data cleanse

“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.

While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.

While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.

This white paper describes the seven steps you need to develop a plan for data quality – one that scales to your growing business needs.

What are the long-term costs of bad data? This white paper gives you the answers and tells you how to fix it.

Learn what a Web Service is and how it works, the advantages of using a Data Quality Web Service, the technology assessment for implementation, and several case studies (Saab and other real world case studies) to demonstrate real life successes.

Tom Brennan and John Nydam explain the Melissa Data and Stalworth partnership, discuss the business problems caused by bad data, and describe how DQ*Plus provides a complete data quality solution for enterprise applications and commercial databases.

For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.

This white paper describes the seven steps you need to develop a plan for data quality – one that scales to your growing business needs.

What are the long-term costs of bad data? This white paper gives you the answers and tells you how to fix it.

While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.

Learn how email verification technology helped to minimize Party City's hard bounces at point-of-sale by allowing data to be cleansed and verified in real time, which greatly reduced the amount of human error.

Learn how email verification technology helped to minimize Magento's hard bounces at point-of-sale by allowing data to be cleansed and verified in real time, which increased their email deliverability rates by 25%.

Scalable data platforms such as Apache Hadoop offer unparalleled cost benefits and analytical opportunities. IBM helps fully leverage the scale and promise of Hadoop, enabling better results for critical projects and key analytics initiatives. The end-to- end information capabilities of IBM® Information Server let you better understand data and cleanse, monitor, transform and deliver it. IBM also helps bridge the gap between business and IT with improved collaboration. By using Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of- impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time. Since its inception, Information Server has been a massively parallel processing (MPP) platform able to support everything from small to very large data volumes to meet your requirements, regardless of complexity. Information Server can uniquely support th