It is believed that there has been a lot of “big data” in the market in recent years. However, big data doesn’t work humanly. However, data on fills the space, all the same, thick data are qualitative data that provide an overview of the daily emotional life of consumers. Finally, we see that it is easy to invest in thick data, but if it is used, it is a completely different proposition, and more and more data is being sucked into the financial collection it uses. The reasons for these errors range from lack of technical knowledge to inefficient management. However, an important factor contributing to the obsession with thick data is to the detriment of qualitative data. In principle, institutions are limited to the human element and context that must correspond to numbers with the purpose to have any value. Here’s why big data needs thick data.
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Why Big Data Needs Thick Data
Big data is created through the millions of organizations have with their customers. This is fairly simple to do by simply looking at historical patterns of behaviour, but quantitative analysis can yield results for the examples if you try to use the data to better understand customer behavior and specific marketing trends, as much more information is needed. You need to understand the feelings of the people behind the numbers. Perhaps this is best illustrated by mistakes in a study in last year’s EU referendum and the U.S. presidential election, where a lack of public opinion, according to most statistics, meant deceiving most people.
However, thick data fill this knowledge gap. It provides a context that allows you to understand why this is so. As a final point, the shareholder and brand relationship is based on emotions. One needs to understand the uncertainties of people’s behavior to predict how an individual’s attitude toward the service or product will change over time. Without that understanding, the pattern you discover that encourages people to behave in a certain way can be found in a world that no longer exists. The trends you identify can be noticed in the console, so your campaigns won’t attract those you think are loyal customers or new prospects.
This is extremely difficult and it is too early to enter machine learning, as is often the case with big data. Human activities are often of little value even to other people, not to mention machines. Fear, greed, selfishness … are simply too difficult for algorithms to understand. Thick data are generated by basic and further research in the form of surveys, focus groups, interviews, and questionnaires. This comes from ethnologists, anthropologists, and others who have extensive experience from big data certification and training in monitoring and analyzing human behavior and underlying motives. It allows them to discover the feelings, stories and patterns of their work and, therefore, which products they most often buy, at what price they will pay and so on.
Visions and Emotions
However, thick data is the best way to map an unknown site. If organizations want to know what they don’t already know, they need it because they offer something big data doesn’t particularly have – inspiration. Collecting and analyzing stories creates ideas. However, stories can encourage organizations to find different ways to get to their destination: information. If you drive, we advise you to avoid frequent data transfers. Thick data often shows unexpected outcomes. It comes as a surprise. Anyway, it is inspiring. Innovation must take place in a society of imagination. Stories contain emotions, something that usually purified data can never provide. It is difficult for an algorithm to describe the strength of a connection to a service or product and how the meaning of membership changes over time. Thick data methodologies apply deep in people’s hearts.
Finally, stakeholder relationships with companies or brands are emotional, not logical. Some are discomfited to use the term “stories” to describe ethnographic works. There is a lot of confusion that stories match anecdotes. Marketing experts ask if it’s “style”. Even among researchers, many sociologists avoid using “stories” because their qualitative work makes them less scientific. People stated that they were often told to use the word “language” instead of a story. However, there is a big difference between them. Random stories are random disputes. In the context of research, stories are consciously collected and systematically sampled, shared, re-examined, and analyzed, creating information. Excellent perspectives foster design, strategy, and innovation.
We still have a lot of answers about organizations’ thick data:
- How to present thick data? Stories are successful, but they require time, resources, and communication skills.
- What is the pointer on successful thick data testing?
- How to train teams in integrated methods with thick data and big data? Ethnologists are sought after more as suppliers and service providers than as employees in organizations. The company does not employ enough ethnologists to conduct ethnographic research and research different ways to increase knowledge of big and big data.
It’s time to leave it and move on. We are in a good position to show the added value we offer in a mixed process. Composing “close descriptions” of the social context completes the Big Data collection – innovations of people and organizations in thick data and big data. If we rely only on big data, we encounter a distorted sense of the world, where people are just numbers entering algorithms. This does not mean that it is useless or that in many cases it can be used on its own.
It is still a powerful and useful tool that companies should invest in. But companies should also invest in collecting and analyzing thick data to discover the deeper and more humane meaning behind big data. They have to work side by side, ethnologists and data scientists, and data engineers work side by side to ensure that big data is based on people’s actual behavior, not just on what the machine thinks it should be… no, never! That’s not even happening!