Data Monetization – Use Cases, Implementation and Added Value
Data monetization: a definition
Data monetization describes the goal of generating actual value from data in the form of cost savings, revenue increases and risk minimization. It is about developing a vision and strategy that includes guidelines on how to use time, money and resources properly. In this way, the requirements to generate real value from data can be created without becoming mired in a flood of possibilities.
Costs and benefits
Building and maintaining IT, data and BI environments generates a lot of work and many companies find it difficult to compare their costs with the added value they bring. Nowadays, with the creation of data labs, classic ROI analyses are no longer relevant because it is not possible to predict in advance which – or whether – innovative business potential or solutions can be identified and subsequently used to create value. So how can costs and benefits be compared in these cases, and how can data be monetized?
This global BARC survey examines how far data monetization initiatives have already flourished, what approaches have proven to be successful, and the challenges that need to be considered. The results should inspire readers to purposefully and efficiently generate value from data.
The following questions are addressed:
- What value / goals do companies expect to achieve through the use of data?
- How far are companies along the road to successfully monetizing their data?
- How do companies measure the value of data management and analytics? How are projects monitored / controlled?
- How are organizations trying to make better investment decisions in data management and analytics?
- What opportunities do companies see in the use of data in the area of data management and analytics?
- What are the biggest challenges in predicting the added value / presenting the benefits?
- What are the biggest challenges when deciding on investments?
- How do existing organizational structures, processes and tools enable or support investment decisions for data management and analytics?
- Who is responsible for preparing, executing and evaluating the added value of investment decisions?
- Which elements are taken into account in investment decisions? (e.g. time, money, resources)
- Which measures have proven to bring transparency to value creation through data?
- Which types of companies see the greatest need for better organization and implementation of investment decisions, and for clarification of the added value of data management and analytics?
- Which types of companies are currently the most mature in this area?
- What investments are required? (e.g. investment in skills, external consulting, software)
Download the report
The management summary of this study can be downloaded here