From operational data to predictive insights. Here is how data analytics works!

What do we mean by data analytics?

Johan Söderström meets Susanne Söderholm, Head of BI, Data & Analytics at Implema, to clarify what data analytics means in practice. Instead of just looking at what has happened, data analytics helps organizations understand why it happened and what might happen next. This requires more than just pulling numbers from a system. It is about combining operational data with financial context, such as product costs and margins, as well as external data, and then cleaning and structuring the information so that everyone uses the same definitions.

Why operational data is not enough for decision-making

Many companies still treat data analytics as “export to Excel and create a chart.” This doesn’t work when management needs answers to strategic questions. For example, which products are actually profitable, how cost changes affect margins, or how external factors impact demand.

Susanne highlights two recurring obstacles.

1. Operational data itself is often backward-looking.

2. Different teams use different definitions for the same concepts, such as what a “customer” is or what counts as a “good customer.” Without common definitions and well-maintained master data, you get stuck discussing numbers instead of acting on insights.

What data analytics looks like in practice

1. Start with operational data – Sales figures, volumes, delivery precision, and other metrics show what happened.
2. Add decision support – To be able to make decisions, you need the reasons behind the numbers. Cost per product, margin, personnel data, and external factors like inflation or currency fluctuations.
3. Go from hindsight to foresight – When you add the right context, you can test scenarios and understand consequences in advance, instead of just reacting after the fact.
4. Create a common language in the organization – Data analytics often includes data cleansing, working with master data, and common definitions. This is how different teams stop comparing different versions of reality.

FAQ

What is meant by data analytics in a company?

It is the process of combining operational data with supplementary information, such as costs, margins, and external factors, to understand what is happening and make better decisions.

Why isn’t Excel enough for data analytics?

Excel can visualize data, but it rarely solves the difficult parts. Common definitions, high data quality, and connecting multiple data sources are what make insights reliable.

What is master data and why is it important?

Master data is shared core information such as customers, products, and suppliers. If it is inconsistent, different teams get different KPIs and reports, and trust in the numbers decreases.

How does data analytics become more forward-looking?

By adding factors like inflation, currency fluctuations, cost drivers, and personnel data, you can model scenarios and understand possible consequences before they occur.

Next step: Workshop on data and analytics

Stop guessing in strategic discussions. Let us help you clarify which decisions you need support for, which data sources are important, and how you create a shared view of the business.