This article first appeared in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to receive our feature articles, or follow us @DataMiningApps. Do you also wish to contribute to Data Science Briefings? Shoot us an e-mail over at firstname.lastname@example.org and let’s get in touch!
The democratization of analytics is apparent when you consider who is involved these days. SAS has been doing statistical analysis for 40 years (without the aid of big data in the early days) and had algorithms for machine learning in its portfolio long before it became a buzzword. In the early years, we spoke with specialists (mathematicians, statisticians, and, now, data scientists). That has changed in the last five to ten years. Lately, we have been working directly with the departments for which the data is relevant.
When algorithms compose music…
Today, we not only have vast quantities of data, we also have numerous possibilities for analyzing it all. Algorithms are capable of learning to recognize people as people on images and even to recognize them as specific individuals. The formula behind it can be put simply as: data + algorithms + machine learning = artificial intelligence (AI).
The formula behind it can be put simply as: data + algorithms + machine learning = artificial intelligence (AI).
People are often surprised at what machine learning is already able to do. AIVA (artificial intelligence virtual artist) generates works of classical music by way of algorithms, the app Replika copies the behavioral patterns of a friend, and the Kelly Burger robot cooks up hamburger patties, recognizes when they are done, and even develops new recipes.
In the B2B segment, applications are found across industries and fields: customer segmentation, fraud detection, risk analysis, predicting employee attrition, and cybersecurity are just a few examples. Hasn’t that sort of thing been around for a while? Yes, but the new methods deliver better results.
The new possibilities make for new use cases: proactive health management instead of health insurance (or rather, sickness insurance), automatic handling of insurance claims using image analysis. That all sounds pretty cool but the question remains: Is there any money to be had in machine learning?
Companies have to integrate analytics into their business processes instead of just plugging the results of analyses into PowerPoint presentations. Despite many investments, both in innovative and legacy AI environments, most AI projects outside of the high-tech industry are stuck in the prototype phase and seldom deliver the expected business value. The primary challenge is to operationalize AI applications, embedding them into enterprise business processes.
During the SAS Platform roadshow on 29 March, we will zoom in on how the SAS Platform can be used to develop managed, governed AI applications using AI technology. We will show how SAS supports the industrialization of AI applications within the enterprise and how SAS can participate in the new innovative ecosystems.
Furthermore, Business Lines struggle to process analytics insights in a timely manner and the data and analytics infrastructure is often inefficient and costly.
To cope with the high business demand for valuable insights, the lack of time and resources from analytics practitioners and the need for IT to get a platform they can trust and share, SAS offers an open and industrial platform to “put analytics into action”. The SAS platform is best suited to deliver enterprise analytics across the organization and support the complete lifecycle from discovery to build and deployment to production in a diverse business context.
This, and great customer stories (one from SciSports for the football lovers) is on the agenda of the SAS Platform Roadshow in Hotel Bloom in Brussels on 29 March.