As I reflect (although a better word would maybe recover) on the incredible experience at Qlik Connect in sunny Orlando, I am filled with excitement and pride. This year’s event was particularly special because it was our first opportunity to showcase the expanded Qlik portfolio, including our robust data foundation, Qlik Talend Cloud, and Qlik Answers, our all-new knowledge assistant that delivers personalized, relevant answers to questions generated from a variety of unstructured data sources.

The 2024 Gartner Magic Quadrant for Analytics and BI Platforms just dropped, and we’re thrilled to announce that ThoughtSpot was recognized as a Leader in the report. But, we aren’t the only ones finding ourselves in a new position this year. The analytics and BI space has undergone some of the most significant shifts in over a decade, an aftershock of generative AI.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential to deliver value. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale gen AI while avoiding risk.

When creating an ODBC driver, it is essential to convert data from the data source into a relational format. This is necessary because ODBC and SQL require and operate on data that is organized with a fixed schema and returned in a tabular structure.