How data science solves business problems
 
 

How data science solves business problems

Data science teams can help solve business problems

 

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:

 

  • 1. Understand your data. They’ll gather, evaluate, and monitor information related to your customers and interactions with your business.
  • 2. 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).
  • 3. 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.

 

 

 

Need to fix a business problem?

 

Learn more about data teams.

 

 

 

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.

 

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