2024 was a defining milestone for Qlik, as highlighted in our previous review by our Business Intelligence Lead and Managing Partner, Phuoc Tran Minh, in early 2025. Strategic goals were met, and the integration of Talend solidified Qlik’s position as the market’s most robust data integration platform.

But what lies ahead? 2025 and the beginning of 2026 have marked a fundamental shift in the ecosystem: moving from passive reporting to active, “agentic” analytics. Here are the key innovations shaping the daily operations of Qlik users right now.

1. From Assistant to Agent: The Rise of Agentic AI

If 2024 was the year of experimenting with Generative AI, 2025 was the breakthrough year for Agentic AI. Qlik no longer simply answers questions; it takes action.

These new AI agents execute complex sequences of tasks autonomously. They can detect an anomaly in sales data, analyse the root cause by comparing multiple data sources, and automatically generate a proposal for next steps – all without the user needing to build a query. Qlik Answers is pivotal here, integrating unstructured data (contracts, manuals, PDFs) into the analysis to ensure a true 360-degree view of the organisation.

2. Qlik Trust Score™: AI is Only as Good as Its Data

The biggest barrier to AI adoption is a lack of confidence as we know at GOODIN. Qlik addresses this with the Qlik Trust Score for AI, which automatically scores the reliability of data. As businesses build their own custom models on top of Qlik, the Trust Score ensures that AI does not draw conclusions based on flawed or outdated information. It is the “green light” management needs for automated, defendable decision-making.

3. The February 2026 Breakthrough: The Qlik MCP Server

The most significant update in early 2026 is the general availability of the Qlik MCP (Model Context Protocol) Server. This is a game-changer for AI Interoperability.

Rather than locking your data inside a single platform, MCP acts as a universal “USB-C port” for AI. It allows third-party assistants – such as Anthropic Claude, Microsoft Copilot, or your own internal LLMs – to securely “reach into” Qlik’s engine. This means your external AI tools can use Qlik’s governed measures and logic to provide answers that are actually accurate and grounded in your business reality. Here is more info!

“Qlik has gone so far beyond visualisations and dashboards: it has become the trusted intelligence layer for your entire enterprise AI ecosystem.”

Says Phuoc Tran Minh

4. Next-Level Integration: The Open Lakehouse

The Qlik-Talend merger has matured into a seamless Open Lakehouse architecture. In 2026, there is a massive emphasis on real-time data movement across Snowflake, Databricks, and AWS. Native support for the Apache Iceberg format allows enterprises to store vast quantities of data cost-effectively while avoiding vendor lock-in. Data quality is now managed by AI-assisted tools that rectify errors automatically as data moves through your pipelines.

5. User Experience: Beyond the Dashboard

Analytics visualisation has undergone a significant makeover to drive operations, not just viewing: Write-back Capabilities: Users can now modify or input data directly from a Qlik sheet back into source systems (like CRMs or ERPs). Discovery Agents: New “always-on” agents monitor your metrics 24/7 and proactively alert you to meaningful trends or anomalies before you even open a dashboard.

Conversational Interface: Interacting with data through natural language is now the standard. The dashboard has evolved from a primary interface into a supporting visual for deeper context.

Towards Autonomous Analytics

At GOODIN, we have followed Qlik’s journey closely, and the direction is clear: analytics is shifting from the “rear-view mirror” to real-time operational guidance. The Qlik MCP capabilities added in late February 2026 represent a “safe harbour” moment – providing a standardised, governed way to connect any AI tool to your most valuable data.

“The innovations of 2025–2026 represent a new era where data is a company’s most active asset. Agentic capabilities are already delivering massive value to end-users by automating the “boring” parts of data analysis and focusing on what matters: action.”

says Mikko Kuusela.

Is your organisation ready to leverage Qlik’s latest Agentic and MCP capabilities? At GOODIN, we help you translate technology into measurable business value and can train your entire organisation to make use of data and AI.

Reach out to our CEO Jarmo Rajala or Mikko Kuusela, Petri Viljanen, or Siru Saaristo. We are happy to help you find better ways to get the most out of your data and AI!

Our Business Development Lead Mikko Kuusela has worked in analytics for nearly three decades. This is the story of what he learned – and why one question stayed with him the entire journey.

Mikko has a habit of saying that his career has become more technical than he ever imagined as a young economics student.
But perhaps that’s exactly why he has held so firmly to one core idea. The most important job of technology is not to look complex. Its job is to help people succeed.

Where it all began

The year is 1997. Mikko starts his career at BasWare, working with budgeting and forecasting systems. Oracle consulting follows, then reporting, then business. Early on, a conviction takes shape that never changes:

The best solutions do not emerge on technology’s terms. They emerge when technology genuinely serves the business – with people at the centre.
In 2005, Mikko returns to BasWare and encounters QlikView. It changes his thinking. It is no longer just about reporting, but about analytics: the opportunity to understand the business more deeply, to spot patterns, to make better decisions.

#Qlik technology has been part of his career ever since. Around 20 years in total, more than 15 of them with Qlik directly.

The question that never went away

Alongside everything he learned, one thing kept nagging at Mikko. What if data entry could live in the same interface?
If viewing, analysing, and updating information could all happen in one place, a solution like that would serve the business in an entirely different way. No separate Excel files. No system-hopping. No unnecessary intermediate steps.

It’s a question he has heard from clients over the years countless times, too.

The answer arrived six months ago.

About six months ago, Mikko came across #Inphinity. He was immediately excited.

Inphinity enables data entry directly within Qlik – in the same interface where data is also analysed. No more separate processes, no more separate systems. One environment, one whole.

The concrete impact was visible quickly. In a client project, key metrics from around 50 companies were brought together into a single Qlik environment. Data entry, review, and utilisation – all in one place. It worked.

Why this matters – from a data empathy perspective

At GOODIN, we talk about data empathy. It simply means that data and solutions are not built for systems – they are built for people. Understanding users’ day-to-day reality and understanding what stories the data tells. Understanding the genuine needs of the business. Building something people will actually use.

When the process feels natural, users trust the data. When users trust the data, the organisation makes better decisions.
That is exactly what Inphinity delivers – and exactly what Mikko’s story is about.

30 years in analytics creates a certain level of #DataEmpathy

“The job of technology is not to look complex. It’s job is to help people succeed.” — Mikko Kuusela, GOODIN

#GoodIn #DataEmpathy #Qlik #Inphinity #Analytics #PeopleOverProcesses

Generative AI has emerged as a transformative force, impacting all facets of an organization and influencing existing tools and solutions. While it enhances efficiency, Generative AI introduces new requirements for data structures, security protocols, and corporate governance within organizations. Despite its capabilities, it’s crucial to note that Generative AI doesn’t assume responsibility for decisions and actions; that remains the responsibility of human operators.

To realize the full Return on Existing Investments (ROIe), organizations must ensure that users maximize the utilization of these tools and solutions. While significant investments are made in technology, equal attention should be given to nurturing the skills of the teams, the potential generators of profit. It is imperative to monitor how users leverage these investments actively. The real value lies not just in the technology itself but in how effectively it is utilized by individuals within the organization.

Data Empathy – a Holistic Approach

When investing in new technology, organizations must concentrate on three primary areas. The principal impetus for any investment typically originates from business needs and potential benefits. The technology team plays a pivotal role in narrowing down and selecting appropriate technologies for the identified requirements. In theory, collaboration between business and technology teams can yield flawless reporting systems and dashboards. However, the recurring challenge lies in the third crucial area – the organization.

The diagram above illustrates the common areas for any organization and their interdependencies. In Data & BI projects, collaboration between Business and Data & Tech teams is typical. However, the ultimate success is contingent upon whether people use the solutions, find them easily applicable in their roles, and whether the business demands their usage, all of which require continuous monitoring.

To achieve Return on Existing Investments (ROIe), it is crucial to identify user needs through Work Design. Understanding the information required for everyday tasks, assessing data skills and literacy, and providing targeted training and coaching are vital. Leadership, management systems, and organizational culture play pivotal roles in achieving ROIe, impacting the human element significantly. Without an organizational emphasis on data use, investments may not yield expected results.

The path to realizing the full return on existing investments involves focusing on the organization and its people, embracing Data Empathy.

Decision Dimensions – Aligning People, Processes and Data

Management Information Systems are 90% people said my professor in 90’s. Today we have so much new and existing tech that we might not remember this truth. We are blinded by expanding number of technologies and solutions and often users are not able to keep up with the pace of development. Introducing new systems alone is insufficient; attention must be directed towards role design. This entails understanding how work aligns with the new system, necessitating changes to fully realize the benefits of the investment.

Aligning People, Processes and Data. People within an organization occupy specific roles, belong to teams, and engage in processes where decisions take place. To enhance data utilization and identify gaps in both people’s capabilities and data availability, an understanding of work design is essential. Mapping out a user’s typical day, identifying decisions tied to processes, and determining data requirements for those decisions are critical. Data presentation should align with actionable intelligence requirements, meeting the demands of users and fostering motivation for increased data utilization. Importantly, it’s essential to identify the role of GenAI/LLM solutions in the user’s daily workflow, not just focusing on technical solutions.

In conversations with numerous leaders and data & BI professionals, it’s surprising how little attention organizations have given to their people. While many have heavily invested in Cloud Data Platforms, AI solutions, and BI fronts, acknowledging that people are the biggest challenge in reaping the full benefits of these investments, very few have taken tangible steps to bridge the gap between people and data.

Now it is time to do just that.