According to a new press release, Qlik’s Generative AI Benchmark Report, based on research executed by Enterprise Technology Research (ETR) in August 2023, reveals that enterprises are increasing investments in technologies to enhance data fabrics and achieve success with generative AI. The report, which surveyed 200 C-level executives, VPs, and directors from Global 2000 firms, demonstrates a growing interest in blending traditional AI with generative AI to maximize its impact across organizations. This approach is driven by the understanding that a trusted data foundation should support generative AI tools to fuel insights and advanced use cases.
While generative AI holds promise, leaders recognize the importance of surrounding these tools with appropriate data strategies and technologies. Enterprises plan to leverage public or open-source models refined with proprietary data, and they seek third-party expertise to address challenges related to data security, governance, bias, and hallucination issues. Additionally, organizations are investing in data fabrics to mitigate these concerns and improve data quality, ML/AI tools, data governance, data integration, and BI/Analytics.
Qlik, a leading data integration and analytics solutions provider, offers a data foundation for AI, AI-enhanced and predictive analytics capabilities, and AI for advanced use cases, enabling organizations to leverage AI effectively and drive success with generative AI. The report emphasizes the importance of data quality and a modern data fabric as a foundation for AI success, highlighting the need to curate, assure quality, secure, and govern data for generative AI models.
The report underscores the critical role of data in AI success and outlines Qlik’s contributions to enhancing AI outcomes through data integration, data quality, and analytics solutions. It also highlights the significance of investments in data fabrics, emphasizing their role in overcoming common barriers to generative AI implementation, such as regulation, data security, and resource limitations.
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