The Data-Driven Innovation (DDI) Framework is a methodology developed in the context of BDVe to guide start-ups, entrepreneurs, and established companies in identifying data-driven innovations. It focuses on three key elements: identifying new offerings, analyzing underlying technologies, and assessing the nuances of addressed markets and ecosystems. Data-driven innovation is critical to the future of business, but it is only part of the solution.
To create an effective data-driven innovation framework, organizations must first define the problem they want to solve and a solution for that problem. Traditionally, data was distributed through physical media, which is expensive and limited the volume of data. Today, organizations make data available on their websites, which can be accessed at no cost. Some organizations may simply provide raw data files, while others develop application programming interfaces (APIs) that facilitate access to their data.
While big data technologies are a key aspect of the new economy, governments can also help accelerate their development. The Obama Administration, for example, announced a $200 million program to accelerate the development of big data. By helping to foster data technologies, government agencies should be encouraged to share their data with other organizations. And to ensure the success of the new technologies, governments should consider tax credits and open-source software. This way, government agencies can avoid losing out on tax dollars.
Developing an effective data innovation framework requires the creation of a dedicated team and a clear definition of the goals and objectives of the lab. A data innovation lab can create a new idea but if it’s not properly guided, it can become an academic exercise that does not produce tangible value. By taking the time to define the project selection process early, companies can make sure that the data innovation labs are truly a worthwhile investment.