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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
A shrinking pool of qualified candidates surfaced as a top business risk for global executives in risk, audit, finance, and compliance, according to a recent survey by Gartner, Inc. In a time of historically low unemployment where the supply of available workers is much lower, organizations are struggling to find and retain the talent they need to meet their strategic objectives. At No. 3, behind accelerating privacy regulation and cloud computing, this is the first time talent shortage was named a top business risk in Gartner’s quarterly Emerging Risks Report . Cloud computing , which was ranked the No. 1 risk in 2Q18, remains a concern. Cybersecurity disclosure and the artificial intelligence (AI)/robotics skills gap round out the top five concerns among executives surveyed. “In this strong economic environment of significant business growth and record-low unemployment levels, the battle for talent is heating up as employees now have more bargaining power,” said Matthew Shinkman, practice leader at Gartner. “As a result, talent is harder to find and even more difficult to keep.” In the U.S. alone, the number of unfilled jobs rose by 117,000 to 6.94 million from June to July 2018, based on the most recent Job Openings and Labor Turnover Survey. And in the U.K., the unemployment rate is now at its lowest level in four decades, according to the Office of National Statistics.  As business leaders feel the squeeze, the pressure on recruiters continues to intensify. In a Gartner survey of 400 executives on their level of satisfaction with their organization’s ability to attract and retain high-performing talent in the current environment, only 26 percent reported being very satisfied or satisfied. Digital transformation initiatives have only increased this pressure by creating immense competition for workers who are skilled at navigating the increasingly digital environment. Moving From Needs-Driven to Market-Driven Sourcing To mitigate the risk of talent shortage as competition for workers continues to rise, leading organizations are shifting how they source talent. Most recruiting professionals have a needs-driven approach to finding talent, setting the sourcing strategy to fulfill the defined needs of the organization. Instead, Gartner recommends a market-driven approach that ensures the sourcing strategy adapts to evolving external labor market realities and organization needs. This approach includes the following hallmarks: 1. Confront brand weaknesses. Recognize (mis)perceptions that limit access to talent pools, and actively address them. 2. Coach prospects’ career decisions. Understand prospective candidates’ decision-making process, and act as a career coach. 3. Expand the labor market opportunity. Optimize the search criteria to redefine and expand the available talent pools. 4. Cultivate critical talent supply. Reorient learning and development to close skill gaps that exist because of digital transformation. “In today’s tight labor market, where employees have the upper hand, workers are more willing to look for a job with better pay, more generous benefits, and defined career development opportunities — or all three,” says Brian Kropp, Gartner HR practice group vice president. “To retain your best employees, companies need to better understand what matters most to them and help them see how they can advance in their careers with their current company, especially if wage growth continues to remain stagnate,” says Kropp. Article published by Anna Hill Image credit by Getty Images, Westend61 Want more? For Job Seekers | For Employers | For Influencer
In banks and financial institutions, there is a typical barrier that exists between Finance and Risk. The Finance world is very “exact”. A balance sheet or a Profit and Loss (P&L) statement have to be very precise. In the Risk Management world, things are slightly different. It is common that numbers are created as a result from statistical analysis or stochastic simulations; recipes vary in order to create those numbers. It is indeed fascinating. Balance sheet forecasting is a technique being used by Finance professionals since a long time ago; although most of the time, the forecasting models being performed are fairly simple. Perhaps by applying a more analytic approach to the balance sheet and other Finance artefacts, such P&L or income statements could provide us with some leverage. Assets, liabilities and equity can be broken down into small categories and end up with dozens of items (or factors for the purpose of our discussion). Now, let’s imagine we have access to the general ledger system of a bank (or any business), and that we could extract a time series of those factors in the balance sheet. We could simulate future balances for each one of those factors and add them up as a portfolio, which will be seen as projected upcoming balance sheets. If we do this process for thousands of times and multiple time steps, we could create a distribution and apply a confidence level (e.g. 99%) so a worst case scenario could be forecasted on the balance sheet. Since we have the balance sheet time series we got from the GL system, we could also calculate volatilities of each one of the factors and compute correlations amongst them. A similar approach could be applied to income statement or P&L data, as well. By treating the company’s finances with risk analytics techniques, potential benefits could include: Obtaining a quick and dirty forecast on capital (i.e. equity) and losses Devising a direction of your business Observing potential cash-flow (i.e. liquidity) gaps Analysing worst-case scenarios for P&L Screening on potential M&A opportunities (i.e. valuation) Having a sneak preview of future tax impact Identifying areas to be addressed on the different factors to avoid future problems In a bank, the data from the GL most likely would reflect balances on different types of retail products (e.g. home loans, credit cards, deposits, personal loans, etc.). The Monte Carlo Simulation discussed above could be applied to each one of those product types and forecast a potential state by product type. This becomes very powerful for a decision-making process and to learn possible growth on the different product types. Also, exercises on mix optimisation could be performed if this feature was available. On the not-so-uplifting side, a potential challenge could be seen in preserving inter-relationships across the balance sheet factors. In this regard, we could create functions as part of the model, in such way that a segregation of dependent and independent factors becomes available. Also, we need to be mindful that reconciliation of forecasts might be required at different levels of the GL hierarchy. As it can be seen, there are plenty of opportunities that could be targeted by using this approach. In the same fashion that we have explored benefits from a Finance perspective, a Marketing lens could also be applied, opening up other opportunities. Article written by Jaime Noda Want more? For Job Seekers | For Employers | For Influencers
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