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Big data describes facts, thick data explains them

By July 7, 2021No Comments

Big data describes facts, Thick data explains them.

  07/07/2021

Big data describes facts, Thick data explains them.

07/07/2021

Big Data

Businesses are constantly making assumptions about human behaviour. Which product is most likely to sell and at what price would people make a purchase? Businesses that do well in making assumptions based on gut feeling and experience tend to thrive in the marketplace. However, it doesn’t mean that this approach is always right. Nowadays, the omnipresence of sensors noting down our social whereabouts and the trails of data businesses have gotten hold of, is evidently seen and experienced. This, in turn, enables a better understanding of human behaviour and thus businesses can move away from making assumptions and start making data based decisions. This has gained traction under the term ‘Big Data’, which has become one of the most important developments in business. It has come out to be remarkably useful when it comes to finding answers to important questions and addressing phenomena that are well understood.

Kirsten Saliba,
Research Analyst & Team Lead

ABOUT ONEST  


Kirsten Saliba,
Research Analyst & Team Lead

ABOUT ONEST  

Big Data

Businesses are constantly making assumptions about human behaviour. Which product is most likely to sell and at what price would people make a purchase? Businesses that do well in making assumptions based on gut feeling and experience tend to thrive in the marketplace. However, it doesn’t mean that this approach is always right. Nowadays, the omnipresence of sensors noting down our social whereabouts and the trails of data businesses have gotten hold of, is evidently seen and experienced. This, in turn, enables a better understanding of human behaviour and thus businesses can move away from making assumptions and start making data based decisions. This has gained traction under the term ‘Big Data’, which has become one of the most important developments in business. It has come out to be remarkably useful when it comes to finding answers to important questions and addressing phenomena that are well understood.

Thick Data

The revolutionary essence of Big Data is undeniably positive. However, we need to acknowledge the knowledge gaps that Big Data sometimes has.  This heavy use of Big data has consequently led to the devaluation of qualitative data. Thick data is another word for qualitative research which is backed up by design thinking which is the iterative process that people and groups use to understand users by creating innovative solutions to problems. Qualitative research is also explored through observations, intuitions, sensible thinking and many more. Thick data comes from the collection and analysis of ethnographic and observational data. This was traditionally explored by Clifford Geertz (1977), who spoke about how thick descriptions of human behaviour incorporate data collection. Grasping onto thick data means being open to the unknown which means data that sometimes, we don’t even know we need to collect. This sounds rather challenging. How can one know what might happen or what’s the next step without knowing how your audience feels? This creates a need to deeply understand people’s behaviour, not just in quantitative terms. Businesses need to understand that people have fluid identities. For example, Big data might show that people are making use of personalised discounts but it doesn’t mean that people want personalised discounts. It is important to keep in mind that what is measurable isn’t the same as what is valuable and this is why we need qualitative research.

A case study which shows the success of incorporating a mixed method approach is of Lego. In the early 2000’s, it was on the brink of financial collapse. It tried several times to reposition its brand but failed. The brand eventually engaged in a major qualitative study that looked at the emotional needs of children and why they weren’t being met by Lego. It spent time observing and analyzing hours of video recordings. It was then able to successfully reposition their products and recover their status as an important toy brand.

A case study which shows the success of incorporating a mixed method approach is of Lego. In the early 2000’s, it was on the brink of financial collapse. It tried several times to reposition its brand but failed. The brand eventually engaged in a major qualitative study that looked at the emotional needs of children and why they weren’t being met by Lego. It spent time observing and analyzing hours of video recordings. It was then able to successfully reposition their products and recover their status as an important toy brand.

The combination of both

The answer for a business to be truly customer-centric is the combination of Big data and Thick data. Ethnographic work, which is the study of humans in relation to their society and culture, oftentimes conducted through fieldwork;  will generate customer understanding needs that go hand in hand with several data sets which in turn will help speed up the said understanding. Big data has to be seen as a means to an end not an end itself. Big data helps in knowing insights within a range of data points, while thick data illustrates the social contexts of these data points, and thus helps in creating a better picture on which businesses can make assumptions. Quantitative data would not hold much substance without understanding deeply the context from which it originates meaning from which countries, communities, and societies. The solution to this is mixed data as it can offer both scale and depth. Mixed data  is a methodology that offers to close the qualitative-quantitative divide by integrating both approaches. Both methods are not just brought together, but they are used to create combined results. This  is not an easy task. The two seem to act in opposition however, it is only with the combination of both that one can decipher the real meaning of any social phenomena. Ethnographers get their data by going around and witnessing things first hand. Big data analysts do it differently by figuring out ways to capture actions in the moment, for example: Amazon Fresh and Wholefoods makes use of big data analytics in order to understand better how customers buy their groceries which thus helps Amazon to innovate when needed. Ethnographers connect apparent behaviours to underlying meanings through conversations or more formal interviews. Data scientists, on the other hand, tend to focus only on behavioural data traces, which are the marks we leave behind when we are online. Having a team of quantitative and qualitative experts working closely together will help leverage your data in a way that it becomes easier to understand your customers which helps in innovating your product, service, employee management, operations and much more. Thick data poses a lot of questions as it’s slowly gaining popularity however, it is important to keep in mind that being data-driven in a holistic manner, becomes crucial to survive in today’s marketplace. 

The combination of both

The answer for a business to be truly customer-centric is the combination of Big data and Thick data. Ethnographic work, which is the study of humans in relation to their society and culture, oftentimes conducted through fieldwork;  will generate customer understanding needs that go hand in hand with several data sets which in turn will help speed up the said understanding. Big data has to be seen as a means to an end not an end itself. Big data helps in knowing insights within a range of data points, while thick data illustrates the social contexts of these data points, and thus helps in creating a better picture on which businesses can make assumptions. Quantitative data would not hold much substance without understanding deeply the context from which it originates meaning from which countries, communities, and societies. The solution to this is mixed data as it can offer both scale and depth. Mixed data  is a methodology that offers to close the qualitative-quantitative divide by integrating both approaches. Both methods are not just brought together, but they are used to create combined results. This  is not an easy task. The two seem to act in opposition however, it is only with the combination of both that one can decipher the real meaning of any social phenomena. Ethnographers get their data by going around and witnessing things first hand. Big data analysts do it differently by figuring out ways to capture actions in the moment, for example: Amazon Fresh and Wholefoods makes use of big data analytics in order to understand better how customers buy their groceries which thus helps Amazon to innovate when needed. Ethnographers connect apparent behaviours to underlying meanings through conversations or more formal interviews. Data scientists, on the other hand, tend to focus only on behavioural data traces, which are the marks we leave behind when we are online. Having a team of quantitative and qualitative experts working closely together will help leverage your data in a way that it becomes easier to understand your customers which helps in innovating your product, service, employee management, operations and much more. Thick data poses a lot of questions as it’s slowly gaining popularity however, it is important to keep in mind that being data-driven in a holistic manner, becomes crucial to survive in today’s marketplace. 

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