While almost every organization recognizes the power of finding and harnessing the value in their data, few have truly realized the transformational potential of becoming data-driven. Some of the major barriers to data nirvana are ever-growing data volumes, new data sources and types, and the need for quick wins that demonstrate an ROI while simultaneously having to lay the analytics foundation for future business needs.

Having a well-armed data science team that uses the latest analytics technology and having the most-skilled employees and partners are pre-requisites for success with analytics. An often-overlooked ingredient to overcome the barriers mentioned above is the approach to sharing insights with your organization.

There have been two major strategies to date: The Big Bang and a less-centralized method where individuals and lines of business develop their own resources, pipelines, and output.

The Big Bang approach involves meeting the needs of every department and individual knowledge worker at the same time. This philosophy almost never works because it is so difficult to align the technology with all business use cases. LRS has a customer that attempted this with another partner and a pre-built platform that could aggregate all data and solve every problem. They gave up trying to make it work after 5 years and $7 million and had to start over from scratch.

Another common process is “grassroots,” where individual teams select their technologies, access data sources themselves, build their own data pipelines, visualizations, and AI models. The problem with this approach is that teams duplicate efforts, miss out on enterprise standards on technologies, and governance may be an afterthought or ignored altogether.

Faced with these challenges and current methods, what is the best way to realize near-term value with data while laying a foundation for future requirements? That’s where thinking about your data as a product will help you solve your most pressing business problems.

Data products deliver high-quality, governed, and trusted data that users across an entire enterprise can consume for analysis on a single platform. While an organization could have dozens or hundreds of individual use cases, all the cases can be rolled up into broader categories such as operational reporting, data exploration, data science, process automation, or even external-facing applications for partners and customers. A single platform, such as a data fabric, can support each of the major categories and the specific way that they need to collect, organize, and govern data. This is similar to how auto manufacturers will build different models on the same chassis. This produces entirely different cars with different features, but all built on the same standard foundation.

Using the Data-as-a-Product strategy with a data fabric is a winning approach. With this, you can access data in place with built-in protection, ensure governance and compliance that’s centrally defined but executed in a distributed way, and automatically funnel your data to wherever it needs to be.

If you are interested in learning more about Data-as-a-Product and how LRS can help you implement it with a data fabric, please contact us to request a meeting. If you don’t know how to get started, our strategic roadmapping services will help identify the gaps in your analytics approach that prevent you from achieving your business goals and recommend the capabilities you need to attain them.