Evan Terry, Chief Analytics Officer at Velocity Mortgage Capital said that enterprise analytics technology is increasingly sophisticated, due to machine learning, artificial intelligence (AI), and streaming analytics, but you can’t benefit from these advancements without a solid foundation. “There’s a real interplay there between the foundational and the advanced.” Terry discussed these interplays during his […]
Case Study: Big Data Analytics Advance Sutton Bank Forward
Sutton Bank is an FDIC-regulated, Ohio state-chartered bank. They have been in the payment business about 20 years, specifically in the prepaid space. Jeff Lewis, Senior Vice President of Payments and Prepaid Card Services at Sutton Bank, says that payment and card services are an increasingly popular offering for financial institutions, provided they can keep […]
Case Study: New Innovations in IoT Technologies for Manufacturers
Manufacturers want to save money, conserve energy, and reduce greenhouse gas emissions, but it’s a challenge to see where and when energy use is occurring. When the utility bill arrives, a manufacturer can see how much total energy was used, but energy use is not broken down on a machine-by-machine basis. Invisible energy use hides […]
The Data Warehouse, the Data Lake, and the Future of Analytics
Data lakes were created in response to the need for Big Data analytics that has been largely unmet by data warehousing. The pendulum swing toward data lake technology provides some remarkable new capabilities, but can be problematic if the swing goes too far in the other direction. Far from being at the end of this […]
Artificial Intelligence, Machine Learning, and Deep Learning Explained
The first reference to artificial intelligence was in a paper written by Alan Turing in 1950, entitled Computing Machinery and Intelligence, where he asked the question, “Can machines think?” Turing’s article, as well as a 1977 paper entitled History of Artificial Intelligence by P. McCorduck are recommended reading for those wanting a greater understanding of […]
Data Management vs. Data Virtualization
Data Management, as a principle, requires that data is brought to a single place, governed actively, and available in real time. It also includes the trust needed for business users to perform day-to-day business functions and to do it better than the competition. Data needs are contextual and based on the role of the user. […]
The Challenges of Data Quality
The Data Quality and Data Management market is going through a paradigm shift where the focus has turned to the business user. Historically, business users have been at the mercy of over-burdened IT departments with limited resources, but IT is not to blame. Even with the simplest query, the answer used to be “It’ll be […]
ETL vs. Data Preparation
Extract, Transform, and Load (ETL) technologies, managed exclusively by IT, have until recently been the primary tool used to combine data from multiple sources and thus provide the ability to drive important business decision making for organizations. But, with the advent of self-service data preparation, business users and subject matter experts (SMEs) can find those […]
Data Governance and Data Architecture: There is No Silver Bullet
In terms of a market perspective, Data Governance has increased in visibility partly because of the increase in security breaches, data security issues, and compliance requirements for various industry regulations. To secure and manage data properly, it helps to manage it at a higher level and know which of your data is sensitive in the […]
Streaming Analytics: The Value is in the Action
“When we’re talking about streaming analytics, we’re talking about flipping our traditional paradigm a little bit and thinking about how we bring analytics to our data, and not necessarily data to our analytics,” said Kimberly Nevala, the Director of Business Strategies for SAS Best Practices, while discussing streaming analytics during the DATAVERSITY® Enterprise Analytics Online […]