Big data is inseparable from "thick data"

Big data is inseparable from "thick data"

At present, companies of all sizes around the world are told that they need big data? ——? Big data is the source of the next round of innovation. Venture capital companies set up investment portfolios specifically for big data, start-ups claim to be "big data" companies, and mature giants will set up digital innovation teams specifically for big data projects. Faced with advanced computing data collection and analysis capabilities, many start-ups and large enterprises focus too much on collecting quantitative data at the expense of human insight. This practice of putting quantitative figures above qualitative opinions is really worrying. I have witnessed a huge impact on a company, and no company is willing to follow this practice.

In 2009, I did research work in Nokia. At that time, Nokia was the largest mobile phone company in emerging markets. In my research, I found that the overall business model of this company is facing challenges. After years of anthropological research in China, I have seen many market signals, whether I live with migrant workers, experience the bitterness of street vendors or immerse myself in the world of Internet cafes. I have reason to believe that low-income consumers are ready to pay for more expensive smartphones. ?

At that time, I concluded that Nokia must change its current product development strategy, from making expensive smartphones for elite users to developing moderately priced smartphones for low-income users. I reported my research report and related suggestions to Nokia headquarters. But Nokia didn't know what to do after reading my research findings. They said that my sample size is only 100, which is insignificant compared with their millions of samples. In addition, they also said that according to the information they have, my insights are completely unfounded.

Of course, now, we all know what happened to Nokia. Microsoft acquired Nokia's mobile phone business in 20 13, and now its global smartphone market share is only 3%. The decline of Nokia is caused by many reasons, but one of the most serious reasons I have experienced personally is that Nokia relies too much on numbers. They pay too much attention to quantitative data, so that they become at a loss when faced with data that are difficult to measure or not available in existing reports. It could have become Nokia's competitive chip, but in the end it helped and led to its decline.

Since I have worked in Nokia, I have been puzzled by the fact that enterprises pay too much attention to quantitative data and ignore qualitative data. With the rise of the era of big data, I found that this situation began to intensify. Some companies will not hesitate to deduct the budget spent on people-oriented research, and would rather spend a lot of money on big data technology. I am deeply worried about the living conditions of qualitative anthropological research in the era of big data.

In the current data-driven world, anthropological research (usually in the form of market research, design research and qualitative research) is facing a very serious misunderstanding. It is often said that the sample size of anthropological research data is too small, and anthropological research data is "small data", just as Nokia executives said at that time.

Due to the lack of conceptual words to quickly define the value of anthropological research in the era of big data, I have been using "thick data" since last year (here to pay tribute to Clifford Geertz! ) to express my advocacy and support for the comprehensive research method. Thick data refers to the data interpreted by anthropological qualitative research methods, aiming at revealing emotions, stories and meanings. Thick data is difficult to quantify, but profound meanings and stories can be interpreted from a small number of samples. Thick data and big data are completely different. Quantitative data need to rely on a large number of samples, and at the same time, data are collected, stored and analyzed with the help of new technologies. To be analyzable, big data must go through a normalized and standardized definition and classification process, which will virtually eliminate the background, meaning and story contained in the data. And thick data can just prevent big data from losing these background elements in the process of being interpreted.

"Thick data refers to data interpreted by anthropological qualitative research methods, aiming at revealing emotions, stories and meanings."

Integrating big data and thick data can enable enterprises to grasp any situation more comprehensively and thoroughly from a global perspective. To see the overall situation, enterprises must use big data and thick data at the same time, gain different types of insights, and gain rich breadth and depth. Big data needs a large number of samples to reveal specific patterns, while thick data can interpret various people-oriented patterns with a small number of samples. Thick data depends on human learning activities, and big data depends on machine learning activities. Thick data reflects the social background behind various data relationships, while big data reflects insights extracted from a series of specific quantitative data. Thick data technology can contain irreducible complexity, while big data technology clarifies patterns by separating variables. Thick data lacks breadth and big data lacks depth.

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There are risks in using big data.

When enterprises use big data, if they don't have a set of integration framework or weight scale, then big data will become a risk factor. Steven Maxwell pointed out: "People are too obsessed with the quantity of data and information, but ignore the' quality' part, that is, the business insight that analysis can reveal." The larger the volume, the more insight it produces.

Another problem is that big data often pays too much attention to quantitative results and devalues the importance of qualitative results. This will lead to a dangerous view that the standardized data obtained by statistical analysis is more useful and objective than qualitative data, thus further affirming the view that qualitative data is small data.

The above two problems have led enterprises to make management decisions only by quantitative data for decades. For a long time, enterprise management consultants have been using quantitative data to improve the operational efficiency and profitability of enterprises.

The risk of using big data is that enterprises and individuals will start to rely on algorithms as a measure to make decisions and optimize performance.

Without a balancing force, big data is likely to cause enterprises and individuals to always make decisions and optimize according to the standards obtained by algorithms. In this optimization process, everything including people, stories and real experiences will be ignored. As Clive Thompson wrote: "If the decision-making factors of people are removed from this equation, it means that we will gradually drift away from the thoughtful practices, and these thoughtful moments are precisely the opportunities for us to reflect on our actions from the moral level."

Release the fusion effect of big data and thick data

The amount of information generated by big data is so huge that we have to resort to other means to fill and/or reveal the knowledge gap. This is precisely the value of anthropological research in the era of big data. Below, I will share some methods about how enterprises integrate the use of thick data.

Thick data is the best way to outline the unknown world. When enterprises want to know what they don't know, they need the help of thick data, because it can bring something that big data doesn't have-inspiration. Collecting and analyzing stories helps to generate insight.

When enterprises want to know unfamiliar fields, they need the help of "thick data" because it can bring something that big data can't bring-inspiration. Collecting and analyzing stories helps to generate insight.

Stories can inspire enterprises to explore different ways to reach their destination, and this ultimate destination is insight. For example, if you are driving, thick data can make you move to the place you want to go instantly. Thick data often bring some unexpected discoveries, which are both confusing and surprising. But anyway, it can bring inspiration. Only in imaginative enterprises can innovation survive.

When enterprises want to establish a more stable relationship with stakeholders, they need to use "stories". The "story" contains emotions, which cannot be provided by standardized data after analysis and filtering. Numbers can't reflect all kinds of emotions in daily life: trust, vulnerability, fear, greed, desire, security, love and intimacy. It is difficult to express a person's goodwill towards a service/product by arithmetic, and how this goodwill will change with time. Relatively speaking, the analysis method of "thick data" is more deeply rooted in people's hearts. After all, the relationship between stakeholders and enterprises/brands is emotional, not rational.

Future integration opportunities of thick data and big data

Roger Magoulas, who put forward the concept of big data, emphasized the necessity of stories: "Stories can spread quickly and spread the lessons of data analysis to all corners of the enterprise organization."

Just using big data can cause problems. The key is to know how to use big data and thick data at the same time, so that the two can complement each other. For qualitative researchers, this is an excellent opportunity to define the nature of their work in the era of big data dominated by quantitative results. Some companies like Claro Partners have even begun to redefine the way we ask big data questions. In their personal data economy research, they did not ask the question of the enlightenment of big data on human behavior, but instead asked the enlightenment of human behavior on the role of big data in daily life. They also developed a set of tools for customers to help them change their thinking perspective, "from data-oriented to people-oriented."

Regarding how big data and thick data can play a synergistic role in enterprise organizations, I have sorted out the following opportunities (of course, not limited to these):

Health care?

As individuals can track their health status more and more conveniently, self-quantification value is becoming a mainstream. Medical service providers will have more and more opportunities to collect all kinds of anonymous data. Projects like Asthma Files can give you a quick understanding of big data and how big data will solve global health problems.

Relocate anonymous data of mobile operators?

Mobile companies around the world have begun to repackage and sell their customer data. Marketers are not the only buyers. Urban planners are using Air Sage's cellular network data to understand the local traffic conditions. In order to protect users' privacy, these data will be anonymous or personal communication records will be deleted. Of course, without key personal information, the data will lose key background information. In this case, if there is no thick data, it is difficult for enterprises to decipher the personal situation and social background lost due to the erasure of personal information, and it is impossible to truly interpret the data.

Social network analysis

Social media can generate a large amount of data, which can enrich social network analysis. At present, research scientists, including Hilary Mei Sen, Gilad Lotan, Duncan Watts and Ethan Zuckerman (and his lab in MIT Media Lab), are studying how information spreads on social networks, and what problems will occur at the same time. These problems can only be solved with the help of "thick data". Nowadays, more and more companies use social media as a measure. Enterprises must be cautious about this, and don't mistakenly think that "influencing factors" can only be seen through data. The misreading of Cesar Hildargo's work by the media is an example of misreading the analysis results of big data networks, which means that Wikipedia can become a cultural agent. (Click here to see Heather Ford's correction. )

Brand strategy and generational insight

For a long time, enterprises are used to relying on market analysis to formulate enterprise strategies and generate insight. Nowadays, enterprises are turning to a more people-oriented way, that is, based on "thick data". In Jcrew's latest report, Fast Company clearly pointed out that after the failure of the big data-driven management consulting method, it was those employees who really understood what consumers wanted to lead the brand out of the predicament. Among them, an employee named Jenna Lyons has the opportunity to try, modify and test products with consumers in real time. Her method caused a response among consumers, and finally successfully transformed Jcrew into a brand that people admire, and its revenue tripled.

Product/service design

Algorithms alone cannot solve the problem, but there are still many companies that rely on algorithms to guide the development of products and services. Xerox uses big data to solve problems for the government, but it also uses anthropological research as a supplement to data analysis. Ellen Issacs, an anthropologist at Xerox PARC, said: "Even if you have a clear concept of a technology, you still need to design it to ensure that the concept conforms to people's views on their behavior ... You must observe how they do it."

Implement enterprise organization strategy

Thick data can be used as a supplement to big data, complement big data, and reduce the subversive impact of planned enterprise transformation. Quantitative data may indicate that some changes must be made, but the cost of subversion within the enterprise organization is huge. Re-layout the enterprise organization chart, rewrite the job description, change the job functions, and re-establish the success criteria-all these subversive changes have costs, and the consequences may not be reflected in the big data plan. Enterprises need thick data experts to work with business leaders to understand the impact and background of changes, decide which changes are feasible from a cultural perspective, and how to design the whole process. Grant mccracken called thick data experts chief cultural officers. They are like "the eyes and ears of enterprises, and they will be keenly aware of the upcoming changes, even if these potential changes only send a very weak signal." The chief cultural officer is a thick data expert, who is responsible for collecting, telling and spreading stories and maintaining the aura and flexibility of the enterprise organization. Roger Magoulas, who put forward the concept of big data, emphasized the necessity of stories: "Stories can spread quickly and spread the learning brought by analysis and summary to the whole enterprise organization."

Comprehensive utilization of empathy and data resources for innovation.

In addition to all these opportunities that can be tapped, it is also important that there is still a lot of room for improvement in big data. Gartner's research shows that only 8% of companies investing in big data capabilities are using big data to do something far-reaching. The rest of the companies just use big data to drive incremental growth. This means that although many companies are talking about and investing in big data, they are not really using big data to promote real change.

In my opinion, if enterprises and institutions want to give full play to the potential of big data, they must use thick data in combination, which is why we need researchers who are more inspired by people than ever before, whether they are anthropologists, market researchers, design researchers, designers, product managers, documentary directors, producers, writers or social media managers, because such researchers always collect and analyze data with empathy. The most innovative companies are often those that know how to combine big data and empathy. This is one of the reasons why companies such as Alibaba, Baidu and Tencent are so successful. They can always grasp the situation of actual users as quickly as lightning, thus driving their technological innovation. The future innovation of China depends on both the situation and the data.

The above is what Bian Xiao shared with you about big data being inseparable from "thick data". For more information, you can pay attention to Global Ivy and share more dry goods.