Empowering application development teams with the best tools while creating a unified and highly flexible data layer still remains an operational challenge for the majority of businesses. Hence, data engineering is fast taking the center stage acting as a change agent in the way data is collated, processed and ultimately consumed. Not all AI/ML projects undertaken at an enterprise level are successful and this mainly happens due to lack of accurate data. Despite making generous investments in data analytics initiatives, several organizations often fail to bring them to fruition. Yet companies also end up spending significant time preparing the data before it can be used for decision modeling or analytics.

Decision modeling is the proven requirements gathering and documentation approach that bridges the gap -- between business process, business rules, and artificial intelligence (AI), machine learning, and predictive analytics technologies -- for true Digital Decisioning.

Sparkling Logic is focused on enabling business and data analysts to manage automate decisions better and faster – what they call Analytics driven Decision Management. Sparkling Logic was founded in 2010 and I have blogged a few times about their decisioning platform (most recently here). Customers include Equifax, Paypal, FirstRate, Accela, Northrop Grumman and others across a wide range of solution areas with a strong focus on enterprise customers. These enterprise customers are very focused on multiple projects, enterprise integration and supporting both on-premise/cloud deployments. The Product Portfolio now includes Pencil, a decision modeling and requirements tool, and SMARTS, their full lifecyle decision management platform supporting predictive models, data analysis, and expertise / business rules. SMARTS is available on premise, on cloud or embedded in another product and runs across Java and .NET deployments.