From data noise to decision power. Most companies agree that data is critical to their business. Yet it's rare that data is consistently used to drive decisions. Usually you hear phrases like: "We have a lot of data, but the numbers don't always add up" or "We still use Excel for most things, even though we have a platform".
It's not uncommon. Many analytics projects never get past the dashboard stage because the foundation isn't strong enough. There's a lack of context, structure and trust in the data. And without trust, the numbers lose meaning.
In principle, an analytics platform can give your business a huge boost. It can gather data across systems, create an overview and open up new opportunities with AI and automation. But the platform doesn't do it alone.
The real value only arises when the technology is combined with a solid data foundation and a clear understanding of what the company will use its data for. This requires governance, ownership and a realistic approach to maturity.
You can think of working with data as a journey. Firstly, it's about collecting and storing data in a structured way. Secondly, it's about understanding and quality-assuring it through a data model that makes sense to the business.
Only then does it become realistic to analyze and act on data - the point where the analytics platform really makes a difference. Many people jump straight to the last step and end up with great dashboards that can't be used in practice because the data quality lags behind.
A key goal for many organizations is to establish a single point of truth, a common and reliable data foundation on which all decisions are based.
When finance, sales and operations work from the same data, the classic "versions of the truth" disappear. It builds trust and makes it possible to use analytics as a basis for real, data-driven decisions, not just as a reporting tool.
In our discussions with companies, we often find that AI only starts to make sense when the fundamental relationships between systems, data sources and ownership are in place. It's rarely about more technology, but about better structure.
Read: Petoro ensures data quality with Source of Truth
You may have already started your analytics journey with big ambitions: to become data-driven, automate reporting or use AI to predict everything from sales to production downtime. The ambitions are good, but without a clear business anchor, the projects risk stalling.
The most successful examples start small: a defined area where data can actually make a difference. It could be reducing manual work, improving forecast accuracy or increasing transparency in operations. Most importantly, users can see the value and trust the results.
Once that trust is in place, the platform can gradually be used for more advanced use cases - such as AI or automated decision support.
At Cegal, we're increasingly faced with questions like "how do we get real value out of what we already have?" rather than "what tool should we use?". It marks a shift from technology choice to data maturity, governance and business-relevant use cases - a decisive step towards moving from data noise to decision-making power.