In this episode, Chris talks to Sharad Kumar, Field CTO at Qlik about the value of good-quality data when developing AI solutions. Much of the current discussion around AI and large-language models (LLMs) is focused on the infrastructure and the significant expense needed to build and train generative AI. However, as the revelation of DeepSeek shows, the industry trend will see models commoditise and become cheaper to train and run.
If infrastructure and software become quickly affordable, what is the differentiator for businesses? ...
In this episode, Chris talks to Sharad Kumar, Field CTO at Qlik about the value of good-quality data when developing AI solutions. Much of the current discussion around AI and large-language models (LLMs) is focused on the infrastructure and the significant expense needed to build and train generative AI. However, as the revelation of DeepSeek shows, the industry trend will see models commoditise and become cheaper to train and run.
If infrastructure and software become quickly affordable, what is the differentiator for businesses? The answer is clearly their data. Data has value to an enterprise, but only if it is in an acceptable format. That means being of high quality and in terms of how Qlik operates, a trusted resource.
During the conversation, Sharad explains the six metrics of the Talend Trust Score, a methodology that measures the value of data based on Diversity, Timeliness, Accuracy, Security, Discoverability and Consumability. He explains how the Trust Score is calculated, but more importantly, how businesses can build a framework to continually improve the quality and value of their data resources.
More information on Qlik can be found on the company website – here. Sharad mentions the user conference taking place in May, details of which can be found here. Finally, Sharad references the Qlik LinkedIn page, which can be found here.
Elapsed Time: 00:47:47
Timeline
- 00:00:00 – Introductions
- 00:01:46 – Data is the value piece within AI, not infrastructure
- 00:02:27 – What is occurring within the AI market?
- 00:04:25 – The future will be a mix of AI model types and sizes
- 00:05:20 – Will businesses build or buy models?
- 00:07:10 – How will agentic AI architectures work?
- 00:10:30 – Customers need to focus on data quality
- 00:12:44 – Both training and RAG data needs to be high quality
- 00:14:40 – Agentic AI wil be intent-driven
- 00:16:43 – What does good data look like within an enterprise?
- 00:19:28 – Qlik has a 6-dimensional trust score
- 00:26:11 – How do customers calculate their trust score?
- 00:30:09 – Is AI driving better data quality?
- 00:34:51 – Qlik can help customers develop a data improvement programme
- 00:37:36 – Qlik brings “product thinking” to data
- 00:38:56 – Where are businesses on the AI journey?
- 00:41:12 – How is improving data quality driving improving AI benefits?
- 00:42:26 – AI could be applied to fix data quality problems
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