How the Economics of Data Science is Creating New Sources of Value | 7wData

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There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let’s review some of those driving forces.

Artificial Intelligence holds the economic potential to drastic drive industry and business model disruptions. But having AI technologies is not sufficient, especially when most AI technologies are open source and readily available to everyone.

Data is the source of economic potential, and the Data Lake is a source of latent value.However, data, like oil, needs refining in order to turn it into something of value, and that’s the role of Data Science.

Data Science requires a highly iterative, rapid exploration, rapid testing environment that supports a fail fast/learn, continuous learning development approach that seeks to discover the drivers for success. Design Thinking is a complementary discipline to Data Science; the secret sauce that fuels that discovery, exploration, ideation and validation process between the data scientists and business and operational subject matter experts.

Data Science requires more than just data scientists. Data Science requires a team that also includes data engineers (who are responsible for accessing, assembling, cleaning, aligning, normalizing, enriching and blending the data sources) and the business and operational subject matter experts (who have the tribal knowledge that guides the data science development process).

Economics, when coupled with Data Science and Design Thinking, provides the frame – the connective tissue – against which to focus financial, technology and human investments in order to create new sources of wealth (value).

The Big Data Business Model Maturity Index provides a benchmark and a roadmap for helping organizations to exploit the economics of data science to optimize business and operational processes, mitigate risk, uncover new revenue streams and create a more compelling customer / operator environment.

The convergence of connectivity, flexible automation and the culture and economics of data science are the rocket fuel for the digital transformation that will differentiate winners from losers during the Fourth Industrial Revolution.

“Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035.

Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth).  But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?  The equation for deriving and driving economic value isn’t having the Machine Learning, Deep Learning and AI frameworks, but is found in the organization’s ability to become more effective at leveraging data with Data Science to power the organization’s business and operational models.

Data Science is the Data Lake Monetization Engine If data is the source of economic potential, then the data lake is a source of latent value (similar to how oil is a source of latent power).