We solve: Data-driven management - can data see the future?

20.12.2023Hans NieminenWe solve

Until today, data management or business intelligence has been based on the analysis of data through interactive visualization and computational analysis. The collected data has always one thing in common: it always represents the past. In this case, the decisions taken are also based on history. What if we could see into the future? At least it would be much easier to make the right decision.

If the data is reliable and there is enough of it, data analytics and machine learning can be used to make predictions about the future. For example, the target could be a single metric related to, say, demand, supply, prices, trends, etc.

RatkaistaanArtikkeli Hans Nieminen Artikkelikuva

The use of data is a growing trend in different business sectors

Understanding what has already happened based on historical data is still important in data driven management, nowadays it is even more essential to look to the future and use data proactively. As a result, organizations should be able to identify trends, anticipate changes and make forward-looking decisions based on data.

Data can be used to predict things such as:

  • Market development: data can be used to predict the evolution of demand, supply or prices. This can help make better decisions in strategic business planning.
  • Customer behavior: data can be used to predict customer buying behavior, customer satisfaction and customer journeys, for example.
  • Business efficiency: data can be used to predict the efficiency of production, logistics and sales, for example. In manufacturing, data can be used to predict the maintenance needs of machinery and equipment.

Data can be used to predict the future more accurately and reliably than guesswork. Data can also be used to identify new trends and opportunities that can be used to improve business performance.

Methods for using data to predict the future

Statistical methods and machine learning can be used to use data to predict the future. The most commonly used term is predictive data analytics. Statistical methods are simple and easy to understand for the user, but they may not be able to take into account all the factors that influence the prediction of the future. Machine learning is a more efficient method that can take into account complex factors.

Machine learning uses data to learn the values of the parameters in the model. As an illustration, a prediction model can be compared to a machine, where each parameter in the model corresponds to a single knob on the machine. For learning to be possible, the data must be reliable and sufficient in quantity.

Everything is based on data

Data is the foundation of all AI-based projects. Every organization has data, but the biggest challenge today is how to meet the criteria for data quality and quantity. This is where organizations usually need help. Another challenge is data analysis, which requires expertise and some technology.

Investment in the right tools, skills and culture is needed to implement future-oriented data driven management. In concrete terms, this may involve issues such as:

  • Designing and building a high-quality data platform
  • Data collecting and analysis
  • Developing predictive models
  • Strengthening the culture of knowledge management in the organization

Did you know this?

  • The volume of data is constantly growing. In 2023, it is estimated that the world will produce around 467 exabytes of data. A byte is a unit of data storage containing 256 different value options. An exabyte is 1 000 000 000 000 000 000 000 000 bytes.
  • The size of a prediction model can be unimaginably large when measured by the number of parameters. For example, the text prediction model on which ChatGPT is based has so many parameters that placing them one centimeter apart in a row would take you more than 43 times around the globe.
  • At SAMK, data analytics and machine learning are taught in the Finnish-language degree programmes in Bachelor of Business Information Technology and in the international Bachelor of Data Engineering degree programmes.


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