Prior to decision support systems, organizational leaders relied heavily on a combination of their experience and professional training, and applied those to thoughtful use of the advanced insights generated by a data analytics platform. Decision support systems systematize that by taking organizational data, analyzing it, and presenting it for use in company decision making.
This DSS approach enables powerful augmented analytics or modeling to make analysis recommendations and game play the outcomes of different scenarios. By varying considerations, outcomes can be more accurately predicted and business decisions made based on the best available information. In this way, DSS supports both predictive and prescriptive analytics.
Three key elements that characterize a decision support system framework are model management, organizational data (your knowledge base) and user interface. Let’s briefly explore each.
Model Management: To make effective decisions, especially those made on an ongoing basis, it’s crucial for companies to develop key performance indicators (KPI’s) from which to evaluate performance against goals, and measure improvements over time. These KPI’s then form the decision criteria for the information models used to guide decision making. Having models provides the backbone of consistency every business needs to sustain success. Models can be leveraged by formally coded rules in DSS or prescriptive analytics software or by analysis using a BI platform.
Organizational Data or Knowledge Base: Before any DSS can be used, raw data must be transformed into clean, accurate, and up-to-date information. The graphic below illustrates how different types of data are combined, cleaned and transformed into standardized formats. The data is then stored in a repository such as a data lake or data warehouse using a governed data catalog.