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Garbage in, Garbage Out – How We Got Here and Why We Must Get Out Now

This whitepaper from our friends over at Profisee, reflects on why the state of data in most  organizations is as dismal as it is, and why there is such a challenge involved in demonstrating the value of trusted data available across mission-critical operations and analytics in an enterprise.

Why CRM and Data Warehouses Fail with Customer 360

This whitepaper from our friends over at Profisee explains why achieving a complete view of the customer is so difficult and how customer relationship management (CRM) systems and data warehouses, especially in the  insurance industry, do not manage customer-related data well. The core elements of this type  of data fall into a discipline called Master Data Management (MDM).

Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data

In this white paper, our friends over at Profisee discuss how Master Data Management (MDM) will put your organization on the fast track to automating processes and decisions while minimizing resource requirements, while simultaneously eliminating the risks associated with feeding AI and ML data that is not fully trusted. In turn, your digital business transformation will be accelerated and your competitive edge will be rock solid.

What Makes GPUs, GPU Databases Ideal for BI?

What makes GPU databases ideal for BI? That’s what a new white paper from SQream DB wants to explain — incorporating real-world use cases to explain how you can turn your existing BI pipeline into “a more capable, next-generation big data analytics system.” Download the new report, courtesy of SQream DB, to learn more about how GPUs and GPU databases can help you organize and benefit from your next big data analytics system.

Insurtech Data & Privacy in 2019

In this special guest feature, Jason T. Andrew, CEO and co-founder of Limelight Health, discusses how InsurTech is coming of age at just the right time to decide how to use Big Data in a way that protects startups from the pitfalls into which the large social media companies fell. He believes we have the opportunity to build the InsurTech industry as digitally transparent and ethically sound, even as we accept the inevitability that the curtain has been pulled back on our privacy as we imagined it. As we revolutionize the insurance industry, it’s all the more important that we lead the way in developing technologies that empower users and set the right safeguards for data protection between technology vendors and insurers.

Why Self-Service BI Tools Alone Can’t Build Data-Driven Cultures

In this special guest feature, Brett Hurt, CEO of data.world, suggests that while 99% of executives want a data-driven culture, it’s hard to build one. Enter the Chief Data Officer (CDO), tasked with capturing and growing the value of data and analysis within his or her enterprise. It’s not an easy job. True data-driven cultures aren’t built by buying expensive tools to empower the data elite. And while deploying self-service BI (business intelligence) tools is one important step in the right direction, the Chief Data Officer is on a journey.

Building a Data Catalog: A Guide to Planning & Implementing

Building and implementing a data catalog can help your enterprises’ data community discover and use the best data and analytics resources for their projects. A data catalog can help businesses achieve faster results, and make better decisions. As for the next steps to address the importance of data catalogs in your business, Data.world covers that, as well, in a new report.

A ‘Pre-Flight Checklist’ for Machine Learning Training Data

Machine learning is often key to success for today’s institutions that rely heavily on data for success. But often, data science teams can have a difficult time convincing their organizations of the breadth and size of a training data challenge. A new report from Alegion walks through a checklist to review before helping your enterprise take the next step in machine learning.

Alegion Outlines the 4 Most Prevalent Types of AI Bias

AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.

New Guide Offers Databricks Unified Analytics Platform Machine Learning Use Cases

The fields of machine learning and deep learning are on the brink of unprecedented breakthroughs across a variety of verticals. And according to a new report from Databricks, “data is the new fuel,” for these market advancements. Download the new white paper today, “Four Real-Life Machine Learning Use Cases,” to explore Databricks Unified Analytics Platform use cases in the advertising, loan servicing, media industries and more.