As the effects of climate change become increasingly clear, businesses today are facing unprecedented pressure to rein in their carbon footprints and to properly account for and disclose their environmental impact. The era of vague, voluntary corporate mission statements on climate-related goals to satisfy internal and external stakeholders’ demands is over. In their place have […]
Streaming Data Dilemma: 5 Reasons Some Companies Aren’t Streaming Data Yet
In the era of big data and bigger AI, businesses are relying more on the importance of real-time data processing and analytics. Streaming data is a powerful paradigm for handling continuous, unbounded streams of data in real time. However, despite benefits like reduced latency, improved responsiveness, and the ability to make data-driven decisions on the […]
Data Synchronization: Definition, Tips, Myths, and Best Practices
Businesses rely on accurate, synchronized information to make informed decisions. Because discordant data leads to operational inefficiencies, missed opportunities, and costly mistakes. Hence, a unified, reliable source of truth is key, which is possible through data synchronization. It helps maintain consistency across disparate systems, enhancing data reliability and improving decision-making. So, to get started with […]
Enterprise Data World 2024 Takeaways: Trending Topics in Data Architecture and Modeling
I was privileged to deliver a workshop at Enterprise Data World 2024. Publishing this review is a way to express my gratitude to the fantastic team at DATAVERSITY and Tony Shaw personally for organizing this prestigious live event. Part 1 of this article considered the key takeaways in data governance, discussed at Enterprise Data World 2024. Part […]
Integrate Data Architecture with Business Operations to Boost Success Now
In the contemporary data-driven business landscape, the seamless integration of data architecture with business operations has become critical for success. There is a symbiosis between sophisticated data architectures and operational agility that demonstrates how this integration facilitates real-time decision-making, predictive analytics, and personalized customer experiences. As IT professionals amalgamate data architecture and business operations, they analyze […]
RAG (Retrieval Augmented Generation) Architecture for Data Quality Assessment
A large language model (LLM) is a type of artificial intelligence (AI) solution that can recognize and generate new content or text from existing content. It is estimated that by 2025, 50% of digital work will be automated through these LLM models. At their core, LLMs are trained on large amounts of content and data, and the architecture […]
How AI Liberates BI Data from Dashboards
Business intelligence (BI) has historically been a platform for data and business analysts who build dashboards for executives to make decisions. But the generative AI (GenAI) explosion over the past 18 months has changed the game. People everywhere have tasted the power of ChatGPT and now want a natural language interface to every application. But […]
Identity, Security, Access: Three Reasons Why Enterprises Need Zero Trust
Zero trust is taking the enterprise by storm. About two-thirds (63%) of organizations worldwide have fully or partially implemented the cybersecurity posture, Gartner reports, following the motto of “never trust, always verify.” This rush to zero trust makes sense in the remote age. The proliferation of anywhere users means it’s harder than ever to lock down […]
Proceed with Caution: Generative AI in Identity
OpenAI launched generative AI (GenAI) into the mainstream last year, and we haven’t stopped talking about it since – and for good reason. When done right, its benefits are indisputable, saving businesses time, money, and resources. Industries from customer service to technology are experiencing the shift. In fact, a recent study showed a significant increase in GenAI […]
Ask a Data Ethicist: When Is It OK to Use “Fake” Data?
It’s easier than ever to use AI-generated images or text and to create synthetic data for use in research. A recent high-profile story got me thinking more about this question: When is it OK to use “fake” data? Before we dive into this, I’m putting “fake” in quotes because I’m taking a wide perspective on […]