A New Approach to Business Value Driven Planning for Data Projects

With the advent of new technology and digital trends, realizing value from data is a top priority for organizations. Along with this, the increased awareness that every business is a data business is beginning to take hold, especially in organizations engaging in data projects through the use of technologies such as Big Data, the Internet of Things and Advanced Analytics. However, it has been shown that there is a lack of understanding on how these projects will deliver value or benefit for the organization (LaValle et al. 2011). Or indeed, there is a lack of understanding on how to effectively manage and govern such projects and capabilities (Tallon 2013). In other words, implementing a data project does not automatically deliver business value, execute as expected, or make your organization data driven. To make your organization more effective when implementing data projects and developing a mature data capability, conversations need to be initiated between stakeholders and focus on the key problem to be solved by the data project. This focus is provided by answering six simple questions: why, what, when, who, where, and how. Yet, given the multitude of conversations that need to take place around the problem there are a lack of appropriate tools that can enable stakeholders to reach a shared understanding when planning data projects. Our research with major public and private sector organizations over the past three years has resulted in the development of a new ‘discursive template’ (c.f. Tsoukas and Chia 2002) (namely the Data Value Map - DVM to promote new transformative conversations within data projects while also producing a more rigorous and robust validation of the potential value of those projects. Moreover, this new approach is the output of four studies, which include: (i) a survey of over 50 organizations worldwide which examined the drivers, goals and barriers of data analytics, (ii) an analysis of 18 projects focused on developing data solutions, (iii) an analysis of over 100 implementations of the DVM, and (iv) one in-depth case study with multiple implementations. The objective of this paper is to present both our new approach for planning data projects along with the insights gained from these studies.