First of all, what is Bid Data? According to IBM, “Everyday, we create 2.5 quintillion bytes of data–so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: from sensors used to gather climate information, posts to social media sites, digital pictures and videos posted online, transaction records of online purchases, and from cell phone GPS signals to name a few. This data is big data.” Read more about big data here.
According to O’Reilly Radar, The value of big data to an organization falls into two categories: analytical use, and enabling new products. Big data analytics can reveal insights hidden previously by data too costly to process, such as peer influence among customers, revealed by analyzing shoppers’ transactions, social and geographical data. Being able to process every item of data in reasonable time removes the troublesome need for sampling and promotes an investigative approach to data, in contrast to the somewhat static nature of running predetermined reports.
In general large enterprises allocate sizable resources in the form of people and software infrastructure to manage training and performance. Learning management systems, content management systems, talent management systems, and myriad other tools are deployed. In variably data from these various tools never gets analyzed holistically. Every tool has its own reporting interface and most of them are extremely transactional in nature. Example, a report that spews out who sat through the course. While such reports are important, enterprises are missing out on the need to figure out how training can be delivered to an individual in a way that takes into consideration personal preferences, learning styles, cognitive abilities, temperament, training usage patterns, context, and performance history. I definitely see an opportunity for deploying analytics that take a broader view to enhancing learning effectiveness and the resulting performance improvements.
Here is a scenario. Retail cosmetics sales is a high-energy and demanding job. If you spend some time in the cosmetics section at any of the major retail chains, you will see that the sales associates are performing a variety of tasks that includes educating customers, upselling, cross-selling, selling financial products (store credit cards), generating new leads, building relationships, explaining product usage, answering questions pertaining to skin types and suitability of a product (example: lip stick for a particular skin profile) and so on. You might think retailers would go to great lengths in using data and analytics to train cosmetics sales teams the same way they analyze their customers.
The ground reality is different. Training for cosmetics sales is still delivered in a one-size fits all models through classrooms, LMS or a combination of both. These training models don’t take into consideration the individual’s personality and background. Interestingly, retail chains and cosmetics brands have rigorous evaluation and on-boarding process for cosmetics sales positions. What is missing now is the ability to map the individual’s personality, ability, and intelligence with training content and delivery mechanisms. Imagine an Amazon recommendation engine embedded into your LMS that knows who you are, how you prefer to learn, and when to present content!
Check out this brief talk by Steve Schoettler, co-founder of Zynga, about how big data and analytics might help K12 education. We can easily draw parallels between K12 education and enterprise learning when it comes to big data and analytics.