Extra-condensed knowledge

This golden knowledge article from MIT SMR, based on solid business case research, outlines five common hurdles in business data science projects and how to overcome them.
CONTEXT

g-f(2)151 The Big Picture of the Digital Transformation, 3/1/2021, geniouxfacts, How To Succeed At Business Digital Transformation.
g-f(2)153 The Big Picture of Business Artificial Intelligence (3/3/2021) in a Single “g-f KBP” Chart

Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles.
The mistakes we identified invariably occurred at the interfaces between the data science function and the business at large.

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Condensed knowledge

CONTEXT

Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles.

Mistake 1: The Hammer in Search of a Nail

It’s not exactly rocket science to observe that analytical solutions are likely to work best when they are developed and applied in a way that is sensitive to the business context.

Mistake 2: Unrecognized Sources of Bias

The bigger problem for companies seeking to achieve business value from data science is how to discern sources of bias upfront and ensure that they do not creep into models in the first place.

Mistake 3: Right Solution, Wrong Time

The lack of synchronization between data science and the priorities and processes of the business.
To avoid it, better links between data science and the strategies and systems of the business are needed.

Mistake 4: Right Tool, Wrong User

There is a need to pay attention to how the outputs of analytical tools are communicated and used.
To generate full value for customers and the business, user experience analysis should be included in the data science design process.

Mistake 5: The Rocky Last Mile

Companies can involve data scientists in the implementation of solutions.
One bank in our study achieved this by adding estimates of the business value delivered by data scientists’ solutions to their performance evaluations.

The mistakes we identified invariably occurred at the interfaces between the data science function and the business at large.
This suggests that leaders should be adopting and promoting a broader conception of the role of data science within their companies — one that includes a higher degree of coordination between data scientists and employees responsible for problem diagnostics, process administration, and solution implementation.
This tighter linkage can be achieved through a variety of means, including training, shadowing, colocating, and offering formal incentives.
Its payoff will be fewer solution failures, shorter project cycle times, and, ultimately, the attainment of greater business value.

Category 2: The Big Picture of the Digital Age

[genioux fact produced, deduced or extracted from MIT SMR]

Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).

Type of validity of the "genioux fact".

Inherited from sources + Supported by the knowledge of one or more experts + Supported by research.

Authors of the genioux fact

Fernando Machuca

References

Why So Many Data Science Projects Fail to Deliver, Mayur P. Joshi, Ning Su, Robert D. Austin, and Anand K. Sundaram, March 2, 2021, MAGAZINE SPRING 2021 ISSUE, MIT Sloan Management Review, MIT SMR.

ABOUT THE AUTHORS

Mayur P. Joshi (@mayur_p_joshi) is an assistant professor in FinTech at Alliance Manchester Business School at the University of Manchester. Ning Su (@ningsu) is an associate professor of general management, strategy, and information systems at Ivey Business School at Western University. Robert D. Austin (@morl8tr) is a professor of information systems at Ivey Business School. Anand K. Sundaram (@iyeranandkiyer) is head of retail analytics at IDFC First Bank.