This whitepaper, “Why CRM and Data Warehouses Fail with Customer 360,” from our friends over at Profisee explains why achieving a complete view of the customer is so difficult and how customer relationship management (CRM) systems and data warehouses, especially in the insurance industry, do not manage customer-related data well. The core elements of this type of data fall into a discipline called Master Data Management (MDM).

In this special guest feature, Abhishek Bishayee, Associate Vice President – Strategy and Solutions at Sutherland, believes that while AI-driven IoT is already making its mark, we are only at the start of this exciting union and realizing the potential extent of its impact. The combination of both technologies enables businesses with a physical presence to reap greater insights from the large volumes of data generated by a slew of IoT applications, sensors and devices.

In this contributed article, Alex Williams, Writer/Researcher at Hosting Data UK, observes that NoSQL was developed to counteract SQL, being both horizontally expandable, and not even needing to use a schema at all.t? Traditionally, SQL databases tend to be very costly, from their vertical-only expansion to a large amount of design required to be done on the schema before the database is even made.

In this contributed article, co-founder of Hackr.io, discusses how artificial intelligence will revolutionize the way video games are developed. AI has brought a change in the gaming industry ever since its inception. Over the years, we have seen drastic changes in the way games are developed. In today’s technologically advanced world, games have become more challenging and exciting by providing human like experiences.

Exasol, the high-performance analytics database, published new research that reveals since the pandemic started, 87% of retail organizations in the U.S. have been under pressure to make data-driven decisions faster. The finding comes from an in-depth research report, “Retail: Decision making during times of uncertainty,” that quantifies how retail organizations in the U.S. are heightening data-driven decision-making cycles to meet real-time demand and prepare for surging online sales brought on by the COVID-19 pandemic.

In this special guest feature, Michael Coney, Senior Vice President & General Manager at Medallia, highlights how contact centers are turning to narrow AI, an AI system that is specified to handle a singular task, such as to process hundreds of hours of audio in real time and create a log of each customer interaction. Thousands of hours of calls can be processed and logged in a matter of a few hours.

In this contributed article, Darshan Rawal, Founder and CEO of Isima, explains how the data ecosystem has exploded in the last decade to deal with multi-structured data sources. But the fundamental architecture of using queues, caches, and batches to support Enterprise Data Warehousing and BI hasn’t. This article looks at the architectural styles of the three eras of data management – pre-big data, the open-source revolution, and the cloud-native version. It will be a dive into trade-offs of each and what lies ahead. You’ll get a techno-strategic best practices of architecting data platforms as they pave the path to recovery for your organization.

In this contributed article, Christian Thun, leader of the engineering team at Agiloft, discusses the barriers organizations face when adopting AI. The challenge organizations face today is to make all business process data available in one centralized location where it can be accessed, cleaned, normalized, and analyzed by AI for decision making.

In this special guest feature, Jono Marcus, Behavioral Insights Director and Digital Project Owner for AtSource.io, Olam’s sustainability insights platform, at Olam International Ltd., explores how behavioral science can make complex data meaningful and useful.

In this contributed article, Luming Wang, CTO, EVP of Software at ElectrifAi, explains that the theory behind data science was never meant for small data sets and scaling to do so comes with a host of issues and irregularities. Luming also notes data typically has many, sometimes infinite dimensions and, while organizations are trusting algorithms and models to make sense of their data lakes, finding a singular truth is a rare occurrence.