The companies that will succeed in future are the ones that understand big data. But how will it impact on knowledge management and innovation? Janet Garcia tells us more.

The convergence of knowledge management systems and big data will see the emergence of big learning data as a key element of competition, innovation and growth in knowledge-based industries. By being able to quantify how, when and where employees share insight, management can guide investment in knowledge solutions in alignment with business plans.

Innovation is often seen as the key driver of growth in knowledge economies, its output the result of developing commercially useful knowledge.

The quest to tie these last three words together has proven to be the holy grail of the learning industry, its elusiveness long holding terms like L&D and P&L apart, occasionally bumping into each other in the lobby of learning conferences but rarely seen together in the corner office.

The reason why is less difficult to pin down. Traditional corporate learning has always been inherently difficult to quantify in terms of a measurable outcome. Indeed, there has been an increase in ‘ruthless prioritisation’ whereby L&D investment is being focused on a smaller number of key areas that are expected to have the greatest impact on business performance.

We are also seeing increased emphasis on knowledge management strategies at a business level as technological advances in the processing and application of knowledge are being seen as a major source of innovation and competitive advantage.

In terms of processing knowledge, knowledge management systems are pivotal in supporting the creation, capture, storage and dissemination of information. In order to effectively guide decision making however, companies need to be able to transform and exploit that knowledge for commercial gain. It is this gap between acquisition and application that has perhaps prevented knowledge management from grabbing management’s full attention until now.

"Within those industries where knowledge is the biggest asset, the emergence of big learning data has the potential to become a key basis of competition and growth."

Today, the convergence of a series of technological trends is poised to push knowledge management in to the limelight. While the advent of web 2.0 and the explosion of ubiquitous computing have been crucial in the evolution of knowledge management systems to their current locus of value, it is the subsequent arrival of big data [1] which has the potential to add the transformative and exploitative capabilities that will form the final and perhaps most important piece of the puzzle.

While data has always been part of the impact of technology, it is the accelerated scale (90% of the world’s data has been created in the last two years) and variety of data that now sees us on the brink of a tidal wave, with the flood set to penetrate and transform every sector and function of the economy.

Companies are already churning out a tremendous amount of not only transactional data but also an increasing amount of 'exhaust data' (data created as a by-product of other activities) as they interact with customers. In a digitised world, this information has the potential to help businesses create new products and services, enhance existing ones, and invent entirely new business models [2].

Within those industries where knowledge is the biggest asset, the emergence of big learning data has the potential to become a key basis of competition and growth. Under the same principle, learners communicating, browsing, sharing and searching generate a vast digital footprint which will help guide how companies optimise knowledge within the organisation to grow the business.

While efforts to digest an increasing number of bytes to deliver actionable insights has long been a goal of knowledge management, the prohibitive cost of collecting and storing the scale of data required limited the capacity of firms to do so.

However, the ability to store, aggregate, and combine data as well as use the results to perform deep analyses has become increasingly affordable as trends such as Moore’s Law in computing, its equivalent in data storage, and cloud computing continue to lower costs and other technology barriers [3].