If there’s one thing that gives a job an indefinable allure, it’s that no one really knows data scientist exactly what you do, or how you do it; they just know you do something important that generates impressive results.
A recent article in the Harvard Business Review, for instance, claims the data scientist is the sexiest job of the century. It’s not a job you’ll find advertised, nor can you get a degree in it, but as you might have guessed, data science is all about transforming data into business value using math and algorithms.
A new breed
Data scientists combine the analytical capabilities of a scientist or an engineer with the business acumen of the enterprise executive. According to SAP’s chief data scientist, David Ginsberg, data scientists are people who know how to ask the right questions to get the most value out of massive volumes of data that solutions like SAP HANA unlock. They are able to articulate fundamental questions using the business language of a specific industry:
How can we quickly calculate alternative passenger itineraries after major flight schedule disturbances?
How can we achieve the desired trade-off between profit and market share?
How can we pinpoint fraud across disparate databases and massive volumes of data?
Once the business challenge has been identified, data scientists develop models and run simulations to find the right solution and identify the right tools and platforms needed to solve the issue. So for companies grappling with big data, which includes nearly enterprises of all sizes and industries, it’s crucial to have access to teams like SAP’s Data Science organization, who retain this very special skill.
Data scientists are changing the way things work. For example, according to SAP Performance Benchmarking, 68% of retail firms have no predictive modeling software or complex optimization techniques for analyzing big data. But with current merchandising trends putting the customer at the center of a model that includes mobile, store, and web sales along with loyalty programs, the optimal management of the forecast, merchandise plan and customer preferences is more important than ever.
Here’s where seasoned data scientists can help retailers increase customer loyalty by finding new meaning hidden in the volume of data. They can now analyze billions of rows of point-of-sale data to identify stores and categories that drive sales. They can manage brand by maintaining fresh, seasonal products in an exceedingly competitive market, and they can also adapt in real-time to identify key changes in the marketplace.
How it works
Alliander, is a Dutch utilities company providing electricity and gas to 3.5 million people. In order to optimize energy transmission, integrate renewables and manage dynamic assets, Alliander dramatically increased the number of sensors throughout the grid, which results in much greater data volumes. Working with SAP’s Data Science team, they are now running in real time to gain insight into the data generated by their operations. Alliander can now monitor their transformer stations, automatically detect sensor problems and forecast the future power load. In the past, forecasting and grid optimization was something they only did once a year because the process took ten weeks. Now they do it monthly because it only takes three days with. In fact, Alliander now prides itself on being a data company rather than just a utilities company.
Clearly, data science is no longer a backend function. It’s now used on the front lines of business where experts make decisions at the highest level.
[divider scroll_text=”Back To Top”]