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Link to Best Practices for Deploying and Scaling Industrial AI

Best Practices for Deploying and Scaling Industrial AI

Artificial Intelligence (AI) is transforming industrial operations, helping organizations optimize workflows, reduce downtime, and enhance productivity. Different industry verticals leverage AI in unique ways.

Link to The Center for Adaptive Edge Intelligence

The Center for Adaptive Edge Intelligence

Adaptive edge intelligence brings real-time decision-making to the point of data creation, whether from sensors, machines, or cameras.

Link to The Value of Vehicle Electrification

The Value of Vehicle Electrification

Electric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges.

Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Digital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.

Link to Smart Manufacturing for Automotive

Smart Manufacturing for Automotive

Leading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.

Link to Center for Data Pipeline Automation

Center for Data Pipeline Automation

As businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential.

Link to Center for Automated Integration

Center for Automated Integration

The goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more.

Link to Continuous Intelligence: Insights

Continuous Intelligence: Insights

Digital transformation requires continuous intelligence (CI). Today’s digital businesses are leveraging this new category of software which includes real-time analytics and insights from a single, cloud-native platform across multiple use cases to speed decision-making, and drive world-class customer experiences.
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Goodbye Don Draper, Hello Big Data: An EMA Report on Modern Analytics

Goodbye Don Draper, Hello Big Data: An EMA Report on Modern Analytics

Modern analytics for businesses must move beyond a traditional approach of poring over structured data in a back office, demographic profiles, or coming up with a “gut feeling” solution. With the explosion of the Internet, its “Things,” the cloud, and social media, data is proliferating quickly. Getting a handle on Big Data, and pinpointing it for business advantage, is a Herculean task. It’s one that requires scalable, simple-to-use solutions, according to a position paper from Enterprise Management Associates/9sight.

Written By

RTInsights Team

Aug 12, 2015

Operational analytics, which provides real-time support and decisions, was cited as the largest project challenge in an EMA survey of 259 Big Data professionals. “For many organizations, the term ‘real time’ has stopped being a marketing buzzword and is now a core business requirement,” report authors stated. “Equally urgent is the need to simplify getting to value.”

Big Data Challenges

That’s a tall and wide order, as analytics must now deal with large data sets, including structured, semi-structured (XML and JSON), and unstructured data. In addition, there are new sources of data, including analytical database platforms/appliances; NoSQL data store platforms; data discovery platforms; the cloud; Hadoop and related technologies.

In such a Big Data environment, the cloud is crucial, as it provides the elasticity to cope with scaling, speed, and data-access issues. Companies also need code-free solutions that can be deployed quickly and be understood by line-of-business professionals, not just data scientists. (See more on this subject from RTInsight’s expert Connie Moore.)

One solution, RapidMiner, was evaluated. EMA argued it met the requirements of a modern analytics platform, which include ability to deal with full data sets while being easy to use; scalability, acceleration of time to value, and Agile development methods.

Please fill in your information to get the complimentary report now.

RTInsights Team

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