Data Science

The key to building a data driven future

The key to building a data driven future

The world is powered by big data, forcing organizations to seek the best of the best in analytics, capable of organizing data and making big decisions in real time. The data practitioner that relies on old techniques will soon become obsolete in this fast-moving data driven world.

A growing number of data scientists are adding this new skill to their toolbox. This game-changing technology known to most data scientists as prescriptive analytics, mathematical optimization (MO) or even mixed-integer programming (MIP) is becoming an essential skill for the future data practitioner. Data Scientists are discovering that MO solves problems, answers questions, and delivers insights that machine learning (ML) cannot. Incorporating MO into your data science repertoire gives you the opportunity to utilize a broader range of applications, maximize the business value of your data and increase your overall impact on your organization.

ML & AI + MO = Complete Data Science Toolbox

ML makes predictions while prescriptive analytics (aka MO) makes decisions. When your problem involves complex tradeoffs between competing activities and allows for trillions of possible solutions, only MO has the power to find the best or optimal one.

The future Decision Scientist

While ML can enable you to make predictions, mathematical optimization MO empowers you to make decisions. When your problem involves complex tradeoffs among various (and often conflicting) business objectives and has an astronomical number of possible solutions, only MO has the power to find the best or optimal solution – which can be used to make optimal business decisions.

Better together

MO and ML are complementary technologies, and more and more companies are developing and deploying applications that combine MO and ML. MO technologies can leverage ML-generated predictions by using them as input for MO-based solutions and decisions. A good example of this is predictive maintenance: ML can enable manufacturers to predict when machine failures are likely to occur, and then MO can use these ML-based predictions to help create optimal maintenance schedules that minimize resource costs and production disruptions. Also, MO-based solutions can be utilized to help shape, retrain, and improve ML models – which can decay over time.

4 Reasons Why Data Scientists Should Add MO to Their Analytics Toolbox