Self-Service for data is not a new concept. Even in the early 2000s, companies have struggled with giving “power users” or “information workers” access to data to develop value-based insights. One of my past customers had a simple as a web page with hundreds of CSV formatted data extracts that people could “self-service data” based on their assigned role (security group). Although crude, it was one of the more popular pages on the corporate portal. The data extracts where indexed like a table of contents with descriptions that made data easy to locate. Data was downloaded with a point and click, so it was easy to access.
Challenges Facing Data Leaders
Although this previous example is 15 years old, it illustrates some of the critical challenges with which data leaders are struggling to provide data self-service or data as a service. Specifically, these challenges include how to:
- Publish curated data
- Organize data so it can be found
- Provide easy access to data
- Describe data in business terms
Today we have a bevy of tools that enable self-service data, data integration/preparation, analytics, BI, and machine learning capabilities. From the architecture perspective, containerization, microservices, DevOps, DataOps, and cloud services all provide infrastructure and processes to enable scalable and cost-effective self-service data and analytics. Weaving all these tools and technologies into an enterprise’s data ecosystem can be daunting, even for large, well-resourced companies.
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Self-Service data and analytics (think AI, ML, and Model Building) and self-service Business Intelligence require different mindsets. With self-service BI, we had the luxury of buying a single tool like Microstrategy or Tableau and just enabling self-service during implementation. The success depended primarily on how well you or your consulting partner implemented the device and how well it was governed.
A Different Mindset
However, with Data and Analytics, we have a set of complexities that we did not have with self-service BI. These include enabling and governing direct data access, providing tools to transform, prep, and cleanse data, facilitating analytical models being deployed to production, creating sandboxes in the cloud, and helping users connect a wide variety of analytical and AI tools to enterprise data.
At Perficient, we had the opportunity to guide a large number of organizations through the process of specifying and implementing data and analytics architecture. Through this vast experience, we have observed that companies that gain a significant return on their analytics and data investments have changed their mindset from “let’s implement a tool” to “let’s provide a service.”
Whether we call this Data as a Service or Analytics as a Service or any other name du jour really does matter. But it was the mindset to define a set of enabling services (that involve data and analytics) then continually improve these critical services. This mindset revolves around approaching your program from your customer’s perspective. Talking to your data consumer and understanding how they access and use data, what their challenges that impede productivity are all items with which the leaders of the Data and Analytics program should be familiar.
Approaching the data and analytics program from the consumer point of view will undoubtedly change your perspective. Instead of looking at implementing tools that make IT happy, successful programs view tools as a way of making your users happy – and rarely will that happen without a service or capability driven implementation.