may 12, 2025
Big Data is no longer an experiment: businesses are expecting quick results
How popular were Big Data projects last year?
Demand for big data solutions continues to grow at an average rate of 30% per year. In 2024, the number of Big Data and BI projects remained consistently high. The majority of requests came from companies in the banking sector, various industrial sectors, as well as retail and FMCG.
What is driving their popularity?
There are several reasons. The global development and growth of AI technologies have increased demand for AI in companies to solve a wide range of business tasks. However, implementing AI-based solutions is impossible without high-quality data: companies require perfectly cleaned, structured, and consistent data from all sources in order to achieve measurable business value.
At the same time, demand is growing for integrators specialising in big data solutions. Many companies have faced the need to re-evaluate their data infrastructure. However, finding ready-made off-the-shelf solutions for large enterprises has proven to be extremely difficult, and often even unachievable. In such cases, businesses are increasingly turning to integrators who can offer adaptation of existing market solutions to suit specific business needs, as well as a range of other comprehensive services — from migrating data from legacy systems and deploying open-source solutions, to ensuring full compliance with regulatory requirements.
How have clients’ expectations of big data solutions changed over the past two to three years?
In the past, companies would invest in Big Data «for the future» — building data warehouses and using them for analytics without a clear understanding of when or how this would generate profit. Today, requests have become more specific and pragmatic: clients now expect fast, measurable results — either increased revenue or reduced costs. Ideally, of course, both.
For example, in industry there is growing focus on predictive maintenance systems that help reduce the risk of equipment downtime and associated losses. Energy supply companies are using data to analyse the causes of overdue payments, enabling more accurate forecasting of inflows and helping to minimise cash flow gaps. The banking sector is increasingly adopting AI-based scoring and anti-fraud systems to reduce credit losses.
Expectations around implementation speed have also changed. In the past, projects could take years. Now, clients expect to see initial results within the first few months. The reason is the high cost of capital — businesses need a quick return on investment.
Overall, clients now expect integrators not only to have relevant case studies from their industry, but also to offer flexible solutions and pay close attention to existing system architecture. Companies no longer want to overhaul their entire infrastructure — today, the focus is on integration with current processes, with revolutionary changes coming second.
A third of companies plan to increase their budgets for Big Data. Why should businesses invest in a new data architecture?
Many companies today face a critical need to modernise their data platforms. Legacy systems create three key problems: technological stagnation, talent-related risks, and inefficient resource allocation. When software is no longer updated and specialists with knowledge of outdated technologies become increasingly rare, businesses become dependent on an unsustainable IT ecosystem.
Companies that delay modernisation lose out twice: first, by spending up to 80% of their IT budget on fixing existing issues rather than developing new solutions, and second, by missing opportunities to monetise their data.
Therefore, investing in a modern architecture is not a choice — it’s a necessity. Modern cloud-based and hybrid solutions significantly reduce operational costs. In addition, outdated systems often contain unresolved vulnerabilities, making system upgrades primarily a matter of security.
What tools and methodologies are recommended for ensuring data quality?
The main mistake many companies make is treating data management purely as a technical task. Building a data platform is, first and foremost, organisational and methodological work. If the IT department implements such projects in isolation from the business, the data may lose — or never gain — practical value.
Therefore, the key principle is a solid methodological foundation: jointly developed standards for data management with vendors and integrators, including Data Quality, clearly defined quality metrics, established data handling procedures, and clearly assigned areas of responsibility. For example, we use a flexible methodology that involves phased project implementation with regular result reviews and adjustments to subsequent actions. This allows us to quickly respond to any changes and involve the business from the very early stages, which reduces risks and increases implementation efficiency.
Another crucial aspect in ensuring data quality is fostering a data-driven culture at all levels of the company: employee training, introduction of KPIs linked to data quality, and the creation of an end-to-end accountability system.
Can the real impact of data usage be measured?
When we talk about a data-driven culture, the key success indicator is the ability to turn data into concrete business outcomes. Experience shows that even basic data structuring delivers measurable benefits. For instance, building data platforms reduces access time to information by 60–80%, which directly improves decision-making speed and product time-to-market.
What is the key advice for a CIO who is just starting a data transformation in their company?
Data transformation should not begin with technology, but with a comprehensive assessment of the current state of the IT infrastructure. The first step is to conduct a data governance maturity audit to establish a baseline for transformation. This can be carried out either by the internal team or with the support of external experts from a vendor or integrator.
Next, it is crucial to set up the right organisational structure. I recommend establishing a corporate Data Office led by a Chief Data Officer (CDO). However, the key point is that the CDO should not merely be a technical specialist, but a true business partner — someone capable of balancing IT capabilities with the real needs of business units.
Only after this should the company move on to developing a digital strategy and a roadmap. The strategy must be as specific as possible — not simply to «implement Big Data», but to clearly define which business problems will be solved using data. The roadmap, in turn, should outline a phased plan for transitioning from the current state to the target data architecture, complete with clear KPIs and timelines.
