Fragmented data is a leadership challenge – Here’s how to turn data into a business driver
In many organisations, data is plentiful, but the numbers aren’t trusted. This is due to concepts being defined differently across various systems. Metsähallitus is addressing this challenge with DSharp’s tools and sees strong potential in the AI-powered DSharp Scout, which guides users to reliable metrics.
The operational environment of Metsähallitus is exceptionally broad and complex. The organisation is responsible for forestry, nature conservation, and the development of real estate and tourism across Finland’s extensive land holdings. Each of these areas produces and utilises data from a unique perspective. Data users range from field personnel to experts and top management.
“DSharp’s conceptual model-based data platform has made Metsähallitus’s data unified, reliable, and genuinely supportive of decision-making. Without the platform, data would easily be left to linger in silos,” says Annina Malin, Data Architect at Metsähallitus.
Shared concepts and metrics support operational and strategic work
The value of consistent and up-to-date data is also evident in day-to-day operations. For example, planning forest management activities requires data on forest conditions, nature conservation restrictions, other activities taking place in the area, and factors related to real- estate.
“When all the necessary information is available via a single viewpoint, planning is significantly faster and more accurate. Rather than meandering between different systems, experts can focus on their essential work,” Malin explains.
Fragmented data is not just a technical issue for organisations – above all, it is a challenge for leadership. When decisions are made on incomplete or inconsistent data, the risk of errors increases. Essentially, data tools are a strategic investment.
On a strategic level, unified concepts and defined metrics ensure that Metsähallitus’ decisions are based on shared interpretations rather than assumptions. Because decisions affect large areas and long-term objectives, it is critical that the data is reliable and up to date. When operations are guided by a shared conceptual model, no one must rely on assumptions. Instead, the information underlying decision-making is clearly understood, and operations are genuinely data-driven.
A shared conceptual model translates data into the language of business
Metsähallitus has built a shared conceptual model that already includes more than 1,400 defined concepts. These concepts form the foundation of the entire data platform. For Metsähallitus, DSharp is far beyond a mere IT tool – it is a way to ensure that the entire organisation speaks the same language when it comes to data use. In practice, this means that metrics are initially defined through shared concepts and can then be reused in reporting and analytics.
“When concepts and their relations with one another are defined jointly, data is unambiguous and comparable. This accelerates the rate of development and improves the quality of solutions,” Malin outlines.
From a business perspective, this has a direct impact on decision-making. Reports are trustworthy, development work progresses faster, and the margin of error is diminished. Meanwhile, collaboration across units improves as everyone adopts the same terminology, eliminating room for misinterpretation.
“For example, our HR team can independently define the metrics they need based on shared concepts and interpret the results in the department’s own lingo. When data is transparent and comprehensible, experts can focus on analysis instead of spending time searching for or explaining data,” Malin clarifies.
An AI assistant makes data discovery and interpretation easier
In the future, all eyes will be increasingly fixed on artificial intelligence. The experts at Metsähallitus recognise that using AI for data discovery and interpretation raises data quality requirements to a new level. Transparency must extend from the origin of the data to conclusions drawn from it.
“If we don’t understand where the data comes from and how it’s been utilised, AI-produced results cannot be trusted. That’s why we only use solutions with a verifiable data chain behind the forecasts,” Malin explains.
DSharp’s next development step is the AI-powered DSharp Scout, which aims to make data utilisation truly self-service. The idea is that users can retrieve information with a single query in their own natural vernacular. An AI assistant can answer questions and run queries on the data platform thanks to its access to shared definitions and the metadata that describes them.
“Even though data has already been consolidated into a single data platform, its full potential may not yet be realised. Most users are familiar with only the systems closest to their area of operation. An AI bot could be used to reveal connections and opportunities users would not otherwise know to look for,” Malin notes.
The AI assistant assists with reporting and analytics. Leadership gains situational awareness more quickly via smoother data utilisation in a cybersecure way.
“My hope is that in a few years’ time, data usage will have become so easy that it’s a natural part of everyone’s workflow. Alongside, decisions will be made faster and in a more transparent way, with better reasoning,” Malin says to summarise the concrete benefits of the AI bot.
Read more about DSharp Scout and PathFinder.
