muPDNA for Structured Approach to Defining, Representing and Encoding Intelligence
muPDNA for Structured Approach to Defining, Representing and Encoding Intelligence : muPDNA offers a structured approach to defining, representing and encoding intelligence about business problems so that they can then be addressed using data and analytics. The dynamic nature of business creates the need for most organizations to solve multiple problems on a daily basis.Mu Sigma, is a pure-play decision sciences and analytics firms, that helps companies institutionalize data-driven decision making and harness Big Data. Mu Sigma solves high-impact business problems in the areas of Marketing, Risk and Supply Chain across 10 industry verticals. Mu Sigma has driven disruptive innovation in the analytics industry with its interdisciplinary approach combining business, math and technology, and its integrated decision support ecosystem comprised of technology platforms, processes, methodologies and people.
These business problems often start off as muddy or fuzzy before becoming clear -- for instance, "What can we do to stop declining sales?" This makes it challenging to define, articulate and represent business problems. Traditional problem-definition frameworks are not comprehensive enough for analytical problem definition. muPDNA is the first software to integrate all aspects of the business problem required for data-driven decision making, such as current state, desired outcomes, gaps/triggers, key questions, factors, hypothesis and data elements into one easy-to-use workbench that provides the right emphasis on design, hypothesis and representation.
muPDNA was originally developed for Mu Sigma's internal use -- its own decision scientists have used it for 100+ client engagements over the past few years. Having recognized a market need for this type of product, Mu Sigma invested in assetizing this framework to help enterprises address business problem definition for analytics.
"Einstein famously said, 'If I had one hour to save the world, I'd spend the first 55 minutes defining the problem,'" said Dhiraj Rajaram, CEO of Mu Sigma. "muPDNA is all about framing and representing the problem. In our work with Fortune 500 clients, we've seen so many companies struggle to correctly define and represent the problem they are trying to solve. They usually end up with incomplete questions, factors and hypotheses. We developed muPDNA to make this process more efficient, so enterprises can get to the heart of the matter quickly, and then move on to the problem-solving stage. Just like every human being has an underlying DNA that encodes their key traits, so does every problem. These business problems can be encoded in the muPDNA."
For instance, a retail chain that's experiencing slowing sales in a region may feel that a new competitor's presence is affecting foot falls and respond with only that factor in mind. This runs the risk of not considering other factors. muPDNA nudges and guides users through the process of considering other potential factors, to ensure that an enterprise is considering every possible angle, leading to higher probability of discovery and reaching the right conclusion.
Rajiv Narang, Executive Director, Global Marketing Decision Sciences at Mu Sigma customer Dell, said that assetizing this approach to problem definition is a great first step that will help companies ensure they are on the right analytics path. "If you scope the wrong problem, or start investigating a symptom rather than a problem, the rest of your analysis will be flawed," said Narang. "Working with Mu Sigma, we've been through their muPDNA methodology several times, and have found it's a fantastic way to ensure we're exploring the right issues, relatively quickly. We're excited to see Mu Sigma launching what was once an internal asset into the public domain."
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.