Business and data strategies must be closely linked, legacy systems and their limitations must be addressed, and education must happen on both sides. Business leaders need stronger data literacy to recognize where technology can truly help, while data teams must return to the fundamentals of analytics - from understanding processes, quality data modeling, and documenting transformations, to connecting data with business logic.
Martin Markač, Line of Business Director – Data & Analytical Solutions at Solvership, emphasizes in his interview for ICTbusiness.info that cloud technology has moved from an optional investment to a business necessity. He notes that its main contribution lies in accelerating value delivery and freeing teams from infrastructure concerns. According to Markač, we are living through a period of major change that is reshaping today’s data world.
He adds that the rise of the modern data stack has enabled even smaller companies to build competitive data platforms. At the same time, it has created confusion in the market due to an overabundance of tools and aggressive sales tactics. Markač highlights the urgent need to connect business and data teams, because without a clear business purpose, every data transformation remains only a technical experiment. He also explains how Solvership helps organizations navigate this transformation by combining the technological and organizational sides of change.
Is Cloud in the Data World Still a Choice or a Necessity?
Cloud adoption first gained momentum in the application world, where reacting quickly to load changes was critical - for example, with web applications and e-commerce platforms. Peaks were unpredictable, and the cloud solved that problem through auto-scaling and flexible pricing.
While the market focused on advanced examples like Netflix, Airbnb, Meta, or Google, the reality for most organizations was much simpler, and still is. Many companies are still trying to properly establish classic analytics, let alone real-time data platforms, machine learning, or AI solutions.
As cloud technology matured, price became less important and the focus shifted to simplicity, agility, and speed of value delivery. Today, businesses do not choose the cloud only because it is cheaper, they choose it because it allows teams to spend less time managing infrastructure and more time delivering business results.
When we look at pioneers of the modern data stack such as Snowflake AI Data Cloud, dbt Labs, and Looker, we see they created an entirely new market. Small and medium-sized companies that once lacked budgets for large investments or internal data teams can now build platforms that compete with - and sometimes outperform - those of major enterprises. Today, few seriously question the future of cloud data platforms.
How Has the Data World Changed in the Last 10 Years?
The data world has changed dramatically over the past decade, especially from a technical perspective. There are now countless tools, platforms, and approaches promising faster, simpler, and more scalable data processing and analytics. However, this explosion of choice has also created major confusion.
Today, we live in a vendor-driven market where sales tactics are increasingly aggressive and the number of providers and buzzwords feels endless. Companies often experience choice paralysis: they do not know who to trust, which tool will actually help, and which one only looks impressive in a presentation.
Meanwhile, many organizations invested heavily in internal data teams and tooling, yet results often failed to meet expectations. Instead of becoming strategic business partners, data teams in many companies became isolated silos focused on technical challenges and ideal architectures, while real business needs were pushed aside. The gap between business and data teams is, unfortunately, wider than ever.
How Do Business and Data Teams Finally Speak the Same Language?
Primarily through education - for decision-makers and internal teams alike.
Although complexity previously benefited parts of the market, AI has unintentionally disrupted that model. It has accelerated and automated tasks that teams used to spend too much energy on. Pressure to deliver faster results has also made one thing clear: AI depends heavily on quality data, reliable data models, and well-understood business processes.
Strong AI outcomes are rarely the result of technology alone. They come from understanding how the business works, how processes generate data, and how that data should be structured and governed.
Why Is This So Difficult in Large Organizations?
It is difficult because this is not primarily a technology problem - it is an organizational one.
The first step is to stop launching change initiatives simply for the sake of change. Every data initiative must have a clear business objective and measurable purpose.
Business and data strategies must be aligned. Legacy systems must be modernized or integrated realistically. Education must happen on both sides. Business teams need data literacy, while data teams must return to the basics of analytics: understanding processes, data modeling, governance, and connecting data to business logic.
Domain knowledge is also essential for every senior member of a data team. Somewhere along the way, many organizations forgot the difference between a Software Engineer and a Data Engineer. The data world has always been more focused on context, meaning, and understanding the processes from which data is created. A Software Engineer’s role is primarily to build robust, scalable systems and reliable products.
Without business purpose, every data transformation is just a technical experiment.
How Does Solvership Help Companies With Data Transformation?
At Solvership, the goal is not simply to move systems to the cloud. The goal is to help organizations redesign their data ecosystem so it becomes sustainable, flexible, and profitable.
This includes:
- Orchestration Migration – Moving from monolithic ETL/ELT tools to modern frameworks such as Apache Airflow or Dagster.
- Modern Data Ingestion – Replacing legacy tools with modern services that add value through CDC (Change Data Capture).
- Logic Refactoring – Gradually extracting logic from outdated tools so every step creates new business value.
- Data Governance – Establishing ownership, quality standards, and mapping data activities to measurable business outcomes.
Beyond technology, the company also acts as a facilitator of change - breaking down silos and building a common language between business, application, and data teams.
What Does AI Bring to the Data World?
AI is not just another technology trend. It is fundamentally changing the role of the data world.
Data has always been the bridge between business teams (who generate data), application teams (who capture it), and analysts (who use it). AI only increases the importance of that bridge.
In the short term, demand will likely grow for senior professionals and consultants who understand both business and technology. At the same time, increasing automation of development tasks through AI tools may reduce demand for more traditional junior roles.
That creates a serious challenge. If companies do not develop new ways to mentor young professionals in systems thinking, communication, and business context, they risk losing a generation that knows how to complete isolated tasks - but not how to build meaningful systems.