Democratizing Data with Self-Service Analytics
Authors: Jacquin Steven , Pawlewitz Jan , Doyle Aidan 
Published: April 2020

Abstract
Self-service analytics refers to systems where business users are able to access relevant data, investigate data history/relationships, perform statistical and other types of analyses, and prepare their own visualizations. This allows business analysts/users to do their job without requiring support from the IT organization, thereby facilitating data democratization.
Traditional business intelligence (BI) tools are not ideal for the task described above as they struggle to connect data from the wide range of industrial process data source types and integrate it into a model that is BOTH accessible to, AND understandable by, the broadest range of users in the organization. These platforms still require a degree of design and support expertise that is accessible to only a select subset of people in the organization; they do not fully enable data democratization yet.
An alternative approach is to use software that is designed to work efficiently with data ranging from real-time process monitoring, process historian, relational, web services, big data, structured data files, and many other structured and semi-structured data sources.
This paper describes an approach whereby operations intelligence software is used to connect disparate data sources, organize information, and provide visualization in a context that makes it accessible and understandable by users of any discipline in the organization. The openness and accessibility of operations intelligence software allows the layering on of advanced simulation and modeling, advanced analytics, and many other types of advanced information platforms, further extending the capability and comprehensibility of the information in any direction the user groups desire. It truly democratizes the data to empower the widest range of people to both contribute to the data AND benefit from it.
In order to address the challenge of democratizing access to data, we will review the first steps required to get there: data aggregation and contextualization. We will explore how this powerful tool set leverages the concept of contextualization to frame data housed in complex data models in different ways for the benefit of different audiences. We will follow through with extending this benefit of contextualization to provide access to users who want information from advanced data processing platforms, such as simulators and analytics platforms, without actually needing to have expertise in using these advanced platforms.
Finally, we will explore "self-service analytics." The proliferation of self-help BI dashboarding tools in the marketplace would lead one to believe that this is not a new concept, and indeed it is not new from the perspective of classic enterprise BI applications. What makes it exciting and new to this audience is the opportunities that are opening up to leverage these self-serve toolsets in the context of industrial owner operators. A properly implemented self-help toolset that is designed for, and caters to, the industrial business will enable engineers and non-data science experts to conduct their own investigations using process data as well as any other type of data. As a result, industrial business users are easily empowered to extract and understand the substantial value hidden in the insights their data contain.
The architecture required to provide this solution, along with the many advantages it provides, will be discussed in the paper.