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insideBIGDATA Guide to Computer Aided Engineering

[SPONSORED POST] The essential first step for manufacturers is to consider how much data the enterprise has at its disposal. Most manufacturers collect vast troves of process data but typically use it only for tracking purposes, not as a basis for improving operations. The challenge is for these players to invest in the systems and skillsets that will allow them to enhance their use of existing process statistics. This Guide, “insideBIGDATA Guide to Computer Aided Engineering,” sponsored by Dell Technologies, will walk through some of the ways to expand the scope of analytics to further increase business value.

Data Transformations: The New Era

In this special guest feature, Armon Petrossian, CEO & co-founder at Coalesce, discusses the bottlenecks disrupting enterprises from accomplishing data transformations to support the continued push from on-premises to cloud platforms. Also examined are the struggles enterprises face in meeting today’s demands to become data-driven.

Leading and Organizing Analytics for Success. Here’s What the Emerging Leader of Analytics Will Look Like

In this contributed article, Jack Phillips, CEO and co-founder of The International Institute for Analytics (IIA), discusses the data-driven culture and organizing analytics for success; specifically how the highest performers organizing are adapting their cultures around data and leveraging their analytics capabilities for success and why data literacy will matter so much to enterprise workers going forward.

insideBIGDATA Guide to Computer Aided Engineering

The essential first step for manufacturers is to consider how much data the enterprise has at its disposal. Most manufacturers collect vast troves of process data but typically use it only for tracking purposes, not as a basis for improving operations. The challenge is for these players to invest in the systems and skillsets that will allow them to enhance their use of existing process statistics. This Guide, sponsored by Dell Technologies, will walk through some of the ways to expand the scope of analytics to further increase business value.

How AI and ML technologies are Streamlining Language Creation and Impacting the Global Economy

In this contributed article, Joe Hagan, Chief Product Officer at LumenVox, discusses how artificial intelligence (AI) and machine learning (ML) have become so useful and prevalent that we use them in our daily lives without really thinking too much about it. One key area where these intelligent technologies have progressed in leaps and bounds—almost to the point where they match human abilities—is the field of automatic speech recognition technology.

Databricks Launches Data Lakehouse for Retail and Consumer Goods Customers

Databricks, the Data and AI company and pioneer of the data lakehouse architecture, announced the Databricks Lakehouse for Retail, the company’s first industry-specific data lakehouse for retailers and consumer goods (CG) customers. With Databricks’ Lakehouse for Retail, data teams are enabled with a centralized data and AI platform that is tailored to help solve the most critical data challenges that retailers, partners, and their suppliers are facing.

“Above the Trend Line” – Your Industry Rumor Central for 1/17/2022

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Explanatory vs. Exploratory — Which Data Visualization Is Right for Your Organization?

In this special guest feature, Chad Reid, VP of marketing and communications at Jotform, argues that data visualization remains one of the best tools you can use to highlight relevant information for stakeholders, but to use it to its full potential, it is key to understand the difference between explanatory versus exploratory data analysis and know when to use each. He provides three strategies for better data visualization.

Case Study: Balancing MLOps Innovation with Tough Security Standards

As AI adoption has grown, so too have concerns about data protection and infrastructure security across the MLOps lifecycle. At GTS Data Processing, a rapidly growing German IT company, security is top of mind as they deliver Infrastructure-as-a-Service and Software-as-a-Service platforms to companies across Europe. GTS’ DSready Cloud offering, powered by Domino® and hosted in Germany, brings together the tools, technologies, compute, and collaboration capabilities its clients need to deliver and manage data science capabilities at scale—all within a GDPR-compliant environment that supports Germany’s stringent security standards.

The Data Silos Holding You Back are All in Your Head

In this contributed article, Pete Goddard, CEO and co-founder of Deephaven Data Labs, paints a new picture of data: one of a synchronized group effort rather than a relay race of individual, siloed teams. He explains how the “baton pass” method of working with data – inherently full of risks and slow-downs – has evolved but not quite met the needs of modern data teams. Pete then outlines how de-compartmentalizing data use cases can lead to a new understanding that data is changing and exists on a continuum of time. When all data problems are viewed through the lens of how data needs to meet software, and diverse teams are brought together to work in tandem on these interesting problems, we can create one frictionless data world free from the current limits of intermediation.