You may have a HARMAN digital cockpit in your car, a HARMAN Kardon home theater in your living room, or JBL earbuds for your daily commute. Touring musicians use our sound and lighting equipment in concert, and many of your favorite albums and singles were recorded with our pro audio gear. And many enterprises take advantage of our digital transformation solutions to achieve better business outcomes using emerging tech such as generative AI, cloud, and advanced analytics.
In 2019 various departments of HARMAN were using QlikView, but they were developing applications in silos with no single analytics standard to ensure continuity and consistency across departments. Individual teams were building apps that were specific to their operations, and they didn’t scale them across the company. The development of analytics apps in silos meant business users spent a lot of time finding, ingesting, and validating the data going into these tools — users spent 80% of their time ingesting data and 20% analyzing it. To get business benefits, HARMAN data analytics team wanted to flip that ratio.
The migration project to Qlik Sense had already begun when Annette Jonker, Senior Director, Data and Analytics at HARMAN International, joined the team. They formed a centralized data and analytics team to affect the transition and consolidate data analytics usage and development. Qlik Sense was the perfect solution — it was scalable, and the company could leverage its existing QlikView knowledge to get the most out of the platform.
Senior Director, Data and Analytics, HARMAN International
The team identified ChatGPT as an enabling technology that could be used to further accelerate the process of turning data into insights. With this in mind, HARMAN conducted a pilot project to embed GPT functionality into Qlik dashboards. The first solution uses VizLib’s library of third-party visualisations.
Once users have posed their questions, ChatGPT is responsible for providing the answers. The natural language queries and datasets are sent to ChatGPT via the OpenAI platform. The process is transparent to end users because the front end is a Qlik dashboard, which eliminates the need for them to write SQL.
Senior Director, Data and Analytics, HARMAN International
Support AI Ready data
Artificial intelligence (AI) is a field of computer science that attempts to create systems that can mimic and outperform human intelligence. A subset of AI is machine learning (ML), where computers learn from data and are able to make predictions without programming. One type of machine learning is deep learning, which uses artificial neural networks to process information and learn in a way inspired by the human brain. Finally, within deep learning, we have generative AI, which focuses on generating new content.
Generative AI, or GenAI, is an AI system that can generate text, images or other data using generative models in response to human input. GenAI models learn the patterns and structure of their input training data and then generate new data with similar characteristics.
Organisations that want to use AI must create a data environment that is ready for it. This means more than just collecting lots of data or investing in the latest AI tools. It means creating AI ready Data: making sure that data is managed, governed and used in a way that aligns with the principles of clarity, quality and accessibility.
Data should be unbiased
The term 'AI bias' refers to the potential for biased results in machine learning and algorithms due to human biases in the original training data or the AI algorithm itself. These biases may lead to distorted outputs and potentially harmful outcomes.
To make sure AI isn't biased, organisations must have AI governance. In essence, AI governance is the creation of a set of policies, practices, and frameworks to guide the responsible development and use of AI technologies. AI algorithms can be black box systems that use data in an unfair way. Transparency practices and technologies help ensure that data is used to build AI systems fairly.
Data should be fresh
Real-time data is a continuous flow of data in motion. It is streaming data that is collected, processed, and analysed on a continuous basis. In order to meet the demands of today’s fast-paced business environment, companies must have the necessary technologies, such as Change Data Capture, Stream Data Capture, and Continuous Data Processing.
Data should be accurate
Data should be secure
The use of masking, tokenisation and access control techniques is essential to protect access to sensitive data and to carefully control the movement of data outside the private network. It also requires active engagement and education at all levels to embed ethical data use and security principles into the organisation's data culture.
Data should be discoverable
Moreover, the latest acquisition of Kyndi directly addresses the rapidly growing volume of unstructured data in the world. Qlik's incorporation of Kyndi's technology into its cloud solutions enriches decision intelligence by providing more comprehensive answers with enhanced context and relevance. This advancement strengthens the management and curation of enterprise data, fostering trust and consistency across organizations while upholding stringent governance and security standards.
Finally, Qlik Talend now offers a full stack of data integration, quality and governance capabilities to help organisations build the most advanced data fabric for supporting your business. This integrated data platform can act as the primary information, insight and action facilitator for generative AI so that it can be confidently applied across the organisation.
