5 ways Data Science and Machine Learning impact business Posted on : Apr 14 - 2018

Data science and machine learning are having profound impacts on business, and are rapidly becoming critical for differentiation and sometimes survival. Being able to quickly categorize the potential impacts into one of five categories, and communicate their potential, will help data and analytics leaders drive better results.

The five categories of impact are:

Innovation: Foster new thinking and business disruptions based on data science

With their ability to frame complex business problems as machine learning or operations research problems, data scientists hold the key to unveiling better solutions to old problems. They may even reveal new problems and approaches that were previously unknown.

One example, popularized by the film and book Moneyball, showed how old ways of evaluating performance in baseball were outperformed by the application of data science. One baseball team used data science techniques to overcome its financial disadvantage. It achieved this by using analytics to identify high-performing players who other teams had overlooked using traditional methods, and therefore acquired their services at a relatively low cost. The result was that the team regularly beat higher-spending competitors in their league.

Another example is that of a multinational package delivery company, UPS. Its On-Road Integrated Optimization and Navigation (ORION) system used data science to figure out how to significantly change the routing of its delivery trucks using many new data sources. The impact was hundreds of millions of dollars of savings and an improved customer experience.

Exploration: Explore unknown transformative patterns in data

Data scientists should be encouraged to make “big data expeditions” where there is no clear objective other than to explore the data for previously undiscovered value.

For example, data scientists at a Japanese maritime services provider realized that when providing their traditional services for ship classification, they were collecting a valuable store of data that had great potential in other areas.

Applying the right analysis to this data meant that ship operators could reduce equipment failures and lifetime maintenance costs by 10%. This allowed the organization to quickly increase its market share by 20% when offering this value-added service to customers.

Prototyping: Challenge the status quo with radical new solutions

Human decision making is increasingly inadequate in a new digital world with an ever-expanding universe of data. Data science and especially machine learning excel in solving the kind of highly complex data-rich problems that overwhelm even the smartest person. The list of business or government challenges that data science can tackle is potentially endless. View More