Even with big investments in data systems, many organizations still struggle with basic questions like “What’s the real number of active customers?” because their data is scattered and inconsistent. By organizing data around core business concepts like Customer or Product, companies can create clarity, ownership, and a strong foundation for better decisions and future data products.
The rise of Large Language Models (LLMs) like ChatGPT and Google Gemini is transforming how businesses operate. From accelerating content creation to analyzing complex datasets, the possibilities seem endless. But as we embrace these powerful tools, a crucial question arises: what exactly happens to the sensitive company information you input into these tools?
AI agents are the next step in artificial intelligence, moving beyond generative AI into systems that act, decide, and adapt. Businesses are now exploring how AI agents create real value, but adoption comes with risks and challenges.
We previously wrote about the power of default semantic models, and how they were a convenient out-of-the-box model we can offer our users. Microsoft just announced they will be sunsetting the default semantic models, effective December 2025. But what does this mean for you and the default semantic models in your organization? Here are the two changes, how they can impact you and our advice.
With a pilot launch planned in just one month, our client wanted to ensure a Q&A chatbot was safe, compliant, and reliable. Enter our AI Risk Assessment Workshop, a collaborative session led by our Responsible AI experts to identify and mitigate risks. In this post, we’ll walk you through how we addressed the client's challenges and why this process is critical for responsible AI deployment.
Many teams still work with fragmented pipelines, where knowledge is scattered across individuals. The logic is embedded in legacy tools within siloed teams. Enter dbt: a free (dbt core), open-source tool that empowers data teams to manage all their transformation logic directly in the datawarehouse using just SQL.
Your data isn’t lacking infrastructure. It’s lacking impact.
Product thinking brings the focus back to business outcomes—by treating data like a product, not a project.
This blog explores Datashift’s key strategies for a seamless migration. We’ll cover pre-migration planning, execution with piloting and iteration, and post-migration optimization to maximize Power BI’s benefits.
In 2025, AI-driven data quality, predictive analytics, and seamless tool integration will redefine business decision-making. Discover the top trends shaping data quality management.
