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Data Science 2.0: From Outputs To Outcomes

Data Science 2.0: From Outputs To Outcomes

  • Bill Schmarzo emphasised data is an economic asset and not a technology byproduct.

Bill Schmarzo, the former chief innovation officer at Hitachi Vantara, presented a  talk, titled ‘Data Science2.0: From Outputs to Outcomes’, at SkillUp 2021. Bill said data science is going through a critical evolutionary phase.

In his presentation, he talked about the Big Data Business Model Maturity Index. He said the data scientist team should understand how to provide value to the company. The first step of this process is the value engineering framework — how the organisation makes money and what the business is trying to accomplish. This phase is called a business initiative. 


With the help of advanced analytic tools, data scientists can arrive at granular and well-informed results to inform decision-making. 

Data as an economic asset

Bill emphasised data is an economic asset and not a technology byproduct. He said, “It is not the exhaust of the business, but it is the fuel that drives the economy of the 21st century.” 

He presented characteristics of data as an economic asset. 

Bill also spoke about Economic Value Curve that measures the relationship between a dependent variable and an independent variable to achieve a particular outcome. However, the law of diminishing returns shows that additional spending brings in only marginal improvements. Nanoeconomics play a vital role in transforming such an economic value curve. 

Think like a data scientist

Bill also outlined steps to inspire business people to think like data scientists:

  1. Identify business initiative
  2. Identify stakeholders
  3. Identify analytic entities
  4. Identify use cases
  5. Identify data sources
  6. Group metrics into scores
  7. Identify recommendations
  8. Map scores to recommendations

Design thinking

Bill provided insights into the design thinking process. The process includes phases like: 

  • Empathise
  • Define
  • Ideate
  • Prototype
  • Test

“The process we go through for design thinking is very similar to the process a data scientist goes through in machine learning,” he said. Design thinking involves observation to discover unmet needs within the context and constraints of a particular situation. On the other hand, machine learning is a method of data analysis that automates analytical model building.

Bill provided a roadmap to help organisations take the first step to data monetisation.

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