From healthcare to retail and every sector in between, a strong data science strategy can be a transformative organizational asset. As businesses employ strong analysis techniques, harness technologies like artificial intelligence and machine learning, and leverage both statistics and software, they can design and execute plans, projects, and programs for the good of their companies and customers.
While individual industries require unique approaches, there are several common components of successful data science business strategies. These components include applications and tools, algorithms, and processes—all of which are used to provide key insights, drive data-informed decision making, and contribute to company success.
Applications and Tools
Data science applications and tools empower organizations to discover important information about their products, services, employees, customers, and competition. Data science applications and tools such as pattern recognition and predictive modeling organize and analyze data in useful, understandable ways.
One of the foundational purposes of data science is identifying patterns in data sets. This application of data science has uses across all sectors of industry, providing keen business intelligence to company leaders and key stakeholders.
Consider how pattern recognition has been leveraged in the case of three real-world organizations from the agricultural, oil and gas, and technology industries:
Agriculture: The Small Robot Company built a robot that applies pattern recognition to identify and eliminate broad-leaved weeds, keeping crops healthy and unfettered as well as freeing up time, energy, and human capacity to be spent on other tasks.
Oil and gas: Operational AI company Falkonry leveraged pattern recognition applications to identify operational patterns that preceded critical failures in compressors and turbines, empowering their client to predict failures six weeks in advance and prevent losses that had been costing the company an average of $300,000 per incident.
Technology: The world’s first audio word processor, Descript, adopted Google Cloud’s Speech to Text tool that recognizes speech and patterns in speech, providing completed transcripts in less than five minutes with up to 95% word accuracy.
Predictive modeling tools are used to project future outcomes through data analysis. Predictive modeling, also referred to as predictive analytics, is an element of data science strategy employed in every industry, from ticket sales to food and beverage production.
Here are just a few examples of predictive modeling at work:
Ticket sales: StubHub, the world’s largest ticket marketplace, leveraged predictive analytics tools to study customer-related data, resulting in the ability to calculate 180 million customers’ lifetime value or propensity and reducing fraud issues by up to 90%.
Public schools: Des Moines Public School District leveraged a predictive analytics “dropout prevention” model, empowering teachers to monitor student performance, quickly identify at-risk students, and adjust their teaching methods.
Food and beverage producers: Industry leader Kerry leveraged a predictive analytics artificial intelligence tool to reduce the concept generation timeline from four to six weeks down to five days, and reduce the ideation-to-commercialization product timeline from six to nine months down to less than two months.
Part of constructing a well-formed data science business strategy is identifying the correct algorithms to use at the right time. In order to do so, data scientists need to be familiar with the most popular machine learning algorithms so that they can choose the best option for the project at hand. When used effectively, algorithms can provide key insights and promote effective, data-driven decisions.
Criminal Justice: When the New Jersey Court System discovered that 15,000 defendants had served jail time while awaiting charges because they could not afford to pay 10% of their assigned $2,500 bails in order to be temporarily released, the court system decided to implement an algorithmic solution to manage the risk of pretrial defendants. Data science algorithms empowered the state to reduce its jail population by 40% with no measurable crime rate increase and saved the courts $10 million.
Financial Services and Insurance: Algoan, a financial services and insurance company that offers fully automated lending processes to banks and other financial institutions, implemented machine learning algorithms to simplify and improve the credit scoring and loan application process. As a result, lending decisions can now be completed in five minutes as opposed to five days while maintaining efficiency and security.
Consider a few more examples of data science algorithms making a difference in the marketplace:
Healthcare: The American Cancer Society has used deep learning algorithms to analyze images of breast cancer tissues 12 times faster than before while also enhancing quality and accuracy.
Technology and Manufacturing: Predictive maintenance technology company Augury employs algorithms through artificial intelligence and the Internet of Things to reduce machine failure by 75%.
Media and Entertainment: Bonnier Publications leveraged a machine learning algorithm to model consumer behavior and better understand the customer journey, which informed company decisions that drove e-commerce conversions up by 18%, brought in 50,000 new subscribers, and reduced banners by 90%.
From decision trees to linear regression, algorithms are a key component of a successful data science strategy.
Data science incorporates six processes, which can also be thought of as six steps in a singular process. Those processes are discovery, data preparation, model planning, model building, operationalizing, and communicating results.
The purpose of the discovery phase is to gather data that is relevant to the business question that needs an answer.
The first step in a data science process is acquiring all the necessary data. Data scientists may use data discovery software and tools that provide graphs, charts, and datasets for exploration. Oftentimes these tools are part of a business intelligence solution that will provide information for at least one of the three major data discovery categories: data preparation, visual analysis, and guided advanced analytics.
In the retail space, a store in the discovery process may collect data on customers, such as conversion rates and retention. A hospital may study their time-per-appointment or rates of repeat visits for minor health problems. Manufacturers might analyze data that speaks to a product life-cycle, repairs, or logistics.
Datasets often have issues that need to be addressed before they can be further analyzed. Inconsistencies like poor formatting or blank columns are cleaned up during the preparation phase so that the data scientist can pursue high-quality predictions.
Once the data is ready, data scientists choose a method and technique for determining the relationship of variables. Data scientists use tools and programming languages such as SQL analysis services, statistical formulas, and R during this part of the process.
Data scientists build models for training and testing that employ various tools and techniques to produce the necessary outcomes. Techniques such as classification are applied to the training data set. The data scientist will then use programming languages like Python to test the training dataset against the testing dataset.
After the data scientist has tested the model sufficiently, it is time to put it to work in the real world. The model is deployed during the operationalizing phase.
Did the model answer the business question once it was operationalized? What were the key findings, and are they useful to the organization? These are the types of questions that are posed at the end of the data science process so leaders can determine if the project was successful. If not, the data scientist can set out to modify the model or, if so, move on to answer the next question. Results may be communicated through the use of tools like dashboards and other visual representations that share the findings clearly and accurately.
Solve Real-World Problems with Data Science Solutions
Do you want to work with large amounts of information in order to produce key insights? Are you interested in developing a strong data science strategy that will make a difference in the marketplace? The Worcester Polytechnic Institute Master of Science in Data Science Online degree program will prepare you to do just that.
Whether you have a background in data science or another field, you can take the necessary steps to become or advance your career as a data scientist at Worcester Polytechnic Institute.
The WPI Master of Science in Data Science Online degree program features built-in bridge courses. The program takes place entirely online and features thirty credit hours of coursework that will teach you how to master the skills you need to pursue a career as a data scientist, including:
Programming and math foundations: develop fundamental skills in computing languages, programming concepts, design and analysis techniques, algorithms, statistics, and linear algebra
Data science methods and technologies: learn how to create, manage, and analyze large-scale databases, use relevant statistical methods such as predictive modeling and clustering, and understand machine learning
AI & machine learning or Big Data analytics: choose between these two specializations, or build your specialization from a variety of electives, including Business Intelligence, so that you can tailor your degree plan to the career you plan to pursue.
By earning your data scientist degree at WPI, you will become part of the alumni family at a prestigious, respected university that is ranked:
#4 on the U.S. News & World Report list of National Universities Where Grads Are Paid Well 2021
#5 in Best Career Services by The Princeton Review
Among the top 25 STEM Colleges by Forbes, Top 60 Most Innovative Schools by U.S. News & World Report, and Top 30 Best Value Colleges by Payscale.com.