Data Science

Data science is the use of scientific methods, processes, and algorithms combined with technology to extract knowledge and insights from data assets. It requires a cross-disciplinary approach that incorporates mathematics/statistics & algorithms, programming, data engineering, analysis, visualizations, and storytelling. We complement our data science skills with domain expertise, allowing us to extend the value of your data in the right context. The benefits are more efficient and effective use of your data, creating better insights and empowering decision makers. Definitive Logic has the expertise to help you understand the most appropriate data science techniques to address specific business objectives.

Artificial Intelligence/Machine Learning

Machine Learning (ML) is a subset of Artificial Intelligence (AI). With AI focused on mimicking a human’s cognitive functions, it’s critically important to establish robust and ethical AI methods early in the analytics journey. ML is the application of AI to help computers learn without instruction, iteratively refining models by learning and improving without human intervention. We use a variety of techniques associated with AI that include neural networks, deep learning, heuristics, Support Vector Machine (SVM) algorithms, Robotic Process Automation (RPA), and Natural Language Processing (NLP).

Predictive Analytics

Predictive analytics leverages historical data to make calculated predictions about future unknown events. These predictions are based on models that are built, trained, tested, tuned, and implemented. Our success with creating more accurate predictive models is based on a combination of industry expertise and technical skills. We utilize feature engineering to create smarter, more relevant, and therefore more valuable datasets. We then employ techniques to extract meaningful and actionable information from the data. We apply techniques such as regression analysis (linear & logistic), classification, time-series analysis, and decision trees to separate noise from the signal.

Optimization

Optimization is the process of maximizing or minimizing a key metric (known as the objective function). Examples include maximizing profits, minimizing costs, or determining the best possible routes. In a climate where constrained resources can severely impact a business’s operations, it is important to make the best use of what is available. There is no room for waste in any part of the value chain whether it’s time, money, people, inventory, or transportation. We treat your business challenges as our own and empower our data scientists to build well-tuned optimization models that deliver the best outcomes possible given the constraints.

Use Cases:

• • Natural Language Processing (NLP) to reduce time and manual effort when processing unstructured files
  • Predictive Maintenance (PdM) to better manage assets while reducing risks and costs
  • Supply-Chain Optimization (SCO) to maximize efficiencies across manufacturing and distribution