A few years ago, in the earliest days of the analytics boom, a tech writer at Fortune magazine claimed that data was the new oil. While this is mostly accurate—it’s a highly-prized commodity—a better analogy for data is that it’s a renewable resource.

In contrast to oil, renewable resources promise never-ending profit. Unfortunately, they also have unpredictable development costs. Since data and analytics are revolutionizing almost every industry, companies are getting on board, and fast. They’re prioritizing data from every angle: tools, systems, privacy, and hiring.

Nonetheless, many businesses still want to know: what benefits will they see from an investment in data science?

Today, I’d like to talk about how data science can solve business problems.

A data science solution for every problem

Thanks to cloud computing and data visualization, we have an unprecedented ability to store, process, and examine large data sets. Using mathematics, statistics, and machine learning, we can use data to drive more accurate predictions and decision making. However, these tools mean nothing without a problem to solve.

Business needs are what drive data science. Matching these needs to data science capabilities early in the project management lifecycle helps to avoid distractions and diversions. To optimize your chances of success, your analytics implementation should have a clear, justified business plan and specific software capabilities identified.

Below is a list—a cheat sheet, if you will—of data science services and the business problems they solve.

Business Need Applicable Data Science Capabilities Outcome


Monitor day-to-day success of a business



Data warehouse, data integration, visualization, KPIs


Analytics hubs with agreed upon business metrics


Improve outcomes of a business process



Optimization or simulations


Information on focus areas that will improve efficiency and routing, usually in the form of labor or product allocation


Recommend the right item or action to a customer



Recommendation systems


Personalization leading to improved sales and customer satisfaction


Intelligent chat (conversational AI)



Natural language processing, AI


Rapid and direct customer service and sales coupled with reduced labor costs


Reduce fraud



Anomaly detection


Early warnings that lead to reduction in losses


Interpret market or consumer and business trends, then suggest actions



Statistical testing, forecasting, segmentation


Timely and relevant suggestions to empower strategic response


Determine optimal pricing and inventory strategies



Clustering, dynamic programming, statistical models, machine learning


Implement the greatest margin while staying in line with business constraints


Extract insight from complex data such as text, images, video, and/or audio



Natural language processing, AI, signal processing


Faster response to trends and events leading to better sales, improved customer satisfaction, and/or reduced waste


Set up a successful data science practice



Management experience in full stack, production-grade data science


Faster time to ROI of data science centers and divisions


Create a fast, robust software environment for a data science team to deploy their models and services



Technical experience in full stack deployment of CI/CD data science pipelines


Faster time to ROI of data science products within an organization

Some of these use cases may not apply to your organization or business problem. The most important takeaway is that data science serves your business—not the other way around.

Data science in practice: Making it happen

Data is the resource that drives analytics and enables businesses to make strategic decisions. But how do you go from mountains of data to practical insights?

A data science team.

Data scientists and engineers convert data into an array of directly actionable options. Then, you can use business expertise and knowledge of your customer to choose a course of action to achieve optimal ROI.

For example, if you suspect customer fraud in your business, data from relevant transactions can provide hints about when and where the fraud is occurring. Analytics and data science are the mechanism through which you figure out how to address the problem. A data science team will:

  • Understand your data. They’ll gather, evaluate, and monitor information related to your customers and interactions with your business.
  • Build predictive models to deliver recommendations. These models generate a recommended action (i.e. investigate) and a reason for the action (i.e. customer’s social security number doesn’t match their name).
  • Deploy model(s) and improve their accuracy to reduce false positives (bad for your customer) and reduce false negatives (bad for your bottom line).

These three steps form the standard progression for bringing a new predictive model into the business. In practice, strong data science management creates powerful results while balancing technical debt and speed. Technical debt is the amount of challenging to maintain or poorly supported software environments that a data science solution needs in order to function. Speed is the duration of time from concept to fully functioning solution.

The big picture

There are many technologies and people involved in taking a business from data discovery (finding patterns in data) to profit. At Logic20/20, our experience enables us to quickly choose the correct data science solution for any business problem. Further, we bring the maturity and expertise to manage data science on a direct path to effective business outcomes. Data science is applicable in any industry, from financial services to non-profits to agriculture. We’d love to talk to you about how data science can help your organization.

Like what you see?

Paul Lee

Anne Lifton is a lead data scientist in charge of deployment and development of data science models using Python, Kafka, Docker, PostGreSQL, Bazel, and R for production environments.