The conversation around data preparation has been evolving. What started as a push for self-service access for specific use cases has now expanded to operationalizing a data pipeline across the enterprise. The goal is to create efficiencies and eliminate workflow silos to propel data strategy
Nearly every business is under competitive, disruptive, and regulatory pressures. As companies face digital transformation and modernization to meet their customers’ expectations, leveraging data and AI at the speed of business can be the biggest differentiator.
However, according to MIT Sloan, 81
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
Historically, Master Data Management (MDM) projects have focused on creating a single view of the truth that can be consumed by business processes. Learn more about how the evolving need to utilize MDM serves as catalyst for a new solution extension offering a managed data preparation and data
Data is widely seen as the new source of competitive advantage, driving smarter decisions and helping enterprises outthink their rivals. But opportunities are often missed. Getting the data needed from multiple underlying systems can take far too long for application developers, business analysts
Volumes of data generated from a variety of sources can be a challenge to wrangle. Take a look at the advanced Design Data Flow beta capability of IBM Bluemix Data Connect that enables consumption of large data volumes from disparate sources, which can then be curated for deriving insights and
Many of us might be surprised to learn that some of the most familiar brand names around started off with other names. IBM recently renamed IBM DataWorks—its flagship, self-service data preparation offering—to IBM Bluemix Data Connect. Get a glimpse of it in this brief overview, and discover how
IBM Insight at World of Watson 2016 has oodles of opportunities for data engineers to enrich their skill sets with a bevy of best practices, peers to network with, pointers and tips to discover, sessions to attend and more. Consider five key reasons to get the green light from your organization to
Busy executives that need to make vital business or investment decisions don’t want to wait for days or weeks for an answer to a question they put to analysts or receive incomplete information or analysis with spurious results. Fortunately, analysts today can access cloud-based data services that
Is your organization stuck at the edge of Hadoop adoption, searching for a path to broad use that doesn’t hold back your most proficient users? Big data discovery technology aims to help you bridge the chasm between early adoption and majority use, bringing rank-and-file users into the fold without
Expand the boundaries of your possibility thanks to Apache Spark. Big data analysis is undergoing a paradigm shift powered by Spark, which supercharges the Hadoop ecosystem to help organizations accomplish things that were once thought impossible.
Advanced predictive analytics requires data preparation strategies that can transform the data, enriching it to make it suitable for processing. Indeed, your choice of data preparation strategy can help you boost the accuracy of the outcomes you achieve.
No less than traditional scientists, data scientists need a guiding strategy for solving problems. Such a methodology should directly address the problem at hand and should provide a framework for obtaining answers and results. Learn more about the Foundational Methodology for Data Science and how