Mu Sigma, a leading global provider of decision science and big data analytics solutions, is announcing the launch of “Meta-software”, its new approach to analytical solutions for business problems. As a set of analytical solution frameworks, Meta-software operates as software building blocks that are adaptable to varying business problems through abstraction, generalisation and modularisation.
Meta-software is a powerful way to improve how businesses approach problem solving and is part of Mu Sigma’s new paradigm “Service-as-a-Software”, where software and services are more integrated to address increasing business complexity. By bringing man and machine together, Meta-software blends heuristic and algorithmic solutions, preserves the flexibility of customisation, 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 organisations,” said Deepinder Dhingra, head of products and strategy, Mu Sigma. “But the two main paradigms of problem solving have limitations – packaged software is lacking in flexibility because it requires well defined problems, 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 problems are similar. By abstracting different problem solving approaches in different business contexts at a macro level, generalising them to an adaptive solution framework across different use cases, and then modularising them as software building blocks, Meta-software can resolve the flexibility-scalability trade-off.
Meta-software’s analytical code can be deployed 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. It can deploy 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 two are configured and stitched together to solve business problems. Our Meta-software is made up of adaptive solution frameworks that address different business problems – recommendation, classification, forecasting, segmentation, attribution, optimisation, etc. – where the relevant techniques are wrapped in intelligent workflows and selection classes to enable better and faster solutions. Ultimately, 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.”