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