Creating a Mix of Business and Data Science for Enhanced Productivity

by June 14, 2020

Data Science

In the wonderland of technology and advancements, there live two masters of success – Business and Data Science. However, both have been serving quite different purposes until now. With the rising demands and narrowed down bridges between running a business and achieving data-science success, the two have become more complementary to each other than ever before.

The difference between the two starts from their basic education. Where data scientists are extremely qualified in maths, programming languages, and STEM education, businessmen or business stakeholders have degrees of MBA and finance education. But if you have explored the recent pedagogy offered by different disruptive institutions, you would know that a person with the best in STEM qualities can also qualify as having business acumen.

Now more than ever the need has arisen that data scientists must possess the business knowledge in order to develop better and more functional business models to drive productivity and efficiency. Moreover, business leaders must set a collaborative ecosystem where IT teams, Data Science teams, and other organization’s departments must come together to offer success.

According to Forbes, many companies are trying to cultivate data science to gain a competitive advantage. Figuring out “how” is where they get tripped up. One of the simplest approaches to getting better at data science is by making data science approaches ubiquitous throughout the business, and by forming stronger partnerships between line-of-business people and data scientists. More on that later.

 

How can line-of-business people and data scientists partner more effectively?

There is a long-standing model available known as CRISP-DM, which stands for the cross-industry process for data mining. This model was created in 1996, and Douglas McDowell, Chief Strategy Officer for SentryOne, found it to be very effective. There have been lots of updates and variations, but here are the key steps:

 

Business understanding

Start by understanding your business’s challenges and what types of insights would provide benefits. Here is where a line-of-business person would give their data scientist a use case for analytics and its success criteria.

 

Data understanding

Here the business people, data scientists, and database administrator (DBA) should work together to identify the available data to support their use case, including the source of the data and if the data is complete and trustworthy.

 

Data preparation

Now the DBA, with input and direction from the data scientist, extracts and structures the data that machine learning will evaluate in future steps.

 

Modeling

The data scientist identifies and applies the right machine learning algorithms to the data.

 

Evaluation

Business people and data scientists work together to look at the data-mining results and determine whether the model meets the business objectives. If the result is not acceptable, they return to the “business understanding” step and cycle through again.

 

Deployment

Lastly, the business people work with IT and the DBA to determine a strategy for deploying the results. For example, they could integrate the model into a mobile app or a line-of-business application.

McDowell believes that line-of-business people and data scientists have much to gain by collaborating more closely on data science projects. Data is relatively easy to collect but harder to analyze. Right now, many line-of-business people are likely wishing they had better insights but don’t know where to begin. Data scientists may want to help but don’t have a grasp of the business problems.

By integrating data science more deeply into the business, and by developing a better working understanding of how data science works, including the CRISP-DM model, business people can be more effective partners and drive their data initiatives forward.