Category: The results of learning

I just recently went through an excellent presentation about some workforce programs in an American fast food company and how they encourage their employee’s learning.  The company is huge, having higher sales revenue than the national products of some countries, operating about 40 000 restaurants worldwide.  I was impressed with the results achieved with careful planning of targets and the means, how to meet these.  Maybe there is some truth in the saying: “ Small opportunities are often the beginning of great achievements.”

Programs are designed inter alia to make improvements into attrition rates of the personnel, which is a big concern, typically for the operators in the fast food industry.  To understand the proportions of their challenges we have to understand the scope of their recruiting needs.  Their operations need about 1 million new employees to be recruited each year. It is understandable.

how much the company could benefit, if the personnel would stay longer with them.  The company wanted to create new perspectives for their personnel by designing desirable education-to-career pathways for adult learners as means in their learning programs. The company has tried to engage their employees by effective supporting of their adult learners when they on- and off-ramp between education and employment. The impact of these programs has been very good.   As the most important results they have found, that the employees choose to stay significantly longer in the company and that they find benefits in lower attrition rates.  The benefits of the impact of these programs can be verified by increased revenue of the restaurants and even ROI calculations confirm the positive financial effects of these programs.  Careful planning of the training content and respectful treatment of the personnel are not only means to an end, what could be lower attrition rates. The personnel can feel, that the company is concerned about the welfare of their workforce and this improves the atmosphere within the company and leads to improved results exceeding the naked ROI -results.  If you start looking for benefits this way, you might be surprised to find more benefits, than you were initially looking for.

Although this type of hospitability business requires big investments into tailored training programs, like in this case, it bears all the hallmarks of successful learning programs. These are in brief: Definition of learning targets and selecting the analysis methods to be used.  Measuring of learning results, gathering the learning data into databases, where the real time progress can be monitored.  After that you have to utilize the collected learning scores and to deliver insights to all relevant persons in the organization, who should be interested in this data.  The collected skills- and learning results data provide deep inside knowledge into the depths of the talent pool of your company. You can get information through a detailed analyzes framework for ROI calculations. Based on data availability in the company, different ROIs can be calculated for three important business outcomes at level one:

• ROI in the form of Increased Revenue
• ROI in the form of Reduced Costs
• ROI of Employees/Education Investment

As an example about the multilayered form of the benefits to be received, ROI in the form of Increased Productivity can be achieved by two independent level two items as follows:

• Maximized Productivity
• Realization of Indirect Benefits

Realization of Indirect Benefits can be further divided into four level three items:

• Employee Engagement
• Customer Satisfaction
• Brand Recognition
• Loyalty

From the above mentioned example you can see, that the benefits can be reported in a very detailed level. As it seems for me, this is done for serving benefits in their specific business.  They have reported in the presentation, that the biggest benefits they have received lie in the employees, who choose to stay significantly longer in the company and this leads to lower attrition rates. On top of these other critical measures, like an improved turnover and increased cost savings have been recorded. The same is valid for different learning courses and learning paths in other companies. This case, we have described, has been carried out in an elaborate way that requires so much resources, that only a minority of companies can bear the investment. The consultants of the company have introduced tailored software solutions for the analytics. We are of opinion that many companies should consider seriously this kind learning approach coated with sophisticated out-of-the-box analytics in their own business. There is at least one product available, which can meet most of these needs at a reasonable cost.   Please have a closer look at Boudin Skills Analytics Software for finding benefits for your company.

Written by Kari Hartikainen, Sales, Boudin Oy

Present-day companies have in their hands tons of valuable information which is waiting to be used purposefully. Business days are normally too hectic for having enough time to sit in peace and have thoughts how to exploit all the gathered data in different formats. The fact, that the data is not available in a structured way is normality and It doesn’t make the situation any better for HR- and learning professionals, who might be able to make sense of the vast amount of stored data. The cloud storages and the company owned blades are also full of training scores.  The different datasets have a variable quality and are unrelated in relevance to each other.  Finding a common denominator in this data and making sense of it, are the issues we want to concentrate on in this writing. 

Employee training is done in companies according to their short and long term business needs. Endless meetings are needed for making training programs, which are designed to meet these requirements. External resources are often involved in this endeavor where content developers are needed and a serious amount of money and time has to be invested. To steer businesses efficiently requires continuous flows of reliable and valid information from all business functions. These demands make us to think again about how to access the value of all the available learning scores. How those numbers could create business benefits when properly used by the HR management together with the different business lines, especially sales.  We think, that a better utilization of existing training scores could be worthwhile. 

And the good news is, that learning data utilization is not so time consuming as people generally think. Learning scores are collected from repositories which are already existing and downloaded to be used by skills analytics software and to be related with business results. This way we have established a feed-back loop from the different training results back to the HR- and business line functions. This modest operation gives to HR- and training professionals, as well as business line management valuable analytics about the status of skills within their company. This information can easily give answers to many questions which have been too difficult in the past. The analytics provides wide possibilities for detailed what-if questions to be asked. These questions are of special interest to the business line management, because this kind of analytics give answers to many fundamental questions, which have been open and unanswered for a long time for many. Some tailor-made software for solving very complex, skills related problems exist, but they are property of renowned consulting companies and because of the high expenses involved only accessible for few wealthy companies. Certainly, HR- and training professional are aware of these developments. Below you can find some typical situations explained, where skills analytics can provide valuable results.

Product skills are needed on all levels of the company.  When digging into training score data you can find different levels and distributions of skills in every location of the company. With Boudin analytics you get product specific insights to the skills and skills distributions of your personnel with the accuracy of your financial reporting. You get an understanding where you are in conformity with the needed skills and where you are short of skills. This abundance of pin point accuracy of informativity is totally different to the typical situation you face in most companies, where you have access to some lists, which describe only the names of individuals, who have either participated in a skills training course or, in rarer cases, who has successfully passed the course and when and where this course has taken place.  

With Boudin analytics, you get much more from the training of your employees. When you have set skill level requirements for certain products or services, you have the possibility to make comparisons between the targeted level of skills and the actual level of skills on every level and in every location of the company.  You know now, where possible improvement is needed and what exactly is needed to reach the set goals in skills.  This out-come you can only get, when you set learning requirements for training courses, when you set minimum acceptance levels for passing these courses and finally, you have to start utilizing course score data by analyzing it.  The HR professionals, who work with the training issues of the personnel appreciate accurate information about where and when certain individual needs to be trained, so that the skill level requirements of the person, workplace and the location are met. By planning training this way, HR- and training professionals can be connected with the sales management and they can share their expertise for improving the sales by providing accurate skills training cost effectively.

The sales management gives high value for being assured, that the current skills situation is on par with the requirements, without any need to dig into the skills issues. The fact, that the skills insights are based on sales report structure makes the acceptance of the enhanced training analytics much easier for those, who have to take part in the financing of the analytics.  When you can find out with the analytics, that more accurate training will improve your sales by a certain percentage, you can be sure, that the sales management wants to know, that there are enough and correct skills in all business locations currently, and they want to make action plans, how to ensure correct skills also in the future.  The training needs broken down by business location or sales channel have to be planned in advance and these plans have to be executed respectively.

The last but not the least matter is getting a comprehensive understanding of the connecting link between sales and skills. This link can provide awesome benefits by explaining, how much extra sales we could generate by specific training. This is only possible after we have procedures, for measuring skills and utilizing learning scores continuously, in place, working and finetuned.  Boudin analytics visualizes marginal utility for training, so that you can see how much you should invest into your training.  In the beginning the effect of invested training €uro or Dollar is stronger and with the increased saturation of skills it declines.  You know now, where you are. You don’t need to train your staff for marginal effects and because of this, money is not wasted.  We can now know, that sales are not skills restricted. Sales management can now concentrate on other business concerns having effects on sales, and there are plenty of them.

Written by Kari Hartikainen, Sales, Boudin Oy

Sophisticated use of data analytics marks the moment when the ”Information Society” finally fulfills the promise implied by its name.  According to Wikipedia, there is however currently no universally accepted concept of what exactly can be defined as an information society and what shall not be included in the term. Most theoreticians agree however, that a transformation, that formed most of today’s net principles and currently as is changing the way societies work fundamentally, can be seen as started somewhere between the 1970s, the early 1990s transformations of the Eastern Europe and the 2000s period.

Professor Frank Webster, PhD, author of the book, ”Theories of the Information Society”, has listed five major types of information that can be used to define information society. These are:   

– Technological Information

– Economic Information

– Occupational Information 

– Spatial Information

– Cultural Information

Professor Webster states, that the character of information has transformed the way that we live today and how we conduct ourselves centers around theoretical knowledge and information.  The data takes centre stage. All those digital bits, that we have gathered, can now be harnessed in novel ways to serve new purposes and unlock state-of-the-art forms of value.  But this requires an open-minded way of thinking and will challenge our companies, institutions and us.  The only predictable thing that is certain is the fact that the amount of data to be handled and the needed power and data storage to process it all will continue to grow in sync.  

Most people have considered data analytics principally as a technological matter focused on the hard- and the software used for processing the data. With increasing quantities of data, it has become increasingly important to decide, what insights you want to gain when trying to separate the wanted signals from the surrounding noise.  We also believe that more emphasis needs to be shifted to issues, like what needs to be done when the data has spoken.

Instead of being obsessed about the accuracy, exactitude, cleanliness, and rigorousness of the data, we can today face the data from a more liberal angle and give some slack.  We shouldn’t however accept any data that is outright wrong or false, but some messiness may become acceptable in return for capturing a far more comprehensive set of data and getting new intelligence about issues residing below the surface.  In fact, in some cases big and messy data sets can even be beneficial, since when we tried to extract knowledge by using just a small, exact portion of the data, we ended up failing to capture the breadth of detail where so much knowledge lies.

The main idea in many cases is, that it may be more advantageous to focus on clarifying what information is hiding inside of the data instead of trying to solving the why -issue.  There might be multiple causes for certain phenomena when solving skills related problems. As an elegant way for finding insights form the data we approach it considering in our analysis its group-related statistic characters.  They describe the stochastic relationships within groups, but never allow explicit conclusions to be made about individuals of the population or about the causality of single incidents.  This doesn’t however reduce the empirical relevance of the insights.

Because correlations can be found far faster and cheaper than causation, they are often preferable.  But for many everyday needs, knowing what is not good enough.  Stochastic data correlations can however point the way toward promising areas in which to explore causal relationships.  You need to know, where to dig deeper for answers for the question: Why?

Of course, causality is nice when you can get it.  The problem with real life multi factor causality is, that it is often hard to expose causality, and when we think we have found it we are often deluding ourselves.  Wishful thinking can possibly lead you to wrong avenues of research.   A fundamental reason for that is, that when we have more data available and since more aspects of the world are being datafied and collected, there is a danger for us to go astray.

Much of the value of data will come from its secondary uses, its option value, not simply its primary use, as we we’re accustomed to think about it.  As a result, for most types of data, it seems sensible to collect as much as one can and hold it as long as it adds value, and let others analyse it if and when they are better suited to extract its value.

Sometimes important assets will not just be plainly visible pieces of information.  The bulk of data created by people’s interactions with skills and learning is a source which a clever company can use to improve existing analytic services and even launch entirely new ones.  Because we are now able to predict how different course scores may influence the future of our sales or the efficiency of our services, this will allow us to take remedial steps to prevent problems or to improve outcomes.  We will detect students and performances which will start to slip before the expiry dates of the courses.  We can support those, who have problems, and those who struggle.  Successful supportive action will improve the performance of the individuals and be beneficial for the company. 

Nothing is preordained, because we can always respond and react to the information we receive.  Calculated predictions are not set in stone – they are only likely outcomes, and that means that if we want to change them we can do so.  When we are able to see problems arise in the horizon, we are able to rapidly take corrective actions.    Because we can never have perfect information, our predictions are inherently fallible.  This doesn’t mean they are wrong, only that they are always incomplete.  It doesn’t negate the insights that big data otters, but puts learning analytics in place – as a tool that doesn’t offer ultimate answers, just good enough ones to help us for now with our everyday business challenges until better methods and hence better answers come along.

Written by Kari Hartikainen, Sales, Boudin Oy