Learning Scores for Understanding Skills, Knowledge and the Ability to Predict the Future

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.

Use your competences.

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

When Skills Matter – Keep Them Updated

Today you can evaluate the skills of your personnel objectively by using a math-infused method that looks at the different product related skills using a new approach based on a new set of metrics.

Each product in the company’s selection requires that the personnel has successfully passed several product specific training courses.  Tracking the validity of course results is complicated, because often several individual courses need to be accomplished, before sufficient product skills are present.  The time value of these courses is limited, because they have a predefined validity period.  We have to calculate for each course score their present time values because the learned substance today is less actual, than it was at the time of its complement and some details of the learnings have been forgotten.  

Several course results and their residual values are interdependent but the impacts of the skills on KPIs are independent.  When you Include all these necessary elements into your calculations, this can easily lead to huge number of data points to be actively monitored.

The data-driven approach has revealed dimensions in skills that always have been present in all workplaces but have been well hidden in the complexity of captured learning- and skills metrics in the databases of Human Resources.  

The simple question of: ”What is the level of personnel’s skills?” can reliably be answered only with a data driven approach. This simple sounding question becomes immediately more complex, when you drill down to multiple segments, like, what is the level of product skills in different markets, countries or sales offices.  Multiply these with different language groups, product groups and most important individual key products and you arrive at – reality. Complex reality is the space where we all live. What you need to know changes, whom you need to know changes, and so does what you need to study to prepare for professional life.

Statistical analyses force people to reconsider their instincts.  Through skills data, this becomes even more essential.  The learning specialists have to cooperate closely with their colleagues who are competent in statistics and analytics. They will find new ways of doing their work by giving free speech to the data relying on stochastic correlations without prejudgements and prejudice, confident that the aggregated data will reveal its hidden truths.

For example, the online education company Coursera uses data on what sections of learning material may have been unclear and feeds the information back to teachers so they can improve.  Other companies use analytics to define what is the effect of different course alternatives to work related outcomes, like increased sales or other KPI. 

Yet expertise is appropriate for a conventional world where one never has enough information, or the right information, and thus has to rely on intuition and experience for decision making.  In such a world, experience plays a critical role.  The long accumulation of latent knowledge – knowledge that one can’t transmit easily or learn from a book enables traditionally one to make smarter decisions.

On the other hand, when your company has lots of data at their disposal which you can tap to be used for analytics, you can make better, and more objective decisions.  Thus, those who can analyse their under-utilised data pools better, may see past the superstitions and conventional thinking not because they are smarter, but because they have the data, and they use the data.  

Dr. Erik Brynjolson, a business professor at MIT’s Sloan School of Management and his colleagues have evaluated productivity levels and performances at companies with different decision-making styles and have benchmarked them against competition.  They found out that data-driven decision-making gave the data-guided firms clear advantages.  When this philosophy is adopted into improvement of the personnel’s skills advantages will undoubtedly surface.

With cloud based solutions firms can today easily adjust their amount of computing horsepower and storage to fit actual demand.  Because previous fixed cost have transformed into variable ones, the advantages of scale based on technical infrastructure can be enjoyed by all of us.  What counts today is scale in data.  It is possible to hold and analyse large pools of data and it is realistic to capture ever more of it with ease.  Data holders will flourish as they gather and store more of the raw material of their business, which they can reuse to create additional value also in the field of learning analytics.

Smart and nimble small players can today with SaaS solutions enjoy and offer the benefits of so called ”scale without mass solutions”.  They can have a large virtual presence without hefty physical resources, and can diffuse innovations broadly at acceptable cost.  You just need to be able to enjoy the services based on fresh and innovative ideas and run the analytics on cloud computing platforms. 

Companies, presently using learning data for improved skills based results, have a strong incentive to keep adding and analysing more granular training data, since doing so provides greater benefits and the cost for substantially improved results is only marginal, because of following reasons:

First, they already have the infrastructure in place, in terms of storage and processing.

Second, there is a high value in combining existing datasets processed with new algorithms.

Third, using known data sources in an innovative way, simplifies life for data users.

Using data-driven learning analytics is easy and rewarding.  It gives insight in the value of your company’s learning results data.  Slice and dice the information in a way benefitting you the most.  Bring out the effects of your learning results that many in your organisation have assumed to exist, but only a few have dared to request. Taking the first of five easy steps to learning analytics is gratifying, fun and very interesting. The steps lead you to a new level of skills utilisation.

Written by Kari Hartikainen, Sales, Boudin Oy