As the amount of organizational data around the globe grows, it becomes increasingly important for companies to develop comprehensive data retention policies or strategic plans. The growing impact of new data privacy and compliance laws, coupled with the importance of strong data security measures, makes having a comprehensive data retention
Migration of data from on-premises to cloud systems or between multiple cloud systems is a common and complex event across companies of all sizes and industries. The types of data being migrated can range from email messages to Office documents and PDF files to databases, website data and code repositories.
George Clooney. Cate Blanchett. Beyoncé. Lady Gaga. Brad Pitt. Tom Hanks. What do these artists have in common? Each of them, among others, in addition to a roster of professional athletes, is represented by Creative Artists Agency (CAA), a global entertainment and sports agency co-founded in 1975. CAA brings extensive
Data science was supposed to create a new productivity boom. But, for many companies, that boom never arrived. What’s gone wrong? While companies have invested in data tools, much of the data that’s fed into these systems is low quality – with mislabeled, missing, or incorrect information, which in turn
At some point in time, most businesses will find themselves in a position where they need to migrate data, workloads, or even entire applications and systems to a new location. This process can be daunting, especially for smaller, less established organizations or those with fewer experienced data professionals on staff.
One of the world’s toughest challenges during the height of Covid pandemic was the timely allocation of vaccines to the people and areas that needed them most. Italy, like so many other countries, worked hard to address this daunting optimization problem. One major Italian city decided to introduce a web
While companies may share common ground when it comes to their data quality problems, data quality tools and strategies are not one-size-fits-all solutions to the problem. Each company should approach data quality management through its unique lens. The June 2022 Great Expectations Study surveyed 500 data engineers, analysts and scientists,
As companies have struggled to make use of datasets and AI, many have started to create data products – reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem. Data products can be a powerful tool, especially for large, legacy
While the terms data analysis and data modeling are often intertwined, they are two different concepts. Simply put, data analysis is about using data and information to drive business decisions, while data modeling refers to the architecture that makes analysis possible. In other words, data modeling and data analysis work
“Nudging” – the strategy of changing users’ behavior based on how apparently free choices are presented to them – has come a long way since the concept was popularized by University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein in 2008. With so much data about
For many companies, a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making. Why is it so hard? Our work in a range of industries indicates that the biggest obstacles to creating data-based businesses aren’t technical; they’re cultural. We’ve distilled 10 data commandments to
Data-sharing is both fuel and lubricant to the world’s economy. It powers online and offline business models, and it enables, in the form of cookies and browser history, greater ease of use for consumers. But with every year, as more data – in terms of volume, types, and richness –