7 Reasons Your Team Should Be Leveraging Data Science

Michael Segala
April 20, 2017
Regardless of where your organization sits within the business, data science has the potential to positively transform the way you do your job. A data scientist is a rare blend of computer scientist, statistician and subject matter expert. These unique individuals have the ability to manipulate big data, analyze that data and provide an understanding of how to make that data actionable. While popular wisdom often limits data science to certain business sectors like marketing or IT, successful businesses can leverage data science across all of domains. In this article we’ll share seven lessons that data science can teach your business and hopefully entice you to engage a data scientist in your network or outside of it.

1. Pinpoint Marketing With Existing Customer Data

In a modern marketing department, it's hard not to have at least a passing familiarity with data science and machine learning. By training predictive classifiers to predict which customers in your database are most at risk to stop using your goods/service or segmenting customers based on shared characteristics, businesses can optimize profitability by pinpointing likely targets for ad campaigns and new services. Data Science graph

Using a clustering algorithm, we can identify three distinct customer subgroups

(Image is CC BY-SA 3.0)

2. Detect Fraudulent Transactions

Using similar methods to governments looking for “needle in a haystack” online terrorist threats, businesses can use models to identify fraudulent activities within their historical clickstream or transactional databases. For instance, such a predictive model may use everything from geographic location to unusual sequences of events to calculate the risk of fraud for a given transaction based on how much it stands out from all other transactions on record

3. Forecast Costs in New Product Development

Professionals focused in product development, for instance, can identify customer preferences based on existing product sales data to predict the relative levels of demand for proposed products. In addition, they can build models using existing production data to estimate the costs for new product features, allowing decision-makers to more accurately weigh the risks versus the rewards.

4. Upgrade Overall Workforce Performance

Human resources departments can also successfully leverage data science to drive workforce performance. For example, one company found a patterned relationship between turnover and retention behavior of employees and pay raises: High performers tended to stay if they received high raises, whereas modest raises often pushed them to leave. By making a series of predictions using models of overall profitability and performance relative to turnover and pay raises, the company discovered that paying high performers more than the current standard raise would prevent workers from leaving, while still increasing overall profitability of the company despite the increased raises.

5. Predict Future Sales

Sales managers can also utilize a data science approach to forecast future sales based on past results, taking into account variables like average deal size, time and engagement. In addition, these models can help managers identify underperforming sales reps in any of these variables and coach them on how to increase their chances of closing a deal, increasing profit as well as employee performance. Sales Chart

Forecasting sales over a three-year period from http://mathematica.stackexchange.com.

6. Train Artificial Intelligence to Perform Routine Tasks

Accounting software already performs many routine tasks without the need for human interaction. However, experts argue that AI could enhance automated accounting even further through real-time learning from digital data. In turn, the AI could deliver insights to those who request it based on all the financial data available to it. For instance, in much the same way you interact with Siri on your iPhone, you could ask an Accounting AI to automatically generate reports based on the latest financial data, or identify when specific bills are due. Employees would then be freed from time consuming data-entry tasks, allowing them to focus on high-level decision making and financial strategy.

7. Optimize Supply Chain Planning

Companies often leverage data science techniques to analyze their supply chains and determine the optimal way to meet customer requirements in light of information about supply chain facilities, transportation lanes/capacities, profit requirements and more. Amazon, for instance uses a complex series of machine learning algorithms from customer preferences, locations and so on, combined with simulation to identify from where and how packages should be shipped to customers. Similar approaches can be used to optimize other supply chains as well. Regardless of industry, business department, company size or organizational chart, data science is transforming the way modern companies operate. By partnering with talent platforms, companies can now access on-demand top-tiered data scientists to keep them ahead of the curve and help transform their business. Interested in learning more about how Michael helps companies leverage data science to improve overall business performance? Check out his profile on Catalant.

Meet the Expert:

Michael Segala is the CEO and co-founder of SFL Scientific, a data science consulting firm that specializes in big data solutions. His firm leverages advanced machine learning and analytics to provide insight into numerous industry-spanning problems, from healthcare and pharmaceuticals companies to insurance and financial institutions. Before founding SFL Scientific, Michael worked as a principal data scientist in several well-known tech companies, including Compete Inc. and Akamai Technologies. He holds a PhD in Particle Physics from Brown University.
Featured in: Expert Insights

About the Author

Michael Segala

Michael Segala is the CEO and co-founder of SFL Scientific, a data science consulting firm that specializes in big data solutions.

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