Do We Need to Understand the World to Know It? Knowledge in a Big Data World

In both practice and academia, the incredible access to data is creating a marked disruption. The perfect storm of massive amounts of digital data, sophisticated analytical tools and cheap, scalable processing power has fostered a data-driven mindset in both corporate and academic practices. These are being embraced to varying degrees in companies around the world as well as in global academic communities. However, swinging the pendulum in the relationship between knowledge and data too far toward data can have adverse consequences. This editorial offers a cautionary note for both companies and academia. For a number of years, in practice, the distinction between data, information, and knowledge could be stated with some level of precision. Data are raw facts and figures that can become information when massaged and placed in the right context. The value-adding activities from data to information are largely the domain of information systems. Knowledge, however, adds experience and expertise to the information and often resides in tacit form in people’s heads. So, the Knowledge Management System (KMS) popularity that started in the 1990s was intended to capture knowledge (largely tacit) and put it in a system that could benefit others in the organization (Davenport & Grover, 2001). KM processes include externalization (taking tacit knowledge and representing it in a KMS) and internalization (making this knowledge accessible to people who might need it). For example, a global consulting firm might have a team that concluded a multiyear project in Malaysia – and their experiences, successes, failures, precautions, and guidance in a KMS – could be invaluable for other teams initiating projects in that region. KM practices included creating the right incentives for knowledge to flow between people and the system, as well as embedding knowledge into products and services offered. In academic research, particularly the social sciences including Information Systems, knowledge is largely represented as an abstraction – a theory or model – that explains or predicts the real world. It is in the efficacy of these abstractions, as assessed through logic or impact, that academics gain their esteem. The advent of “big data” in recent years, along with advanced analytics and machine learning has created a disruption both in the practice of KM as well as in the importance of abstraction. Big data, through its global reach and its sheer volume, velocity, and variety, along with computationally intensive analysis, offers opportunities for generating new insights. Mining of big data and digital streams can yield fresh perspectives for decision-makers, optimize and automate processes, and discover new ways to understand and fulfill customers’ needs. It can also offer precision of predictions to important questions in both practice and academia. However, in observing the ready embrace of big data and analytics there are also signs that some companies and researchers might fall into a trap where they see data replacing knowledge, blurring the distinctions in the trichotomy described above. The argument implicit in this, is that knowledge was largely a correspondence between our observation of the world and its interpretation in our brain, in the form of models that we used to make sense of observations. Now, with the plethora of data, we do not need to understand the world to know it. All knowledge can be extracted through the data. So, in this view, knowledge is not internalized through human assessment but externalized through data. While many may not endorse the data is knowledge view, companies that intensively invest in data and analytical capabilities could be accepting a data culture that comes at the cost of human interpretation and judgment. Companies, where everything revolves around data science and “show JOURNAL OF GLOBAL INFORMATION TECHNOLOGY MANAGEMENT 2020, VOL. 23, NO. 1, 1–4 https://doi.org/10.1080/1097198X.2019.1701623