Self-service data strategies are becoming increasingly important for organizations, as they aim to provide enterprise data to a wider range of users, including non-technical professionals. However, a recent study commissioned by Capital One Software found that while 86% of data science and analytics decision-makers viewed self-service data strategies as critical to business success, less than a quarter of organizations have implemented business-focused roles in such strategies.
The study, conducted by Forrester Research on behalf of Capital One Software, defines self-service data strategies as approaches that allow technical and non-technical professionals to leverage data faster without a central tech team. This means empowering users to access and analyze data on their own, without relying on IT departments or data specialists.
The study’s most surprising finding was the discrepancy between the value businesses place on self-service and their lack of success in implementing it. According to Patrick Barch, Senior Director of Product Management at Capital One Software, this suggests that organizations are aware of the problem but don’t have a clear solution.
Implementing self-service data strategies presents various challenges that stem from technical, cultural, and financial considerations. On the technical side, maintaining user-friendly environments was ranked as one of the top challenges by 49% of respondents. Creating an environment that caters to the diverse needs of different user groups, including data producers, consumers, and stewards, can be complicated.
Other technical challenges include increasing data trust and quality, as well as scaling self-service for decentralized environments. These technical challenges need to be addressed alongside cultural issues. Different teams within organizations have their own unique needs and ways of working with data, which can make collaboration and scaling difficult. Creating a collaborative culture is essential for self-service to succeed, according to 75% of the survey respondents.
Financial considerations are another hurdle to overcome when implementing self-service data strategies. More than half of the respondents cited difficulty funding such initiatives, with the main obstacle being the difficulty of demonstrating a clear return on investment (ROI). To overcome this challenge, Barch recommends treating self-service as a table stakes requirement for any data initiative, rather than a standalone initiative. By including self-service as part of a broader data initiative, organizations can maximize the value of their investment and make it easier to secure funding.
It is important for organizations to tie the benefits of self-service to tangible business outcomes, such as improved decision-making, higher-quality data science models, or reduced risk. This can help demonstrate the ROI of self-service and make a compelling case for investing in these strategies.
The study’s findings align with Capital One’s own experience with self-service data. The financial services company is continuously working on improving the data-consumer experience and making governance easier. By addressing the technical, cultural, and financial challenges associated with self-service data strategies, organizations can unlock the full potential of their data and empower users to make better, data-driven decisions.