” to see if we can find anything useful. We recommend avoiding this approach as it is exactly the same as beginning to read Wikipedia from the beginning (like a book) hoping to find something useful. When exploring HR data (or any data) without a question, what you’ll find are factoids that will be “interesting but not actionable”. They will make people say “really, I never knew that”, but nothing will result. You’ll pay an external consultant a lot of money to do this, or have a precious internal resource do this – only to gain little value without any strategic impact. Avoid using the Wikipedia Approach – at least at first. Start with a question to solve. Don’t start with a dataset. Human Resources predictive project results are often met with little enthusiasm Like all other Lines of Business, HR is excited to show results of their HR focused predictive projects. The important disconnect. HR shows results that are meaningful to HR only. Perhaps there is a prediction that ties number of training classes to attrition, or correlates performance review ratings with how long someone would last in their role. This is interesting information to HR but not to the business. Here’s what’s going on. Business outcomes matter to the business. HR outcomes don’t. Human Resources departments can learn from the Marketing Department who came before them on the predictive analytics journey. Today’s Marketing Departments, that are using predictive analytics successfully, are arguably one of the strongest and most strategic departments of the entire company. Today’s Marketing leaders predict customers who will generate the most revenue (have high customer lifetime value). Marketing Departments did not gain any traction with predictive analytics when they were predicting how many prospects would “click”. They needed to predict how many customers would buy . Early predictive efforts in the Marketing Department used predictive analytics to predict how many webinars they’ll need to conduct to get 1,000 new prospects in their prospect database. Or, how much they’d need to spend on marketing campaigns to get prospects to click on a coupon. (Adding new prospect names to a prospect database is a marketing goal not a business goal. Clicking on a coupon is a marketing goal not a business goal). Or, they could predict that customer engagement would go up if they gave a discount on a Friday (again, this is a marketing goal not a business goal. The business doesn’t care about any of these “middle measures” unless they can be proved and tracked to the end business outcome. Marketing cracked the code Business wants to reliably predict how many people would buy (not click) using this coupon vs. that one. When marketing predicted real business outcomes, resources, visibility and funding quickly became available. When Marketing was able to show a predictive project that could identify what offer to make so that a customer bought and sales went up - business executives took notice. They took such close notice that they highlighted what Marketing was able to do, they gave Marketing more resources and funding and visibility. Important careers were made out of marketing folks who were / are part of strategic predictive analytics projects that delivered real revenue and / or real cost savings to the business's bottom line. Marketing stopped being “aligned” with the business, Marketing was the business. Human Resources needs to do the same thing. Best approach for successful and noteworthy predictive workforce projects Many people get tangled up in definitions. Is it people analytics, workforce analytics, talent analytics or something else? It doesn’t matter what you call it - the point is that predictive workforce projects need to address and predict business outcomes not HR outcomes. Like Marketing learned over time, when Human Resources begins predictive analytics projects, they need to approach the business units they support and ask them what kinds of challenges they are having that might be affected by the workforce. There are two critical categories for strategic predictive workforce projects: Measurably reducing employee turnover/attrition in a certain department or role Measurably increasing specific employee performance (real performance not performance review scores) in one role or department or another (i.e. more sales, less mistakes, higher customer service scores, less accidents). I say “measurably” because to be credible, the predictive workforce initiative needs to measure and show business results both before and after the predictive model. For greatest ROI: Businesses must predict performance or flight risk pre-hire Once an employee is hired, the business begins pouring significant cost into the employee typically made up of a) their salary and benefits b) training time while they ramp up to speed and deliver little to no value. Our analytics work measuring true replacement costs show us that even for very entry level roles a conservative replacement estimate for a single employee (Call Center Rep, Bank Teller and the like) will be over $6,000. A great example, is to consider the credit industry. Imagine them extending credit to someone for a mortgage – and then applying analytics after the mortgage has been extended to predict which mortgage holders are a good credit risk. It’s preposterous. They only thing the creditor can do after the relationship has begun is to try to coach, train, encourage, change the payment plan and the like. It’s too late after the relationship has begun. Predicting credit risk (who will pay their bills) – is predicting human behavior. Predicting who will make their sales quota, who will make happy customers, who will make mistakes, who will drive their truck efficiently – also is predicting human behavior. HR needs to realize that predicting human behavior is a mature domain with decades of experience and time to hone approaches, algorithms and sensitivity to private data. What is Human Resources’ role in predictive analytics projects? The great news is that typically the Human Resources Department will already be aware of both of these business challenges. They just hadn’t considered that Human Resources could be a part of helping to solve these challenges using predictive analytics. Many articles discuss how Human Resources needs to be an analytics culture, and that all Human Resources employees need to learn analytics. Though I appreciate the realization that analytics is here to stay, Human Resources of all people should know that there are some people with the natural mindset to “get” and love analytics and there are some that don’t and won’t. As I speak around the world and talk to folks in HR, I can feel the fear felt today by people in HR who have little interest in this space. My recommendation would be to breathe, take a step back and realize that not everyone needs to know how to perform predictive analytics. Realize there are many traditional HR functions that need to be accomplished. We recommend a best practice approach of identifying who does have the mindset and interest in the analytics space and let them partner with someone who is a true predictive analyst. For those who know they are not cut out to be the person doing the predictive analytics there are still many roles where they can be incredibly useful in the predictive process. Perhaps they could identify problem areas that predictive analytics can solve, or perhaps they could be the person doing more of the traditional Human Resources work. I find this “analytics fear” paralyzes and demoralizes employees and people in general. Loosely identified, but important roles on a predictive workforce analytics project Someone to identify high turnover roles in the lines of business, or identify where there are lot of employees not performing very in their jobs A liaison: Someone to introduce the HR predictive analytics team to the lines of business with turnover or business performance challenges Someone to help find and access the data to support the predictive project Someone to actually "do" the predictive analytics work (the workforce analyst or data scientist) Someone who creates a final business report to those the results of the work (both positive and negative) Someone who presents the final business report A high level project manager to help keep the project moving along The business and HR experts that understand how things work and need to be consulted all along the way These roles can sometimes all be the same person, and sometimes they can be many different people depending on the complexity of the project, the size of the predictive workforce organization, the number of lines of business that are involved in the project and / or the multiple areas where data needs to be accessed. The important thing to realize is there are several non analytics roles inside of predictive projects. Not every role in a predictive project requires a predictive specialist or even an analytics savvy person. High-value predictive projects don’t deliver HR answers We recommend, no. At least not to begin with. We started by describing how business leaders are pressuring Human Resources to do predictive analytics projects. There is often little or no guidance given to HR about what predictive projects to do. Here is my prediction and you can take it to the bank. I’ve seen it happen over and over again. When HR departments use predictive analytics to solve real, Line of Business challenges that are driven by the workforce, HR becomes an instant hero. These Human Resources Departments are given more resources, their projects are funded, they receive more headcount for their analytics projects - and like Marketing, they will turn into one of the most strategic departments of the entire company. Article written by Greta Roberts Image credit by Getty Images, Oxford Want more? For Job Seekers | For Employers | For Influencers
Let's take a look at the top strategic Internet of Things (IoT) technology trends that Gartner, Inc. recently identified as driving digital business innovation from 2018 through 2023. “The IoT will continue to deliver new opportunities for digital business innovation for the next decade, many of which will be enabled by new or improved technologies,” said Nick Jones, research vice president at Gartner. “CIOs who master innovative IoT trends have the opportunity to lead digital innovation in their business.” In addition, CIOs should ensure they have the necessary skills and partners to support key emerging IoT trends and technologies, as, by 2023, the average CIO will be responsible for more than three times as many endpoints as this year. Analysts discussed how CIOs can lead their businesses to discover IoT opportunities and make IoT projects a success during Gartner Symposium/ITxpo. They shortlisted the 10 most strategic IoT technologies and trends that will enable new revenue streams and business models, as well as new experiences and relationships: Trend No. 1: Artificial Intelligence (AI) It's forecasted that 14.2 billion connected things will be in use in 2019, and that the total will reach 25 billion by 2021, producing immense volume of data. “Data is the fuel that powers the IoT and the organization’s ability to derive meaning from it will define their long term success,” said Jones. “AI will be applied to a wide range of IoT information, including video, still images, speech, network traffic activity, and sensor data.” The technology landscape for AI is complex and will remain so through 2023, with many IT vendors investing heavily in AI, variants of AI coexisting, and new AI-based tolls and services emerging. Despite this complexity, it will be possible to achieve good results with AI in a wide range of IoT situations. As a result, CIOs must build an organization with the tools and skills to exploit AI in their IoT strategy. Trend No. 2: Social, Legal, and Ethical IoT As the IoT matures and becomes more widely deployed, a wide range of social, legal, and ethical issues will grow in importance. These include ownership of data and the deductions made from it; algorithmic bias; privacy; and compliance with regulations such as the General Data Protection Regulation. “Successful deployment of an IoT solution demands that it’s not just technically effective but also socially acceptable,” said Jones. “CIOs must, therefore, educate themselves and their staff in this area, and consider forming groups, such as ethics councils, to review corporate strategy. CIOs should also consider having key algorithms and AI systems reviewed by external consultancies to identify potential bias.” Trend No. 3: Infonomics and Data Broking Last year’s Gartner survey of IoT projects showed 35 percent of respondents were selling or planning to sell data collected by their products and services. The theory of infonomics takes this monetization of data further by seeing it as a strategic business asset to be recorded in the company accounts. By 2023, the buying and selling of IoT data will become an essential part of many IoT systems. CIOs must educate their organizations on the risks and opportunities related to data broking in order to set the IT policies required in this area and to advise other parts of the organization. Trend No. 4: The Shift from Intelligent Edge to Intelligent Mesh The shift from centralized and cloud to edge architectures is well under way in the IoT space. However, this is not the end point because the neat set of layers associated with edge architecture will evolve to a more unstructured architecture comprising of a wide range of “things” and services connected in a dynamic mesh. These mesh architectures will enable more flexible, intelligent and responsive IoT systems — although often at the cost of additional complexities. CIOs must prepare for mesh architectures’ impact on IT infrastructure, skills, and sourcing. Trend No. 5: IoT Governance As the IoT continues to expand, the need for a governance framework that ensures appropriate behavior in the creation, storage, use and deletion of information related to IoT projects will become increasingly important. Governance ranges from simple technical tasks such as device audits and firmware updates to more complex issues such as the control of devices and the usage of the information they generate. CIOs must take on the role of educating their organizations on governance issues and in some cases invest in staff and technologies to tackle governance. Trend No. 6: Sensor Innovation The sensor market will evolve continuously through 2023. New sensors will enable a wider range of situations and events to be detected, current sensors will fall in price to become more affordable or will be packaged in new ways to support new applications, and new algorithms will emerge to deduce more information from current sensor technologies. CIOs should ensure their teams are monitoring sensor innovations to identify those that might assist new opportunities and business innovation. Trend No. 7: Trusted Hardware and Operating System Surveys invariably show that security is the most significant area of technical concern for organizations deploying IoT systems. This is because organizations often don’t have control over the source and nature of the software and hardware being utilised in IoT initiatives. “However, by 2023, we expect to see the deployment of hardware and software combinations that together create more trustworthy and secure IoT systems,” said Jones. “We advise CIOs to collaborate with chief information security officers to ensure the right staff are involved in reviewing any decisions that involve purchasing IoT devices and embedded operating systems.” Trend 8: Novel IoT User Experiences The IoT user experience (UX) covers a wide range of technologies and design techniques. It will be driven by four factors: new sensors, new algorithms, new experience architectures and context, and socially aware experiences. With an increasing number of interactions occurring with things that don’t have screens and keyboards, organizations’ UX designers will be required to use new technologies and adopt new perspectives if they want to create a superior UX that reduces friction, locks in users, and encourages usage and retention. Trend No. 9: Silicon Chip Innovation “Currently, most IoT endpoint devices use conventional processor chips, with low-power ARM architectures being particularly popular. However, traditional instruction sets and memory architectures aren’t well-suited to all the tasks that endpoints need to perform,” said Jones. “For example, the performance of deep neural networks (DNNs) is often limited by memory bandwidth, rather than processing power.” By 2023, it’s expected that new special-purpose chips will reduce the power consumption required to run a DNN, enabling new edge architectures and embedded DNN functions in low-power IoT endpoints. This will support new capabilities such as data analytics integrated with sensors, and speech recognition included in low cost battery-powered devices. CIOs are advised to take note of this trend as silicon chips enabling functions such as embedded AI will in turn enable organizations to create highly innovative products and services. Trend No. 10: New Wireless Networking Technologies for IoT IoT networking involves balancing a set of competing requirements, such as endpoint cost, power consumption, bandwidth, latency, connection density, operating cost, quality of service, and range. No single networking technology optimizes all of these, and new IoT networking technologies will provide CIOs with additional choice and flexibility. In particular, they should explore 5G, the forthcoming generation of low earth orbit satellites, and backscatter networks. Article published by Anna Hill Image credit by Getty Images, DigitalVision Vectors, miakievy Want more? For Job Seekers | For Employers | For Influencers
We have seen a great deal of buzz surrounding HR analytics in the last couple of years. There have been publications across the globe covering lessons learned in HR analytics initiatives. After reading much of this content, a few observations stand out. First, getting from analyzing people data to achieving tangible results for your organization is a difficult thing to achieve. There are many initiatives in HR analytics, but when it comes to specific examples of results of this new business intelligence form, the same cases in point keep appearing. Especially, Google and its outstanding work that’s ahead of the rest. Second, highly intelligent, analytical people are active in this field of sport. But when it comes to being business savvy and adding real dollar value to your organization, the pool narrows noticeably. HR analytics are a means to an end, not the other way around. If you are passionate about data science and less about running a business, a career in science may be a better match. When reading about others’ experiences in this new field, a few basic pitfalls keep coming up. And these pitfalls I recognize from my own experience working on this subject. So here’s a look at the 5 most common mistakes when starting with HR analytics: 1. Putting the Cart before the Horse One of the first questions to ask yourself before beginning any HR analytics project is ‘why’? What is the goal of your efforts? How will your organization profit? Frequently, HR departments start working on HR analytics without a clear vision on the subject. There is so much hype that HR directors are afraid they’ll be left behind if they don’t start doing something analytical soon. In too many cases, working on analytics becomes a goal in itself. Often, new software, complex statistical techniques or new HR technology is the starting point for analytical ambitions, which is putting the cart before the horse. Business questions should always be leading, not technology. HR analytics only adds value if you can tackle a real, specific business problem. Don’t start your analytics journey working on a flashy analytics dashboard that no one needs. Work on something that will lead to real cost savings, or otherwise, better performance. Only then will you earn the trust of business leaders. 2. Ignoring Legal Risks This is simple. If your HR analytics project involves personal data about employees, serious risks concerning people’s privacy comes into play.There are rules and laws you need to comply to. You have to think of the legal consequences of what you are doing. You need to involve your colleagues from legal. Yes, this might take time and delay your initiative, but making mistakes in this area is lethal. Employees need to feel that their data is safe and handled with integrity. If you lose the trust of your colleagues, you will have a very challenging time earning it back. So get the information you need from legal, then collect and analyze data the right way without breaking any laws. 3. Lacking Balance in Your Team Meeting an expert data scientist that understands the role of HR and also convinces your CEO is a rare thing. HR analytics is a complicated field. To be successful, you need multiple types of people with varying skill sets in your talent analytics team. You need people who understand and are skilled in HR, IT, and data science. But you will also need people who understand the business side of things to bridge the traditional gap between HR and business. You can only be effective in HR analytics if you can make this assortment of people work together cohesively. This is tricky because everybody thinks their background or skills should be leading the way. In reality, you need a range of skills and ways of thinking during each phase of your project. You need razor sharp focus to keep moving ahead and not get stuck in the complex details. 4. Failing to Look Beyond the Borders of the HR Department If you are an HR enthusiast, you will be eager about the new possibilities HR analytics have to offer. For the rest of the world, your cherished HR analytics project is just another business intelligence pilot. Chances are, there have been more than 20 new projects across your organization involving data in this last year alone. Big data is red hot and not only in HR. So don’t expect your colleagues to be as over the moon as you are. Usually, I see HR professionals working on HR challenges nobody else really cares about. To get the attention of colleagues beyond the borders of HR, you have to address distinct business issues. You have to tell a convincing story about you adding value that everybody can directly understand. So always start with a real-life business concern that keeps your colleague’s up at night before you start thinking about an HR analytics approach. 5. Managing Expectations It is better to start small. You already believe in the huge potential of this thrilling new field of sports. Your colleagues from Finance or IT have yet to be convinced about the value of HR analytics. Don’t make the mistake to overpromise on your expected outcomes. HR analytics will not lead to millions of cost reductions in the first few months. In HR analytics, progress comes slowly. It is a complex and time-consuming ordeal. HR analytics project always take longer than you think, and the outcomes are not as overwhelmingly clear as you hope. So stay humble and keep a low profile. Keep your focus and work hard. Once the analytics train start to build up speed and you can show your first real results, you can start to make more noise. The Future of HR Analytics So where is this exciting new approach in HR taking us? Are HR analytics just a hype or more of the same? Considering everything that is written about this subject, I believe the real value of HR analytics is not so much in making HR more efficient or less costly. Reducing costs or risks is not the way to stand out in today’s modern age of business. Hiring and retaining the very best talent for key positions, having a highly engaged workforce, working on innovating in high-performance teams, that’s what the future holds. HR analytics gives an opportunity to finally understand the drivers behind high performance, motivation, and innovation. So I believe HR analytics should focus on improving the quality of human capital and less on cost efficiency. Article written by David Verhagen Image credit by Getty Images, DigitalVision Vectors, miakievy Want more? For Job Seekers | For Employers | For Influencers
View All Insights