In recent years, there has been a lot of hype surrounding "big data" and analytics. People no longer view data as merely IT-related byproducts, but as sources of information that can actually be of strategic value. Managers hope that investing in data-driven initiatives will ultimately bring major positive benefits. The reality is often less rosy: organizations struggle with such initiatives and the benefits tend not to materialize.
In my research, I focus on how organizations can effectively use data in a strategic way.(1) Using three studies, for my dissertation I researched the fundamental choices organizations need to make, the role of data in creating strategic opportunities as well as challenges, and the influences of different stakeholders when organizations want to realize value with data. In doing so, I considered not only economic value, but also social value. As a result of these studies conducted at the KIN Center for Digital Innovation, I am happy to share three key lessons.(2)
Many organizations are rushing to hire analysts and data scientists to translate data into insights. But realizing value with data is not just the job of these analysts and data scientists. On the contrary, this process requires collaboration between people at different levels, inside and outside the organization.
Senior managers, for example, play an important role by ensuring that there is alignment between the way people on the shop floor deal with data, the way processes are set up, and the way the interests of external stakeholders are handled. If an organization wants to use data in a strategic way, there must also be support for this at the strategic level.
Organizations are also forced to cooperate with external parties. On the one hand to be able to access data from other organizations, but also to ensure that social value is actually created. For example, if an organization aims to influence consumer behavior using data and analytics, those same consumers must somehow be involved in the process. After all, they are the ones who have personal data to share and who ultimately evaluate whether the products and services they receive in return are worth it.
One of the key messages from my research is that organizations need to have a clear picture of what data they have at their disposal. In this regard, it is not so important whether an organization collects "big data" or "traditional data. The question that needs to be asked is: what are the characteristics of the specific data we have available and to what extent are those characteristics relevant within the context in which we want to use the data? For example, for some organizations it is important that their data be updated continuously and in "real-time"-though this is by no means a requirement for all organizations. Sometimes it is important that data be collected at the highest level of detail, while otherwise it is sufficient to take average values.
When using data from external sources, it is also critical to know exactly where that data came from and under what conditions it was collected. For example, who owns the data and should we involve them in the process if we are going to use the data? Managers again play an important role, ensuring that there is alignment between the strategic goals and the data.
I emphasize in my dissertation that data-driven value realization is a journey of discovery. Not only are the data themselves subject to change, but analyzing and experimenting with data can lead to insights that had not previously been considered. For example, when analysts actually experiment with data, it may lead to insights that open doors to new markets and business models.
It also happens that new insights lead to new challenges. In my dissertation, I tell the story of a logistics organization that found out that they could infer consumer preferences within certain areas based on process data. Initially, they thought about the possibilities this presented and planned to sell these kinds of insights to outside parties. However, after experimenting with this data, it soon became clear that some of the insights could also be personal in nature. This discovery forced the organization legally, but also instinctively, to rethink their strategy. In practice, it does happen more often that seemingly "innocent" data leads to personal and sensitive insights when combined with other data. Organizations must therefore continually consider the unexpected consequences that arise from data-driven initiatives.
My research shows that data analytics crosses boundaries, that one must be clear about what data one has at hand and where this data comes from, and that data-driven value realization is an ongoing process. These insights can be of interest to organizations that want to move beyond the hype and actually create value with data.
(1) www.kinresearch.nl
(2) Conducted at KIN Center for Digital Innovation, Vrije Universiteit Amsterdam www.kinresearch.nl
This article can also be found in the Big Data dossier
