Qlik: “Transformation Only Happens When You Do Something Different and Create Value”

Qlik, a leading data analytics company, celebrates its 30th anniversary this year. With an innovative approach to data visualization and analytics, Qlik has empowered thousands of companies to make smarter, more strategic decisions. To learn more about the company and its vision for the future, we interviewed James Fisher, Qlik’s Chief Strategy Officer. Fisher, who has extensive experience in the technology industry.

Who is James Fisher?

James Fisher is the Chief Strategy Officer at Qlik, with a decade of experience in the company. His career trajectory goes back to key roles at SAP and Business Objects before starting his career at PricewaterhouseCoopers, where he specialized in performance management, analytics, data and technology.

Fisher has extensive product development experience. He previously served as Chief Product Officer at Qlik until last year, when he assumed his current role as Chief Strategy Officer.

This transition reflects Qlik’s evolving business landscape, highlighting technology advancements, team scalability and product innovation under Fisher’s leadership. His multifaceted experience and deep industry knowledge position him as a driving force behind Qlik’s continued success and adaptive strategies.

Acquisition of Kyndi and unstructured data

– You recently acquired Kyndi, a company specializing in unstructured data. Could you explain the importance of this data in the context of the adoption of artificial intelligence in business operations and the benefits that come with it?

Looking back at the evolution of the data and analytics space, we have talked a lot about the traditional challenges such as volume, velocity, variety and veracity of data. However, with the advent of artificial intelligence, especially generative artificial intelligence, the landscape has completely changed. We are faced with a volume and velocity of data that was not previously contemplated, and in the specific case of generative artificial intelligence, the veracity and complexity of data has not been fully addressed. Why is this relevant? Well, today’s organizations handle huge amounts of structured data, for which they have developed effective tools and analytics. However, they also have at their disposal vast amounts of unstructured data, such as documents, reports or social media interactions, which contain significant value in understanding both the organization and the market in which they operate.

In this sense, our investment in Kyndi responds to the need to extract more information from this unstructured data. We firmly believe that by combining this data with structured data, we can provide a much more complete and contextualized set of insights, which in turn will enable more informed and strategic business decision making. Ultimately, that is our main goal.

– How do you integrate unstructured data insights into structured data analytics? How do you combine them?

With Kyndi, we provide a similar capability as it has historically to provide enterprise search within that unstructured data. We collect that data, analyze it, and provide an enterprise search capability, and then merge those inputs with the structured data already in the Qlik cloud. Our integration and ultimately our goal is to incorporate Kyndi’s technology into the Qlik cloud platform, so we can tie those capabilities together within the Qlik cloud.

Top AI trends for 2024

– Qlik has published the top trends in AI for 2024. In that context, you talk about DNA testing for data, as to how clean that data is to work in AI. How would this DNA testing for data work?

One of the biggest challenges with AI, especially generative AI, is trust in the underlying data. This has been particularly important in AI, but it has also been a crucial aspect in analytics for a long time. Therefore, we have historically developed a set of advanced capabilities to understand the provenance of that data. These capabilities include data traceability, an AI catalog, an enterprise glossary, and health assessment tools, which help determine the usefulness, accuracy, and reliability of the data. We then communicate to users the potential uses of that data, providing them with information such as the latest update, presence of errors, compliance with specific formats, and who else is using that data. This helps guide future use. By understanding these aspects and leveraging data quality and data preparation tools, we can improve and enrich the quality of insights generated by generative AI, minimizing errors and hallucinations within the data.

– I would like to address two respects in the press release on the top AI trends for 2024. First, what are the skills needed for professionals to succeed in the future of data analytics work? And second, what advice would you give to a young professional who is just starting their career as a data analyst?

When it comes to really the notion of AI and analytics in general, data literacy has been a huge barrier to organizations getting value from their information. And there are two elements to that. The first element really is about knowing how to work, prepare, create, analytical insights that you can use to generate answers to questions. The same applies to how to build a model, how to train a model and the right ways to do it, how to deploy a model, how to protect the intellectual property associated with developing the model. All of those things are critically important.

The other element has a lot to do with the end user and the notion of trusting the insights that you give them. I always say that transformation only happens when you do something different and create value. If you keep doing the same thing over and over again, you will never change or you will keep getting the same results. One of the biggest barriers to creating value from analytics is not necessarily the quality of the insights themselves, but really what people do with it. So how do you make those insights executable? How do you make them trusted and how do you make people trust what the data is telling them, what the dashboard is telling them? What is the AI telling them?

It’s a combination of the right technology that leverages even the AI capabilities within the product sets to lower the barriers to entry, to drive more of their consumption in terms of the technology itself. But also that experience, that business knowledge that comes with asking the right questions, knowing how to interpret the results fully and again, knowing how to test hypotheses as well.

In terms of the second point, some of the advice I give you, I think it’s very easy for people entering the world of data and analytics to think about the technology. It’s very easy to think about product training, learning how a product works. And then I can be good at data analytics. And there’s a certain amount of truth to that. Technology is critically important, but understanding data literacy and AI, understanding what makes a good visualization, understanding how to structure a question to get the right result, understanding the domain you want to be in, I also think is equally important. So, focusing on the technology, but not just focusing on the technology, but also understanding data literacy and analytics, understanding what the best practices are. That will enrich your work and your perspectives going forward.

Qlik Stage Launch

– Let’s talk about Qlik’s launch of Staige. What are the typical challenges companies face when implementing artificial intelligence into their processes?

Second, there is the question of how to increase the use of the tools available within the organization. Many companies still struggle to get value from the dashboards they built years ago. While natural language interfaces and other technologies are making data easier to access, there are still a number of improvements, such as using low-code technologies and training business analysts rather than just data scientists, that can make data more accessible and useful to more people.

Finally, there are the more advanced use cases for artificial intelligence. There are several ways to think about how to apply generative artificial intelligence, such as implementing LLMs. But choosing the best implementation option can be tricky, especially when it comes to combining structured and unstructured data. This is where Qlik investments, such as Kyndi, can be of great help to customers.

Therefore, the key challenges companies face when implementing artificial intelligence are establishing a solid data foundation, increasing the use of available tools, and addressing more advanced use cases.

Industry information and news at Qlik Connect Orlando 2024

– I’d like to know, in which sectors does Qlik envision the greatest growth opportunities?

We operate globally. We have customers in a variety of industries around the world, of all sizes. What is particularly relevant right now is the question about the use of generative artificial intelligence. While we have strong strengths in the public sector, government, technology, and retail, I think this wave of innovation around generative artificial intelligence is happening everywhere, in every industry, and in organizations of every size. This presents us with an exceptional opportunity.

– What innovations will we see at Qlik Connect Orlando 2024?

The most exciting thing we will see is everything we are accomplishing with Kyndi by integrating structured and unstructured data to generate new insights. We announced the acquisition in January and will be showcasing the new products at Qlik Connect. In addition, there will be a variety of other innovations as we consider the entire data flow.

We especially focus on how we can help customers create data products, enrich the analytics cycle, and provide data for AI and generative AI use cases. So the idea of data products will be something we look forward to sharing, along with our vision and planning, and we hope to demonstrate some interesting technologies in Orlando in June.