Debbie Ung, Senior Vice President of GP Strategies Corporation’s Performance Readiness Group, talks about the group’s service offerings.

I am sure many of the readers of this blog have enjoyed world-class gourmet dining at a fine restaurant somewhere in the world. If it was a five star Michelin-rated restaurant, the experience was probably flawless, with no indication of what it took behind the scenes to deliver that level of excellence. Maître Ds, hostesses, sommeliers, servers, bus boys, coat check, etc.—all front-office, customer-facing interfaces integrated seamlessly. But if you were to peek into the kitchen or back-office activities, you might be shocked; you might even get up and leave. USA Today had a recent article that stated: "Buzzwords don’t come any bigger than ‘Big Data,’ which promises to reveal the secrets hidden within big blocks of data held by companies, governments, and musty old archives." Bersin and Associates recently released a research report, "Big Data in HR." In this Bersin Report, "Big Data" refers to the use of advanced analytics tools and programs to look at vast amounts of employee, customer, and transaction data. In the case of L&D, this equates to huge amounts of people-related and learning program and initiatives-related data that can be used to better understand the organization’s current composition, performance levels, and risk. The Promise of Big Data for L&D reminds me sometimes of being in the messy kitchen; lots and lots of information chaos, all scattered, out of context, waiting to be selected/extracted, cooked to perfection by experts, and arranged and presented in a manner that drives high customer satisfaction. But most L&D organizations are not looking to unlock the meaning of life; they want information that is actionable to improve business results that are impacted by learning and knowledge transfer. Our kitchens are typically not that cluttered, so let’s not make it more complicated than it needs to be. Companies like Amazon, Netflix, Wal-Mart, Facebook, and Google, have been analyzing consumer behavior for years. They have evolved and matured from data analysis to data science. The skill sets and disciplines needed for these new analytic "kitchen chefs" include statistics, database design, math, industrial psychology, and technologies. In the learning field, these combined skills sets are neither abundant, nor typically found in the L&D organization. Large technology companies, like Microsoft and Oracle, have successfully sourced some of these skill sets into their learning functions because of the nature of their businesses. But L&D groups, as a whole, are struggling to execute on basic scorecard metrics and reporting, let alone sophisticated learning analytics culled from Big Data. Bersin typically presents in their research the concept of a Maturity Model to help organizations evolve within environments fraught with complexity and risk, but where the potential for high reward in world-class execution is evident. Such is the case for Talent Analytics. The following table presents the Bersin & Associates Talent Analytics Maturity Model developed after more than seven years of research working with hundreds of HR and L&D organizations. Source: Bersin and Associates, 2012. Used with Permission. Their research shows that, in order to build a scalable, reliable analytics strategy, you must evolve through these four levels. Most organizations I am involved with today are hovering between Levels 1 and 2, dipping their toe in the water at level 3 and fantasizing about reaching level 4. To go from reactive to predictive is not easy, especially in light of the constant churn most organizations have in their personnel. It’s tough to execute on a five-year analytics strategy that needs to be scalable, actionable, and reliable. So like running restaurants, its best to start small. Think of the lemonade stand you set up as a kid with your siblings or friends as a starting point, not Thomas Keller’s world class, superb art of precise culinary execution found at "Per Se" in NYC. As stated in the research, it’s an area you need to invest in directly. Not rely only on external expertise. Most importantly, it takes time, maybe up to five years before you are good at it. So get in the kitchen, start washing dishes, and prepping the food. If you are disciplined and focused, you might be promoted to Sous Chef. But realize that it takes years to become a Master Chef, and you definitely want to keep the customers out of the kitchen in the process—could be messy and scary! I’ll be conducting an in-depth video interview with Bob Danna, COO of Bersin and Associates, on the Bersin Talent Analytics Maturity Model for my next posting.

Scott Greenberg, Chief Executive Officer at GP Strategies Corporation (GPX), discusses his career and personal interests.

Doug Sharp, President at GP Strategies Corporation, reflects on his career with the company.

Sharon Esposito-Mayer, Chief Financial Officer at GP Strategies Corporation, reflects on the companies growth during her 17 year tenure.

Over the past 18 months, the Cloud has done a number of things to the talent management marketplace. It has gained significant interest among CLOs and the like; it has reduced operating costs in some ways and increased them in others; and quite frankly, the Cloud has confused most talent management system consumers more than Web 2.0 did 10 years ago. Up until now, the decision to go to Cloud (SaaS) was considered an option for organizations looking to leverage the latest emerging technology in the marketplace. Simply put, if you didn’t want to make the transition, you didn’t have to. You could continue using your current Talent Management System (TMS) behind the firewall and paying for upgrades and additional integrations as you needed them. Unfortunately, those days are coming to an end. Software development is expensive. Enterprise software development is even more expensive. And it should be no surprise that maintaining and supporting two separate platforms (on premise and SaaS) begins to eat into operating margins. As a result, TMS vendors must make a decision to support both or one versus the other. As Cloud has gained transaction in the marketplace and consumer buy-in has increased, it only makes sense (for these TMS vendors) to go "all-in" behind the SaaS approach. The SaaS business model is fairly straightforward: Support one major code base with quarterly releases of new functionality and bug fixes. Instead of upgrading major versions every 12-24 months, SaaS vendors deploy updates to the product every 3-6 months. While consumers have the ability to "turn off" these updates, this topic is still widely debated as it affects testing strategies and governance models. So, what are your options if you cannot (or refuse to) transition to SaaS? Option 1 - Continue to operate your non-SaaS system as long at it meets your business and training requirements. At some point, you will be required to pay for additional support should you need it from the TMS vendor (or integrator). Don’t be afraid to ask your vendor what those support costs may look like in the future. Chances are it may be less than the transition cost to SaaS if you’re not using all functionality within your system. Option 2 - Transition to the SaaS model of your current TMS. Your TMS vendor will be more than happy to provide you pricing for the transition. However, it’s important to have a transition plan to ensure you have all Cloud aspects covered. This is step is often left out during the contract negotiations and shouldn’t be ignored. Also, ensure you’ve completed your due diligence and spoken to other consumers that have migrated from on premise to SaaS. Option 3 - Migrate to another enterprise TMS that plans on supporting on-premise deployments for the foreseeable future. This is likely a last resort and should only be considered if you had another vendor highly ranked when you procured your current system. I have spoken to many customers who also considered open source as an alternative under this option. Option 4 - Go back to using Microsoft Excel to track and deliver training. I joke when I use this as an alternative, but I have actually had potential clients consider it—believe it or not. If you’re currently looking at migrating to a new TMS, make sure to address this issue in the procurement process and get the vendor’s commitment to an on-premise system in writing before you sign the contract. What obstacles have you faced as you consider on premise no longer a viable technology option for your organization?

Don Duquette, Executive Vice President at GP Strategies Corporation (GPX) discusses the competitive advantages of GP’s Learning Solutions.

Scott Greenberg, Chief Executive Officer at GP Strategies Corporation (GPX), and Sharon Esposito-Mayer, Chief Financial Officer, discuss the history of the company including strategic acquisitions and financial performance.

The notion of learning styles is interesting: that we can identify different ways that people learn and, through this understanding, design materials and experiences that best suit each of these styles. It’s appealing: an engineering approach that tells us we can fine tune performance, engagement and retention by better understanding the learning process. It recognises the difference of the individual and celebrates that. But it’s contentious. Evidence both for and against the existence of actual ‘styles’ is varied and, even should they exist, there is no clear consensus of what we should do with this knowledge. It’s an area that I venture into with caution as feelings run high on all sides, so in this case I simply posted the question on my learning forum: ‘does an understanding of learning styles make you better able to design learning’. Having lit the blue touch paper, I retreated and watched as the results and opinions flooded in. The results to the survey were reasonably close run: the vote for ‘no’ led for a long time, but eventually the ‘yes’ contingent drew out a lead. The final result was this: 60% of people agreed with the statement that an understanding of learning styles makes you better able to design learning, whilst 40% disagreed. So a victory, but a surprising amount of dissent and, for the record, I voted ‘no’. Many people were kind enough to leave long and very well evidenced supporting comments, a selection of which and commentary on are covered in the full post. I started the debate by admitting that, whilst the academic study of learning styles is interesting, I really struggle to find a practical application. For me, much of the discussion is overly complex or a story badly told. I wanted to know how people translated this knowledge themselves to design better learning. This post was originally published on Julian Stodd’s Learning Blog.

Doug Sharp, President at GP Strategies Corporation (GPX), relates his personal life to his work at the company.