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Data Management > Features
Data issues remain a key obstacle across the training, deployment, scaling, and ROI of AI initiatives at many enterprises. A strong data foundation for AI must balance the demand for fast access to large, diverse datasets across different environments while ensuring effective safeguards are in place that guarantee clean, high-quality data is being used in secure, compliant ways. 

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The Future of Work Chatbots, Voicebots, and Virtual Assistants

Enterprises increasingly rely on chatbots, voicebots, and virtual assistants to service more customers without adding to their staff count. These devices can be connected by a single underlying software platform, although there are several companies that use these devices independent of each other.

Adding Meaning to Data: Knowledge Graphs, Vector Databases, and Ontologies

This article discusses the importance of context in enterprise data for generative AI (GenAI), and, in fact, for any AI initiative.

Transforming Customer Service with AI and Knowledge Management

Customer service and customer support operations are no strangers to AI technologies. AI has long been embedded in the interactions between companies and their customers.

Developing a Strong AI Governance Framework

The exponential advancement of AI technologies necessitates the implementation of robust governance frameworks to guide their responsible development and deployment.

Taming the Data Quality Issue in AI

Data quality is the showstopper of AI. Many enterprise leaders who were hot on the business potential of AI are realizing that their efforts will be dead in the water if the data they are employing to train and populate their AI models is inadequate, inaccurate, or not timely.

The AI-Driven Transformation of Enterprise Data Architecture

The past year has been an exhilarating one with AI and more specifically, generative AI (GenAI), quickly emerging as a transformative force, reshaping how businesses will operate, innovate, and interact with customers. As AI continues to gain prominence, its impact on enterprise data architecture is becoming increasingly apparent.

The Rise of GenAI and LLMs

In 1950, Alan Turing suggested a test to determine if computers could mimic human intelligence well enough that an impartial observer could no longer tell the difference. We are still talking about the Turing Test almost 75 years after its inception.