Data Scientist needs Strategic Thinking

By Kimberly Cook |Email | Jul 18, 2018 | 9729 Views

Having done training and mentoring for quite a while, I noticed something that is not taught in most data science curriculum that I have come across but in my opinion, its an essential business knowledge that the data scientist need.

Let us not dwell into the fact why data science curriculum does not include it inside but more of why it is important for a data scientist to know.

Insights & Feedback
In the very old days, when most things cannot be measured easily, a business owner or CEO can only know the success of its business strategy through the Profit & Loss Statement, and that is after the whole business strategy has been executed. This is definitely not ideal given that resources which are in extreme scarcity are expended during the strategy execution.

Given the rise of technology, what used to be intangibles can now be measured and this is where business intelligence shines. Having a good understanding of strategic management, the data scientist can propose possible metrics, metrics that can determine if the strategy is on track and if its not, be able to determine the reasons for it. Good understanding of how metrics have changed across time helps business to better understand the market and environment they are operating in, how efficient is the usage of scarce resources and best of all, how to improve on the next strategy.

Given that the progress of strategy can now be tracked, businesses can now conserve scarce resources if they found out that the strategy is not working out.

For instance, if a business is looking to improve on its brand image and worked on pushing a series of adverts on it. The business can propose hashtags and see how the sentiment is and how far the message has passed. Through the measurement of sentiment and how widespread the message is, business can determine the effectiveness of their marketing campaign. And if sentiment is measured across time, business can see how the market react to each new marketing element and put a hard stop to elements that are not ideal and extend marketing elements that works. This is a case of moving resources (marketing dollars) from the ineffective to the more effective.

Most importantly, with data now, business can easily seek the required feedback through putting the finger at the right spot (i.e. setting up the right metrics) and have a more efficient use of resources rather than have them expended at the end of a strategy execution, only to find out that the strategy is not effective.

Implementing Analytics
Having a good understanding of strategic management, the data scientist will have a better idea how data is collected and where machine learning models can be used to assist business to make quick and informed decision during the strategy execution.

For instance, the data scientist will be able to propose a cross-sell model and market basket analysis to help a supermarket sell more products through an email marketing campaign with fewer people irritated by junk mail.

Having a good understanding of strategic management, data scientist will understand and be able to help business extract more value from data collected, by proposing how machine learning models can be used in the existing processes to assist the execution of business strategy.

Actionable Insights
Being able to provide "actionable" insights is critical and essential for a data scientist that wants to provide value to the organization they are in. A good understanding of strategic management, helps the data scientist to understand the kinds of insights that are highly valued and actionable, and be able to think a few steps ahead.

Being able to think strategically helps the data scientist to continuously provide value through the provision of insights that can be acted upon, building up credibility along the way. (Heard of the boy who cried wolf? Similarly if a data scientist cannot provide actionable insights, no one will pay attention to him/her anymore).

In conclusion, I think strategic management is an essential knowledge a data scientist should have, as it helps to increase the value a data scientist can bring to the table.

If you are keen to understand how data and strategic management can come together, I suggest reading up on the Balanced Scorecard.

The article was originally published here

Source: HOB