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Data Transformation for Machine Learning

In this contributed article, Damian Chan, Technical Success Manager at Matillion, discusses common data transformations that you can perform so your data can be processed within machine learning models. When it comes to machine learning, you need to feed your models good data to get good insights. Data in the real world can be really messy and in most cases, some sort of data cleansing needs to be performed prior to any data analysis.

The Secret to Accurate Machine Learning Models is Data Transformation

In this contributed article, Damian Chan, Solutions Engineer with Matillion, discusses how machine learning can help your business process and understand data insights faster – empowering data-driven decisions to be made across your organization. For machine learning to be successful, however, your models will need to consume clean data sets. As the quality of your data increases, you can expect the quality of our insights to increase as well. Transforming data for analysis can be challenging based on the growing volume, variety and velocity of big data. This challenge will need to be overcome to unlock the potential of your data and to mobilize your business to move faster and outpace competitors.

Dell Boomi Delivers Security, Usability and Flexibility With Latest Release

Dell Boomi™ (Boomi), the leading enterprise transformation provider of cloud integration and workflow automation to build The Connected Business, announced its Fall 2018 release of the Boomi platform. This update reinforces Boomi’s mission to provide enterprise-grade security, enhanced usability and expanded flexibility for customers.

Nearly 40% of Data Professionals Spend Half of their Time Prepping Data Rather than Analyzing It

TMMData, creator of flexible data integration and preparation platform Foundation, partnered with the Digital Analytics Association to survey its community about data priorities and challenges. The survey revealed that data access, quality and integration present persistent, interrelated roadblocks to efficient and confident analysis across industries. Most notably, nearly 40% of data professionals (37.5%) spend more than 20 hours per week accessing, blending and preparing data rather than performing actual analysis.

Trifacta Reveals Spring ’17 Release to Accelerate Expansion of Data Wrangling Projects in Large Scale Enterprise Environments

Trifacta, the global leader in data wrangling, announced the Spring ’17 Wrangler Enterprise release, accelerating expansion of data wrangling projects in production environments through advancements in self-service scheduling, sharing and sampling capabilities. With the Spring ’17 release, Trifacta now provides enhanced features that meet the growing expectations for deploying data wrangling solutions at enterprise-wide scale.

Pitney Bowes Forms a New Data Practice to Drive Digital Transformation Initiatives

Pitney Bowes Inc. (PBI: NYSE), a global technology company that provides innovative products and solutions to power commerce, introduced a new data practice to help businesses accelerate their digital transformation initiatives. This new cross-company practice is designed to help organizations utilize data and analytics to deliver a superior customer experience, support product and service innovation, and optimize business processes.