How can data analytics be used for digital innovation?
New research from Umeå university explores the possibilities for firms to use data collected via online communities.
Text: Elin Andersson
Vasili Mankevich, researcher in informatics at the Swedish Center for Digital Innovation at Umeå university.
Firms and individuals are interacting online at an unprecedented scale. These interactions may lead to new digital products, services and practices, all of which are manifestations of digital innovation. The innovation process relies on records users leave on various digital platforms which carry information about their activities – digital trace data.
- Data recorded from interactions between firms and individuals online may be developed into valuable innovations. Achieving such value creation is not a trivial task however, since the sheer scale of the generated data makes it difficult to collect, let alone understand. Therefore, firms need strategies for handling (acquiring, filtering, processing, interpreting and exploiting) this enormous amount of available data, says Vasili Mankevich, researcher in informatics at the Swedish Center for Digital Innovation at Umeå university.
In his recently published dissertation, Vasili Mankevich investigates the potential utility of digital trace data generated by online communities. - The explosive growth of digital trace data will continue and intensify. Acquisition and use of this data will become increasingly crucial for economic development and entrepreneurship. My study enables reflection about the limits of using it in in all kinds of automated processes, but also the opportunities it provides.
The dissertation is based on four empirical computational-qualitative investigations of firms interacting with online communities that are rich with digital trace data. - I studied firms that develop digital products and services by interacting with their online environment. In one of my studies, I investigated how data is used for storytelling by entrepreneurs on Kickstarter platform. In another study I investigated digital innovation efforts by the leading data analytics provider – Tableau Software.
While there is an understanding that digital trace data is diverse and with varied promise of innovation, there is still little guidance for firms that attempt to manage this process, says Vasili Mankevich.
Vasili Mankevich points out that there has been previously a strong focus on the technological aspects of how to handle large amounts of data, and a lack of focus on how to interpret its meaning. - When data is collected and abstracted, there is a risk that valuable meaning is lost. For example, how can a meaningful metric be derived from a message left on a support forum, or a rich user submission to improve software be abstracted? We need to confront data with meaning through dialogue, disentangling friction, and contextualization: data analytics professionals need to understand the circumstances of data creation and actualize it to the context of future use.
If firms are to be able to use the full potential of digital trace data, Vasili Mankevich believes that it will require organizational changes. - So far, organizations have widely defaulted to using data analytics for the “safest” projects, such as reporting and accountability. To be able to use rich digital trace data, firms need to embrace experimentation and a level of openness that will challenge many established processes and organizational identities.