Written by GOODIN’s BI LEAD – Phuoc Tran Minh

If you work in data and analytics, you are used to the whiplash. Every few years, the industry promises a new architectural silver bullet that will finally solve the reporting backlogs, the data quality nightmares, and the governance headaches. Recently, that silver bullet has been Data Mesh.

I will be the first to admit I have been a strong advocate for it. The core promise of shifting data ownership away from a centralized IT bottleneck and putting it directly in the hands of the business sounded like the exact antidote large organizations desperately needed. And intellectually, it still does.

But after watching these implementations unfold in the real world, we need a serious reality check.

The War Story Nobody Publishes

I have watched some of the largest companies in the world, flagship enterprises, household names, fail at this problem repeatedly. Not once. Repeatedly. The pattern is almost identical every time.

They invest heavily in a centralized Data Warehouse. The project runs 18 months over schedule. By the time it delivers, half the business requirements have changed. The central data team, talented as they are, becomes a permanent bottleneck. They understand the technology but not the business, and every new report request joins a queue that never clears. The business loses patience and starts building its own analytics in spreadsheets, local databases, and disconnected BI tools. Shadow IT blooms.

Here is the part that rarely gets said out loud: most of these organizations do not actually choose between the two bad options. They end up with both simultaneously. A stagnating, increasingly outdated Data Warehouse on one side, and a sprawling mess of shadow self-service analytics on the other. They are paying for the warehouse they cannot retire and suffering the chaos of the self-service sprawl they cannot govern. The worst of both worlds, billed as a strategy.

When it fails, the instinct is to blame the technology or the consultant. Never the model itself.

The BI Manager’s Dilemma: both choices end in pain.

The Numbers Are Damning, Across the Board

The failure of centralized data platforms is not anecdotal. Gartner, IDC, and Forrester have consistently placed the failure or underperformance rate of traditional Data Warehouse initiatives at somewhere between 50% and 80% over the last fifteen years. The specific number varies by study and definition of failure, but the directional truth has been stubbornly consistent: most of these projects do not deliver their intended ROI.

The unsettling part? The decentralized alternative is not faring much better. Zhamak Dehghani first published the concept of Data Mesh already in May 2019 to much acclaim, but very few successful implementations have since emerged. In 2022, Gartner predicted that Data Mesh may become obsolete before reaching full maturity, and estimated that only 18% of organizations have the necessary governance maturity to successfully adopt Data Mesh architecture. Unfortunately, these estimates still seem highly accurate.

Two Books, One Argument

The industry’s frustration has recently found a voice in two very different books worth naming directly.

Martyn Jones’ F*CK DATA MESH says the quiet part loud. The title alone captures the mounting exhaustion with architectures full of grand theory about domain sovereignty that collapse the moment they meet an actual organization. The sentiment is valid. Data Mesh implementations have frequently produced fragmented, expensive messes that delivered less than the warehouses they were meant to replace.

At the other end of the conversation, Phil Le-Brun and Jana Werner’s The Octopus Organization, a recent Harvard Business Review publication, offers a compelling biological metaphor for what a genuinely decentralized, high-performing organization looks like: distributed intelligence at the edges, fast local decision-making, coordinated but not controlled from the center. It is a useful model, and one worth keeping in mind. But it is also a model that assumes the organizational foundations are already in place to support it.

Both books are participating in the same conversation: what does genuine, functional decentralization actually look like, and why does it keep failing in practice?

The Problem Is Not the Vision. It Is the Execution Trap.

Here is the reality CIOs must face head-on: the core principle of Data Mesh, business ownership of data, is fundamentally correct. Gartner’s recent CIO Agenda research is unambiguous on this point: the organizations winning at digital delivery are the ones with genuine business ownership of technology outcomes, not the ones that kept everything inside a central IT function.

The vision is not wrong. But the enterprise execution of it has been a disaster, for a specific and diagnosable reason.

Data Mesh implementations typically fail because they confuse organizational autonomy with infrastructure engineering. By asking business domains to take ownership of their data products, companies inadvertently require business analysts to master Git repositories, YAML configurations, dbt transformations, and CI/CD deployment pipelines just to publish a clean dataset. The autonomy on offer turns out to be an engineering curriculum in disguise.

Business units do not have the technical skills, the budget, or frankly the incentive to become software engineering teams. The result is anxiety, stalled adoption, soaring cloud costs, and, ironically, a new generation of exactly the same shadow IT silos the whole exercise was supposed to eliminate.

We gave domains the responsibility without giving them the means to carry it. That is not decentralization. That is delegation of blame.

The Question We Actually Need to Answer

So we are stuck. Going back to the centralized Data Warehouse means burying every request in an IT backlog and watching the business work around you. Pressing forward with a full Enterprise Data Mesh means handing business analysts an engineering toolkit they cannot use and watching the whole thing fragment.

The question is not which of these two paths to choose. It is how to escape the false choice entirely.

That is what this series is about. The argument is for a pragmatic middle path, one that keeps the principle of business ownership intact while stripping away the engineering complexity that has been killing it in practice. And by Part 3, we get specific about the technology that makes it executable.

COMING UP IN THIS SERIES
Part 2: Why real agility and ownership requires authority, understanding, and motivation, and why small business-driven teams are the only structure that delivers all three.
Part 3: How Qlik Cloud Analytics breaks the engineering bottleneck and lets your team focus on business logic instead of infrastructure and governance processes.
Part 4: How to escape data silos while doing fast domain owned data iteration without a data lakehouse?
Part 5: Four lean governance rituals that bulletproof your data products without complicated software or bureaucracy.

The era of the IT-driven rigid data factory is ending. But the era of the over-engineered, under-delivered Data Mesh needs to end with it.

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.