One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. Knowing that a transaction is fraudulent is a critical requirement for financial services companies, but knowing that a transaction that was flagged by a rules-based system as fraudulent is a valid transaction, can be equally important.
The success and growth of companies can be determined by the technologies they rely on in their tech stack. To deploy AI enabled applications to production, companies have discovered that they’ll need an army of developers, data engineers, DevOps practitioners and data scientists to manage Kubeflow — but do they really? Much of the complexity involved in delivering data intensive products to production comes from the workflow between different organizational and technology silos.
Well, it is International Fraud Awareness Week, focused on promoting fraud prevention and education. A fantastic initiative! Maybe I am naïve but I feel a bit sad that there is a need for “fraud week”. The insurance industry has a long and intimate relationship with fraud in many different ways. Insurance fraud can take place at a process or business function level, most notably in claims or underwriting.
As a specialized and mature form of embedded analytics, contextual analytics is a game-changer if you're a software vendor looking to further augment your customers’ user experience, without requiring developers to completely reengineer your offering. Contextual analytics blends the data your users need for decision-making right at the point of their daily work, directly inside the interface and transaction flow of your software.
F5 Networks modernized its data stack, boosted time to insight, and placed actionable data in the hands of the right decision-makers. F5 Networks is a Seattle-based application services and application delivery networking company. Because its revenue depends on speed and accuracy, the company is always looking for ways to improve business insights and support data-driven decision-making.
Data analysis is incredibly useful for all kinds of businesses and also has academic and hobbyist applications. Nonetheless, it’s still possible to fall into numerous traps when trying to accurately interpret your data. That’s why we’re giving you a list of the top 8 common data analysis mistakes to avoid at all costs. Our first expert Jitin Narang, CMO at TechAHead contributed the following five top data mistakes to avoid:
Happy anniversary to us! Fifteen years ago, Talend’s founders anticipated the business need to have data accessible to all users across an organization. I’ve been with Talend since the beginning, and I wanted to celebrate this milestone by sharing our product innovation and evolution through the years. Talend was created with the idea that we could offer something new to the market: open source ETL.
This is part of our series of blog posts on recent enhancements to Impala. The entire collection is available here.
