Recent Posts by Michael Chen

In a world filled with global manufacturing and shipping, an organization’s supply chain is both its lifeblood and its biggest hurdle. So many variables seem independent of each other due to the fact that they live in separate organizations. However, everything within the supply chain is connected, and a single hiccup or incident can create a ripple effect that potentially derails critical manufacturing or fulfillment needs. And in today’s age of instant communication and...

As a user of Oracle Analytics Cloud, you already have access to one-click visualizations and reports. These self-service analytics make it easy to quickly handle data preparation, discovery, and visualization for users of all kinds: business users, citizen data scientists, managers, and other people without the in-depth training of a professional data scientist. In fact, that’s one of the true benefits of Oracle Analytics Cloud: its intuitive user interface allows anyone to...

Does your finance team struggle with the basics of data? Data can transform the way your organization understands revenue, allocates resources, establishes budgets, and plans for the future—but only if you can properly analyze that data. And analyzing data doesn’t start with actually crunching the numbers, but several steps before. To get to the stage where you can analyze data, create reports, and derive insights, you will first need to complete some very important data...

Before data scientists begin exploring their data with analytics tools and reports, they must complete data preparation as an essential first step. But what exactly does that mean? Consider how a chef uses the technique “mise en place,” which translated from French means “everything in place.” For the process of cooking, this means getting all ingredients measured and ready, mixing bowls and tools out and available, and vegetables all cut up. Only when that work has...

Note: This post originally appeared on the Oracle AI & Data Science Blog. The world of big data encompasses a lot of buzzwords, and terms like “big data,” “artificial intelligence,” and "data science" have gone from being tech-centric terms to things executives and business analysts discuss. Among the next wave of terms is “automated machine learning,” which is frequently shortened to AutoML.  AutoML automates common machine learning steps that are often repetitive and involve...

By Michael Chen, Senior Manager, Product Marketing The world of big data encompasses a lot of buzzwords, and terms like “big data” and “artificial intelligence” have gone from being tech-centric terms to things executives and business analysts discuss. Among the next wave of terms is “automated machine learning,” which is frequently shortened to AutoML.  AutoML automates common machine learning steps that are often repetitive and involve a good amount of trial and error on the...

Machine learning has been a paradigm shift for companies and organizations, providing opportunities to answer their business questions at previously unachievable speeds. With incoming heavy volumes of data from a growing number of data sources—ranging from social media feeds to Internet of Things (IoT) devices—machine learning provides a cost-effective means of both deriving insights from data and simply processing it in a resource efficient manner. While organizations...

What’s the key to successful data science? While it’s true that so much of it stems from the various people in roles up and down the workflow—the IT staff that put together the infrastructure that collects the data; the data scientists who build the models; and the business users who use the models to derive insights—the truth is that data science involves massive amounts of data. On a micro scale, each person is an equal link in a very long chain that goes from...

Apache Spark is one of the world’s most popular—and powerful—engines for processing big data analytics. But as with any big data platform, its efficiency depends on the infrastructure underneath it. And in fact, 85% of big data projects fail. Using a data lake is part of an effective strategy for mitigating many of the hurdles that impact big data projects (storage, retrieval, consolidation, etc.), but that alone can’t necessarily cover processing and resource issues. When...