Data is key to building resilience and achieving operational excellence—but first, your data must be intelligible. Luckily, modern BI solutions have intuitive interfaces that allow business users to build interactive data visualizations and contextual data stories. With this knowledge at their fingertips, your entire organization is empowered to make data-driven decisions.
Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases, transform it, and load it into the data warehouse. In this post, we show how you can leverage Amazon Aurora zero-ETL integration with Amazon Redshift and ThoughtSpot for GenAI driven near real-time operational analytics.
In The fundamentals of data warehouse architecture, we covered the standard layers and shared components of a well-formed data warehouse architecture. In this second part, we’ll cover the core components of the multi-tiered architectures for your data warehouse.
When using data to make impactful business decisions, certain doubts may start to arise, like “What does this column exactly mean?” or “Can I trust this data source I want to use?” Questions like these speak to a larger need for increased data literacy and trust in data. ThoughtSpot continually invests in this area, giving users the confidence to build the correct Answers needed for their analysis—and ensuring they can trust the data they are shown.
We’ve talked about the many ways large language models (LLMs) and artificial intelligence (AI) are impacting business efficiency, data and analytics, and even FinOps. But we’ve yet to talk about arguably one of the most important areas of concern: security.
By the end of this two-part series, we will dive into what data warehouse architecture is and how to implement one for your organization. Part one will look at architectural layers and common data warehouse components, while part two dives into multi-tiered data warehouse architecture.
We’re thrilled to announce that both ThoughtSpot and Mode (acquired by ThoughtSpot in July 2023) have been recognized as Leaders in Snowflake's recent Modern Marketing Data Stack report! Given the ever-evolving landscape of modern data analytics products, organizations are looking to ThoughtSpot and Mode when seeking innovative solutions—helping them harness the power of their marketing data.
Semantic layers are a game changer, allowing organizations to define metrics and business logic in one, centralized location. Because business users can trust that their data is built on a single source of truth, the semantic layer also empowers self-service analytics. Looker Modeler has become a leader among semantic layers, allowing users to seamlessly layer on top of their business data.
ThoughtSpot users can easily create content with data using our intuitive, AI-powered search experience. However, business users sometimes find themselves asking a critical question: which content should I trust and use for my specific business use case? For example, if there are ten “Sales Performance” Liveboards created by different authors, you may wonder which is the golden version—the Liveboard that is reviewed, approved, and consistently maintained.
In a survey by the Harvard Business Review, 87% of respondents stated their organizations would be more successful if frontline workers were empowered to make important decisions in the moment. And 86% of respondents stated that they needed better technology to enable those in-the-moment decisions. Those coveted insights live at the end of a process lovingly known as the data pipeline.
