Critical Thinking and Data Quality

BLG02x - image EDThere is an increasing amount of data becoming available that can support decision making. However, more data is not necessarily more information. How can you select what is relevant and how can you be sure that the data is accurate?

We are increasingly aware of fake news and our world leaders seem less interested in the truth. At the same time, the complexity of problems is increasing due to globalization and digital communication. How can we use the increasing amount of data to solve these wicked problems?

Can we even talk about solutions given the diversity of forces involved? Often, there are no ultimate solutions and we will have to accept that certain groups will be impacted negatively for the greater good. More and more organizations seem to believe that machine learning and other algorithms are the solution. But garbage in is garbage out and black swans are lurking around the corner, which could make your machine learning useless. This all poses higher requirements on our personal capability to think, consider and decide. We need more critical thinking.

Critical thinking is a mental process of analyzing information by understanding what it is based upon, such as the underlying assumptions, evidence, inferences and meaning. Critical thinkers understand that there are natural tendencies that lead us to incorrect conclusions. They believe that reasonable, intelligent decisions are needed. Critical thinking is not new; it is basically what the left side of our brain is meant to do and is also the cornerstone of philosophy and science. Critical thinking has a direct relationship with data and data quality; high quality thinking requires high quality data. This starts with access to all data that is relevant to the issue at hand, reflected in data quality dimensions such as completeness, availability, and accessibility.

The next step is a good understanding of the data so that it becomes information, reflected in quality dimensions such as understandability and consistency. Critical thinking also requires an assessment of whether the information is correct or at least accurate enough to use it to support inferencing from the issue to a solution and its consequences. This is reflected in data quality dimensions such as accuracy and precision.

The result is that data quality is also becoming increasingly important. This is an opportunity for data management professionals. They can increase management’s awareness of the importance of data quality. A pitfall for data quality management is that it is suggested as a general improvement program, and insufficiently targeted at the issues that really matter in the organization. Critical thinking may provide a focal point and an entry point to management. Management should be aware that the current level of thinking is insufficient for confronting the issues of the future, and that high-quality data can support their quest for the right strategy. The critical decisions that they are feeling insufficiently confident about should be translated to information needs.

This provides data quality with a proper goal to target, and a fitness for use criterion. Critical thinking should also be used as a counter force for hypes such as big data, data lakes, and machine learning. Management should focus on their goals and needs, and let professionals advise them on which solutions are needed to support this. An important note is that critical thinking is much more than logical reasoning; it also concerns the fairness of the arguments and decisions that it leads to. It requires explicit attention to the impact on people and ethical issues. In the end, organizations are nothing more than a group of people with a common set of goals.

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About Danny Greefhorst

Danny Greefhorst, MSc., is a Principal Consultant and Director of ArchiXL in Amersfoort, The Netherlands, and acts as an architect and consultant for clients in the financial and public sector. He has extensive experience with the definition and implementation of enterprise architectures, application architectures and technical architectures. In addition, he coaches organizations in setting up and executing their architecture function. Danny is responsible for the EA portal Via Nova Architectura and is a member of the governing board of the architecture department of the Dutch Computing Association. Danny is active in the architecture community, regularly publishes on IT and architecture related topics and is co-author of the book Architecture Principles: The Cornerstones of Enterprise Architecture. He can be reached at dgreefhorst@archixl.nl.

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