Big Data and Environmental Sustainability: A Conversation Starter

whitePaper | December 29, 2022

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

whitePaper | August 1, 2022

The Graph database is fundamental to driving a much-needed breakthrough in the world of Master Data Management (MDM). Until now, traditional MDM systems have imposed strict rules and structures on how these projects are managed. This has cost enterprises dearly in terms of wasted time and resources and has been a major contributory factor to the failure of many MDM initiatives. Graph has changed all of that, allowing modern MDM platforms to completely redefine how master data is prepared for insight and used to deliver tangible business outcomes.

whitePaper | April 2, 2023

In this white paper, we share how The Alan Turing Institute’s AI for science and government (ASG) programme has been using collaborative and multidisciplinary data science and AI to help tackle climate change.

whitePaper | February 7, 2020

Multiple studies have proven that, while the business demand for databases grows, operational teams are constrained by traditional, reactive processes. Combined with budgetary pressures, this causes them to fall short in meeting the demand in a timely fashion. In addition, as companies adopt cloud paradigms, operational teams must manage across on-premises and cloud environments with the same resources. To be effective, companies need to shift from a reactive posture to one of standardization and proactive analysis and planning by leveraging intuitive tooling to automate manually intensive and non-value adding activities and to deliver real-time insights.

whitePaper | April 27, 2023

Data analytics are used to test controls and validate that business risks are managed. This would generally occur at a point-in-time when an assurance activity is scheduled. Rather than test a number of transactions, the entire population of transactions can be reviewed for greater coverage. Data analytics includes automated tools such as generalised audit software, test data generators, computerised audit programs, specialised audit utilities and computer-assisted audit techniques (CAATs).

whitePaper | September 1, 2022

Artificial Intelligence (AI) is not a recently discovered field. Since the beginning of the computer science discipline, in the late 1950s, AI has drawn a lot of attention in the international scientific community and since then it has represented a field of study which has triggered diverse and numerous research activities.