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20 Predictions for 2020 from AI to Data Management

  • Trifacta Will Davis The Bloor Group Eric Kavanagh & The Bloor Group Evren Cakir

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Expanding Your Data Science and Machine Learning Capabilities

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. In…

  • Cloudera Bethann Noble & TigerGraph Gaurav Deshpande Trifacta Will Davis & DBTA Stephen Faig

How SDI Modernized Supply Chain Data

PROCUREMENT ANALYSTS AUTOMATE REPORTING USING TRIFACTA ON CLOUDERA The Merchandise, Repair, and Operations (MRO) industry is traditionally on the back end of technology. Analysts struggle with the limitations of Microsoft Excel to be effective in their reporting. The procurement analyst may not have the technical skill set to generate complex code to solve their problems,…

  • SDI John Delligatti Cloudera Michael Ger Trifacta Matt Derda

Why Data Management Requires Modern DataOps

Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. The advent of cloud platforms from AWS, Microsoft Azure and Google Cloud gives every business the ability to swipe a credit card and have access to virtually any computing service…to handle just about any data initiative. Yet, why…

  • Forrester Noel Yuhann & Trifacta Will Davis

How to Use Data Prep to Accelerate Cloud Data Lake Adoption

What began as a trickle is now the mainstream: Organizations are moving to the cloud for data management. No longer is the cloud just a cheaper place to park data; it is key to supporting business-critical innovation in advanced analytics, data science, and AI as well as for end-user business intelligence, data exploration, and data…

  • TDWI David Stodder

How to Streamline DataOps on AWS

The quest for the insight-driven enterprise has spurned a mass exodus to the cloud. But cloud data ecosystems can be very complex with multiple data storage and processing options. Most organizations are struggling with how to accelerate analytics in their data lake environments. For example, many organizations are struggling to figure out how to manage…

Data Prep & Automated ML: Better Predictions For Consensus

Financed smartphones are a magnet for identity theft, leaving retailers in the digital and telecommunication industry vulnerable to fraud. Consensus, a Target-owned subsidiary, has developed a highly accurate solution to identify fraud at the point-of-sale before it happens. In this latest webinar, you will learn how Consensus put together agile processes on a cloud analytic…

  • Trifacta David McNamara, Consensus Harrison Lynch & DataRobot Rajiv Shah

Clean Data & Accurate Machine Learning Models

Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the materials…

  • AWS Kris Skrinak & Trifacta Vijay Balasubramaniam

Data Prep For Data Ops: How To Select & Deploy

In recent years, a new term in data has cropped up more frequently: DataOps. As an adaptation of the software development methodology DevOps, DataOps refers to the tools, methodology and organizational structures that businesses must adopt to improve the velocity, quality and reliability of analytics. Widely recognized as the biggest bottleneck in the analytics process,…

  • Forrester Cinny Little & Trifacta Will Davis

Transform a Data Swamp into a Data Lake with Enterprise Data Management Strategies

Clean data is crucial for deriving insight from data and powering applications like Predictive Analytics, Business Intelligence, Machine Learning and Artificial Intelligence. “Research shows that 40% of the anticipated value of all business initiatives is never achieved. Poor data quality in both the planning and execution phases of these initiatives is a primary cause,” according…

  • Caserta Alex Lovell-Troy & Trifacta Vijay Balasubramaniam

How to Structure a Modern DataOps Team

Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. Businesses now have the ability to work with data at a scale and speed that many of us would have never thought was possible. Yet, why are so many organizations still struggling to drive meaningful ROI…

  • Forrester Michele Goetz & Trifacta Will Davis