Building on a new paradigm called Software as a Service introduced by Mu Sigma in November 2016, where software and services are more integrated to address increasing business complexity, Meta-software is a powerful way to improve how businesses approach problem solving. By bringing man and machine together, Meta-software blends heuristic and algorithmic solutions, preserves the flexibility of customization, improves the efficiency of software development and accelerates the deployment of solutions.
“Software has been a boon to enabling and scaling analytics for decision support in large organizations,” said Deepinder Dhingra, head of products and strategy, Mu Sigma. “But, the two main paradigms of the problem solution space have limitations – packaged software that enable scale for repeatable, well-defined problems are lacking in flexibility, and the traditional analytical libraries and languages for developing custom solutions to solve specific business problems are not scalable.”
Meta-software is a new approach developed by Mu Sigma to address the twin limitations of flexibility and scalability in traditional approaches. It suggests that, when viewing business problems and classifying them by their underlying nature, logic and math, many business problems are similar. By abstracting different problem solving approaches in different business contexts at a macro level, generalizing them to an adaptive solution framework across different use cases, and then modularizing them as software building blocks, Meta-software can resolve the flexibility-scalability trade-off.
As an analytical source code, Meta-software can be deployed as a capability within an organization across different functions and can be used in a variety of ways to solve ad-hoc analytical problems, power automated analytical workflows, be deployed as micro-services and application program interfaces (APIs), and enable decision support applications and dashboards.
“When designing software for solving problems, although it is easy to find techniques and algorithms, there is a need to apply an intelligence layer and framework on top of these algorithms that can adapt to the context and content of different use cases,” said Dhingra. “Today, the differentiation is no longer just in the techniques and algorithms, but in how these algorithms and techniques are configured and stitched together to solve business problems. Our Meta-software are adaptive solution frameworks that address different business problem classes – recommendation, classification, forecasting, segmentation, attribution, optimization, etc. – where the relevant techniques are wrapped in intelligent workflows and selection classes to enable better and faster solutions. This is software written by decision scientists for decisions scientists.”
“In today’s world of increasing complexity and change, the Software-as-a-Service model is no longer sufficient. Instead a new adaptive model of Service-as-a-Software is needed in which both the nature of services and nature of software need a re-think,” said Dhiraj Rajaram, founder, CEO and chairman, Mu Sigma. “Over the past decade, we have pioneered a unique scalable talent model by creating interdisciplinary decision scientists, as opposed to just data scientists. With Meta-software, we are now pioneering a unique software model that enables the Service-as-a-Software paradigm.”