Use This Framework to Predict the Success of Your Big Data Project
Up to 85% of big data projects fail, often because executives cannot accurately assess project risks at the outset. But a new research project offers some guidelines and questions to ask yourself before launching a new big data initiative to help predict its success. Access to data is obviously a precondition for any initiative focused on data-driven growth. However, not all available data is useful, nor is it unique and exclusive. Moreover, not all data is available. The question executives need to ask is “Can we access data that is valuable and rare?”. If the answer is yes, you then need to ask: “Can employees use data to create solutions on their own?” and “Can our technology deliver the solution?” And finally, “Is our solution compliant with laws and ethics?” Little value can be created if your solution breaks the law. Moreover, if users think of the solution as “creepy,” you might face a media backlash. Try this structured approach to predict the success of your next big data project.
Big data projects that revolve around exploiting data for business optimization and business development are top of mind for most executives. However, up to 85% of big data projects fail, often because executives cannot accurately assess project risks at the outset. We argue that the success of data projects is largely determined by four important components — data, autonomy, technology, and accountability — or, simply put, by the four D.A.T.A. questions. These questions originate from our four-year research project on big data commercialization.
The components needed for success with big data can be positioned along two dimensions: (1) the focus of the activities (the project’s ideation or implementation — such as coming up with an idea for a big data project versus actually implementing the project) and (2) the focus of the transformation (digital backbone or getting people’s support — such as building the IT-architecture needed to create a sufficient digital backbone or making sure that employees can and will apply data and that this application is in line with societal opinions on what should and should not be done with data). These two dimensions create a matrix of D.A.T.A. components and the key questions executives need to ask when contemplating new big data projects, as seen below.
The figures below provide an overview of the D.A.T.A. components, the related questions, and the rationale behind them as well as examples. Moreover, the table demonstrates the sequence in which the different aspects should be considered in data projects. In the following, we provide an in-depth outline of each component.
Data: Access to data is obviously a precondition for any initiative focused on data-driven growth. However, not all available data is useful, nor is it unique and exclusive. Moreover, not all data is available. The question executives need to ask is: “Can we access data that is valuable and rare?” Only when these criteria are fulfilled can executives hope to gain a temporary competitive advantage based on data.
Consider the Danish social fitness-tracking app Endomondo. In 2015, the American athletic apparel company Under Armor bought Endomondo for $85 million in an attempt to build “the world’s largest digital health and fitness community.” Endomondo had more than 20 million users and over 80% of them were located outside the United States. Hence, Under Armour expected the acquisition to not only provide access to data that it considered valuable and rare, but also to give the company “immediate scale and increased international presence,” as stated in the press release. The very price that Under Armor was willing to pay illustrates the value that it assigned to Endomondo’s data. Consequently, data is becoming a resource that increases in commercial potential with its user value and its uniqueness in the marketplace.
