Good data management is the pre-requisite for sound economic development

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Good data management is the pre-requisite for sound economic development

Henrik Nakskov, Director, CIMSHenrik Nakskov, Director,CIMS
A century ago, the economic growth was dependent on factors such as efficient supply chain management, the industrial assembly line, and exchange of physical goods.

Today's modern economic growth is dependent on the ability to handle and manage large volumes of often unstructured information. Leaving your ability to manage your data as a competitive edge.

The economy is seeking predictability in information as the foundation for decisions. Often described as “Data Driven Decisions” or “Data intelligences” or “Artificial Intelligences”.

There is not necessarily a casualty (cause and effect) between data and human reactions. Human reaction is not always rational, rather driven by emotions, thus in some incidence’s emotional irrational.

Regardless of sophisticated technology being involved, blind reliance on data is not always good. Critical thought and the ability to question data remain highly relevant.

Wrong interpretation is not always being created on purpose. Simple lack of understanding the causality can be the reason.

This leads to false causality and the consequences can be fatal. From a data integrity perspective, decisions based upon poor casualty quality data are in best case worth nothing.

The question is how we can ensure data is correctly interpreted and not biased in some form, politically, financially, interconnection with other assumption?

If we bring data into relative perspective, we get the first reliability indication of the information we have.

Especially in situation with unpredictable reaction and pressure driven decision it is important to acknowledge the risk of pure data causality to ensure good sciences. Decisions based upon poor casualty quality data is in best case worth nothing.

Positive Covid19 test has in 2020 been associated with approximately 1.700.000 death worldwide. A very high number, but it necessary to set data into perspectives like this:

• The worlds 2020 mortality is approximately 58.000.000

• The Net population growth this year is app. 80.000.000

• Covid19 mortality rate is approximately 3% of yearly expected MR

• Approximately 0,02 % of the world’s population
• The Net population growth this year is approximately 4700% higher than positive Covid19 test MR

The SARS-COV-2 pandemic has led to an extreme increase in data exchanges, data analysis and interpretation under pressure.

When this is the case the handling of data is associated with increased risk misinterpretation.

That is especially where we must use common sense and solid data management.

Taking control over the uncontrollable world of data is a process which can be described in 8 stages.

Stage 1 Understand the context as Data Manager

Data management is the development, execution, and supervision of plans, policies, programs and practices that control, protect, deliver, and enhance the value of data and information assets.

Stage 2 Ensure Data Quality

All information transition carries the potential for misinterpretation and translation.

Stage 3 Track Data

Start thinking of the data tracking process as a separate process.

Stage 4 Develop the Right Metrics

When developing metrics that can have a significant influence on an organization, you need to consider human behavior and the dynamic nature of data.

Stage 5 Use Your Knowledge Use the knowledge

you have gained in a knowledge management (KM) system.

Stage 6 Information Supply Chain Management (ISCM)

It ensures you get the right information to the right recipient at the right time.

Stage 7 Integration of external data suppliers

Cross control of data is also a causality test

Stage 8 Get it all documented in an Operational Plan

See detailed description for every stage in Section5

REFERENCES for further reading

• The World Bank data:

• European mortality monitoring:

• European Health Information Gateway:

• Johns Hopkins University and Medicine Coronavirus Resources Center:
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CIMS: A Holistic Approach to Clinical Data Integrity