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 A stagnating, increasingly outdated Data Warehouse on one side, and a sprawling mess of shadow self-service analytics on the other.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.

Mikon reaktio oli erityisen kiinnostava – hänelle nämä ominaisuudet
tuovat mieleen jotain hyvin tuttua.

“Tämähän on kuin 2018 – mutta parempi”

– Mikon tarina Podium Datasta. Mikko on pitkän linjan #Qlik-veteraani, ja kun hän ensimmäistä kertaa näki
uudet Data Products -ominaisuudet, hän hymyili tunnistavan hymyn.

Vuonna 2018 Qlik hankki yrityksen nimeltä Podium Data, ja Mikolla on siitä
omakohtainen kokemus – hän ehti työskennellä kyseisen tuotteen kanssa
ennen kuin se integroitiin Qlikin tuoteperheeseen nimellä Qlik Catalog.

Qlik Catalogin ydinajatus oli ratkaista se ikuinen ongelma: tekniset ihmiset ja
liiketoiminta ihmiset puhuvat samoista asioista eri kielillä. Mitä tarkoittaa
“asiakas”? Entä “tuote”? Catalogin avulla sekä data-ammattilaiset että
liiketoiminta ihmiset saattoivat määritellä ja arvioida käsitteitä samassa
paikassa – yhdessä. Mikon mukaan ajatus oli oikea ja tuote toimi, mutta oli
käyttäjän näkökulmasta kenties hieman tekninen. Visio oli aikaansa edellä.

Nyt visio alkaa näyttää todelta

Kun kävimme Mikon kanssa läpi Qlikin uusimpia julkaisuja, tunnelma oli selvä:
tämä on sitä, mitä 2018 tavoiteltiin – vihdoin kypsänä muotona.

Kolme asiaa erottuu erityisesti:

• Data saadaan kaikkien näkyville – Data Marketplace toimii yhtenä
ikkunana organisaation datoihin.
• Datan laatu selviää – Trust Score kertoo selkeästi, kuinka luotettavaa
data on. Ei enää arvailua.
• Dataa voidaan helposti jakaa eteenpäin – REST- tai OData-
connectorin kautta data liikkuu sujuvasti jatkokäyttöön.

Ja parasta kaikessa? Kaikki tämä löytyy yhdestä käyttöliittymästä: olennaiset
datatuotteet, niiden laatu, missä niitä käytetään, mistä data on peräisin ja
minne se kulkee – Data Lineage kertoo koko tarinan.

Miksi tämä on tärkeää juuri nyt?

Data ei ole enää vain raportoinnin raaka-aine. Kun tekoäly ja AI-agentit
hyödyntävät dataa yhä enemmän, datan laatu, löydettävyys ja luotettavuus
nousevat aivan uuteen arvoon. Jos AI saa syötteekseen huonoa tai
väärinymmärrettyä dataa, seuraukset voivat olla vakavia.

Qlik Cloudin uudet ominaisuudet vastaavat juuri tähän tarpeeseen. Kun
datatuotteet on määritelty selkeästi, niiden laatu on arvioitu ja ne ovat helposti
saatavilla – niin ihmisille kuin koneille – ollaan oikeasti valmiita tekoälyaikaan.

Yhteenvetona voinee todeta: hyvät ideat löytävät aikansa!

Podium Datan visio oli oikeassa. Se syntyi vain hieman ennen aikojaan. Nyt
teknologia, markkinat ja tarve ovat kohdanneet – ja Qlik Cloud vie tätä
kehitystä eteenpäin tavalla, joka saa kokeneenkin Qlik-ammattilaisen
hymyilemään. Mikolle se on selvästi palkitseva hetki.
Seuraamme kehitystä innolla Mikon kanssa ja autamme asiakkaitamme
Goodinilla ottamaan nämä mahdollisuudet käyttöön.

Terkuin, Mats (von Hertzen), GOODINilta

Kiinnostuitko? Laita meille viestiä!

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

1. Half still have no real touchpoint

Around 50% of participants have never opened an LLM before the course. This isn’t about the technology—it’s about caution, uncertainty, and the lack of guidance. Once people start experimenting in a supported environment, the hesitation fades quickly. Many express the same thought: “I didn’t know what I was supposed to ask, so I didn’t dare to start.”

2. Usage falls into three clear levels

Among those with at least some experience, usage forms a consistent three-tier structure:

• 90% use LLMs very lightly: translations, summaries, and simple edits.
• 10% use them more actively, but still at a surface level. Few users actually use frameworks, build consistent logic, or design workflows around the model.
• Deep usage is extremely rare.
  • Custom GPTs, structured tools, and process-level thinking are still marginal.

This pattern repeats across almost every organisation.

3. Agents generate interest – but they are not simple

Agents and custom GPTs now come up in almost every discussion compared to early 2025. Interest is strong, but real hands-on work is still limited. Across our training groups:

• About 50 people have built a custom GPT.
• About 20 people have built an agent (usually as a team).

Building an agent isn’t a “just do it” button. It requires quiet technical intuition, process thinking, and a willingness to experiment. Many are only now developing these foundational skills. This is why claims that “2025 is the year of agents” is more about marketing than reality. The real “agent year” in everyday work is likely closer to be realised end of 2026–2027.

4. Understanding is rising quickly – usage more slowly

Between January and November, the change is clear:

• People now better understand what LLMs do and don’t do.
• They recognise the importance of context.
• The model is seen more as a conversation partner, not just a text machine.

Yet everyday use is still largely task by task, not a continuous partnership with the model.

5. The “sparring partner” mindset works

One of the strongest findings relates to mindset. When the model is seen as a sparring partner, usage becomes more natural and relaxed. In our courses our mission, which is stated at the start of the course, is to make LLMs your sparring partner. At the end we ask how we succeeded and the results are striking:

• 95% of participants respond to our feedback survey.
• Of those, 97% say the model became a sparring partner during the course (N=1000).

Once people understand how an LLM works and where its limits lie, their usage becomes instinctive. The barrier isn’t technical—it’s emotional.

6. The biggest shift is in thinking, not yet in routines

The most notable change in 2025 is not what people do with LLMs. It is that more and more people understand what an LLM is, what it can be used for, and how it should be used. This unlocks internal conversations, role clarity, and new ways of dividing work.

And the one fact: The term “Artificial Intelligence” as a singular is the most misguiding term and should be completely abolished to create better understanding.

What does this tell us about Finnish organisations in 2025?

Based on everything we’ve seen in our training data, the situation looks like this:

🙌 Interest is growing relatively fast, but not like we inside the “AI bubble” think.

🙌 Usage is growing slowly—especially if the emotional barrier isn’t lifted.

🙌 Deep usage is still rare. People think this is a new Google, and naturally that will limit exploration as what you do with Google is already a habit—and as we know habits are hardest to change.

Finland is now in a phase where understanding is expanding, but everyday AI-Human working habits are still forming. This is a natural stage—and right now is the ideal moment for organisations to build their LLM strategy before the next major shift arrives.

Key Takeaways from #Harvest event 9 October 2024

Generative AI (GenAI), and artificial intelligence (AI) in general, is no longer a promise of the future; it’s the reality of today. During the Harvest event on October 9th, 2024, it became clear that AI’s potential, particularly GenAI, is immense, yet many organisations are still standing on the sidelines.

Why? Not because the technology isn’t mature—it is. The real challenge lies in the myths and misunderstandings that continue to slow its widespread adoption. As DB Schenker’s Samuli Salmela explained in his keynote speech, these misconceptions prevent businesses from fully embracing the transformative power that GenAI, especially, offers.

As leaders, it’s time for us to move beyond the myths and recognise AI as a strategic partner, not just a tool. AI can bring profound improvements to our organisational processes and, ultimately, transform the entire business. The question is no longer whether AI is ready – it is. The real question is whether our companies are ready to learn, experiment, and utilise AI in the right way. The businesses that seize AI’s opportunities now will thrive, while those waiting for the “perfect moment” – that never comes – will be left behind.

Let’s explore the key takeaways and insights from the event by addressing the most prominent myths about GenAI and their practical implications for our organisations in overcoming them.

Myth 1: “AI is difficult and expensive”

This belief often stems from a fear of the unknown. In reality, AI has become increasingly accessible, with many easy to use  tools available. The real challenge lies not in the technology itself, but in our willingness as people to adapt. As leaders, we must reframe AI as an investment in both the company’s as well as the people’s future rather than a burdensome expense or a new thing that ends up benefitting no-one.

Myth 2: “GenAI makes mistakes, so it needs constant human supervision”

While it is true that AI is not infallible, neither are humans. The key is to view AI not as a replacement for human intelligence, but as a powerful complement to it. This symbiosis of human intuition and machine processing power can lead to unprecedented insights and innovations for both organisational, as well as wider, good. A need for human oversight in AI related projects depends greatly on the use case and its risk profile. As we learn to understand and trust AI more, we can relax the oversight.

Myth 3: “We don’t have good enough data to use AI”

Perfect data is a mirage. Instead of waiting for an ideal dataset, we should focus on cultivating a data-driven culture where continuous improvement is the norm. AI can actually help us identify gaps in our data and AI can also allow us to start utilising unused or previously difficult-to-use data assets. AI can also refine our collection processes, so again, rather than thinking of it as a technology, think of it as an added competence to your organisation and treat it accordingly. 

Myth 4: “Our data is not safe with GenAI”

In an age of increasing digital threats, this concern is valid. However, it should not paralyse us. Instead, it should motivate us to implement robust data governance frameworks. By doing so, we not only protect our assets but also build trust with our stakeholders. It is also important to use technologies that are secure and enterprise-grade, such as the Microsoft offering.

Myth 5: “AI doesn’t really think”

This myth touches on deep philosophical questions about the nature of intelligence. While AI may not “think” in the human sense, it can process information and identify patterns at a scale beyond human capability in certain cases. Our role as leaders is to harness this power while providing the context, creativity, and ethical considerations and judgement that only humans can offer. This is one of the reasons it is important to focus on your people at the same time as you focus on technology.

Myth 6: “It’s just hype. Companies aren’t getting real benefits”

Scepticism is healthy, but it should not blind us to the real-world impacts of AI. From predictive maintenance to personalised customer experiences, AI is already delivering tangible benefits across industries and will also require a new set of measuring benefits. As leaders, we need to look beyond the hype and focus on practical applications that can drive our businesses forward. AI is like any major technology disruption; its short term implications are overestimated and long term impact under-estimated.

Myth 7: “The models aren’t ready; it is better to wait”

In the rapidly evolving world of AI, waiting for perfection is a luxury we cannot afford. The most successful organisations will be those that adopt a “learn fast” mentality, embracing current AI capabilities, while staying agile enough to adapt to future developments.

Building a Human-Centric, Data-Driven Culture: The Key to Successful AI Adoption

It is essential to remember that adopting AI goes beyond technology—it fundamentally revolves around people. To fully realise AI’s potential, the entire organisation must be engaged in the journey.

Since we’ve been talking about AI like a colleague, we figured, why not ask Copilot  itself, which of these myths resonated the most? “The myth that struck a chord was, ‘GenAI makes mistakes, so it needs human supervision.’ While it’s true that responsible AI use requires oversight in critical fields like healthcare, where accuracy and ethics are non-negotiable, the idea that GenAI always needs human supervision is a bit outdated.

This requires cultivating a culture that embraces data and AI literacy, with human leadership at its core. Such a culture encourages curiosity and experimentation, valuing human creativity and judgement. In this way, employees see AI tools as enablers rather than fearing replacement. This approach creates an environment where both human and artificial intelligence can thrive, laying the groundwork for genuine innovation and growth.

But as any good farmer knows, harvesting is only the beginning. The true value lies in how we refine those fruits—transforming them into products, services, and tangible results that drive real organisational impact. And just as importantly, we must sow new seeds for future growth by budgeting wisely and investing in the AI capabilities that will shape the years ahead. After all, success isn’t just about this harvest; it’s about laying the foundation for future seasons of prosperity.

Authored by:
Kira Sjöberg, GOODIN, Sami Masala & Nino Ilveskero, AIThink & CoPilot, Microsoft. 

Understanding the Learning Stages People in Organizations go through

1. Beginner Level: Grasping the Basics

The journey begins with foundational knowledge. For data literacy, this includes understanding data types, basic data manipulation, and the significance of data in decision-making. For AI literacy, it involves an introduction to AI concepts, what AI can and cannot do, and real-world applications. At this stage, short, introductory sprints focused on key concepts help demystify complexities and lay the groundwork for more advanced learning.

2. Intermediate Level: Enhancing Skills through Application

Once the basics are understood, employees should start applying their knowledge to real-world scenarios. This could involve structured projects or challenges where learners manipulate datasets or build simple AI models relevant to their roles. This stage is crucial for reinforcing concepts and gaining confidence. Naturally understanding that not everyone will utilize data or AI on this level in their roles, but understanding the side of applying is important. Generative AI utilization is however another relevant application mode often in such cases. Organizations can support this through workshops, guided training sessions, and practical hands-on projects that encourage active learning. One example of this is the GOODIN and Splended Data Learning Sprint.

3. Advanced Level: Specializing and Innovating

As learners become more comfortable, they can move into specialized areas such as predictive analytics, machine learning, and advanced data visualization techniques for data literacy. For AI literacy, this might include deep learning, neural networks, or robotics. Advanced learners should engage in longer, more complex sprints that challenge their understanding and encourage innovation within their specific areas of interest. This demands a “fail fast” or “learning by doing” -type of learning culture in organizations and positive and encouraging management practices and commitment that enable failing for learning.

4. Expert Level: Leading and Mentoring

At the expert level, individuals are expected not only to be proficient but to lead initiatives and mentor others. They stay abreast of industry trends and continuously adapt to new technologies. Here, learning involves self-directed projects, leadership in sprints, and contributing to strategic decision-making with data-driven insights become real life value for business.

Implementing Learning by Doing: The Sprint Method

A learning sprint approach can be highly effective in progressing through these stages. Each sprint focuses on a specific skill or project, encouraging rapid learning and application. Here’s how it can work:

• Define Clear Objectives: Each sprint has specific, measurable goals to ensure focus and alignment with business objectives.
• Time-bound Challenges: Limit sprints to a few weeks to maintain urgency and engagement.
• Cross-functional Teams: Include employees from different departments to foster diverse perspectives and collaborative problem-solving.
• Reflect and Iterate: At the end of each sprint, gather feedback and reflect on lessons learned to improve the next cycle.

Supporting the Journey

Supporting employees through this journey requires more than just providing educational resources; it involves creating an ecosystem that promotes continuous learning and application. This includes access to the latest tools and technologies, opportunities for peer learning, and a culture that celebrates experimentation and learning from failure.

As they become more fluent, they will be able to leverage these skills to innovate and lead in their respective fields, ensuring the organization stays competitive in a data-driven future making sure the human drives the AI and not vice versa.

So Why Bother with Data Utilisation Design?

You can think of data as this treasure trove of insights just waiting to help you make your next big move. It is not just about having loads of data as data that is not used is a waste of money; it is about knowing what to do with it and actually utilising it. But naturally if you have not thought about the why’s, what’s or how’s that can be hard. So we collected a list to help you on the way. Here is why you should care about data utilisation design:

• Make Smarter Decisions: When you base your decisions on what the data is telling you, it is like having a crystal ball kind of. It means you are making moves based on what is actually happening, not just gut feelings. And the best scenario is of course to combine the data with you gut feeling as that should not be undervalued either as it is usually data that comes from experience.
• Stay Agile: Markets move fast, and data helps you keep up. You will see the trends as they are happening, so you can steer your team in the right direction without missing a beat.
• Spot Risks and Opportunities: It is like having a map and a flashlight in a dark cave to put it in a simple analogy. Data helps you see where the pitfalls are and where the gold is hidden.
• Keep Your Customers Happy: By understanding what your customers are into, you can tailor what you do to match their expectations. It is a win-win – they get what they want, and you get their loyalty.
• Drive Innovation: Data is POTENTIALLY a goldmine of insights that can spark new ideas. It is all about finding better ways to do things, making your business stand out and the knowledge of market trends, opportunities, cross-level cooperation and so forth are human-led but when backed up by data the delivery can really help you jump over several hurdles.

The importance of data utilisation design for business leaders cannot be hence overstated. It is a critical competency that enables leaders to navigate the complexities of the digital age with confidence and foresight.

In the journey towards data-driven excellence, the role of leaders is not just to manage data but to inspire a culture where data is a strategic asset, driving every decision, every innovation, and every success together with the human aspect that lead the change.

Here is a To-Do List for you to think about when wanting to get started:

Here are the first few steps to take that’ll set you on the right path:

1. Set Clear Goals: Decide what you want to achieve with your data and and your people. It could be improving customer satisfaction, boosting sales, or streamlining operations. Having clear objectives will help you focus on the data that matters and help your people understand how to utilise it.
2. Get to Know Your Data: Take a moment to understand what data you already have, where it is coming from, and how it is being used and if it is not being used, why? It is about getting the lay of the land before you start digging deeper.
3. Build a Data-Savvy Team: Surround yourself with people who get excited about data! If your team can understand and use data effectively, you are already halfway there. There are several methods and services available to enable change with for instance GOODIN and Splended designed data learning sprint to get everyone up to speed.
4. Invest in the Right Tools: There is no shortage of tools out there to help you collect, analyse, and visualise data. Find the ones that fit your goals and your budget. It is about making your life easier, not more complicated.
5. Start Small and Scale Up: You do not have to build the full picture in one go. Rome wasn’t built in one day either. Start with a small project or area where data can make a difference, learn from it, and then gradually expand your data initiatives.

We are also happy to help to get you going so just be in touch!

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.