Data Science is often misunderstood by students seeking to enter the field, business analysts seeking to add data science as a new skill, and executives seeking to implement a data science practice. This article aims to clear up the mystery behind data science by illustrating the sequence of steps to go from a business problem to generating business value using a data science workflow. Once data science is understood, we can take steps to learn data science skills that will generate the most value and/or better make strategic investments in building a data science practice.
Overview
In this article, you will:
Understand what data science is
Learn how data science generates value for an organization
Learn how to go from business problem to business value
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The Mystery & Confusion
Data Science is a mysterious term to many, but why?
Students see data science as machine learning - 100% of the time (this is drastically disproportionate to reality). In reality, Machine Learning (or Modeling) is about 5% of your time. The rest of the time is spent:
Understanding the Business Problem: Communicating with Domain Experts (20%)
Working with Data: Cleaning, Manipulating, Visualizing, Processing, Transforming, and Understanding (60%)
Communicating Results: Reporting, Slide Decking, and Building Distributed Applications (Predictive Decision-Making Tools) (15%)
Executives and business professionals see data science as a new technology that could benefit their organization, but the connection between business problem and business value is not well understood. Fortunately, the reality is that large businesses:
Have many customers - The customers churn, generate sales, drive forecasts
Make many products and/or services - The products are linked to quality, lead time, and inventory
Have many suppliers - The suppliers affect lead times and serviceability
Have data - The data provides a means to measure business drivers and is the fuel for data science
This combination of business-drivers - customers, products, inventory, suppliers, and more - with a wide array of internal and external data available makes data science a competitive advantage to organizations that can effectively implement it.
Making Better Decisions Generates Business Value
The goal for any Data Science Practice (Data Science Team) is to enable the rest of the organization to make better, data-driven decisions. Therefore, a Data Science Practice is a support role (similar to IT) that allows the organization to function better. The Data Science team can add a lot of value very quickly - through better decision making.
A simple example illustrates my point - An organization that does $500M in annual revenue but has a customer churn rate of 10% loses out on $50M in revenue/year. If a data science practice can identify the issue, predict which customers are going to churn, and implement strategies that enable the workforce to targe the customers with retention strategies, the team can effectively reduce the churn rate 20%.
An organization that does $500M in annual revenue but has a customer churn rate of 10% loses out on $50M in revenue/year
In monetary terms, a reduction in churn of 20% equates to an annual savings of $10M. Over 5 years, this is $50M in savings generated from the Data Science Practice working with the decision makers (e.g. Sales, Marketing, Production).
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