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  • Big Data vs Product Innovation

    30 January 2017
    S.Point, Member Firm (China)

    People have talked so much about big data, a concept so bragged about in business plans and daily work. However, in recent years, we have indeed seen more practical players who have been gradually improving data infrastructure across the industries while exploring serious data mining technologies. Our new partner CBNData is such a down-to-earth organization. Talking with them, I appreciate their understanding of the limitation of data. We are a provider of product innovation solutions. Customer insights represent part of our core competitiveness. With the birth of the big data terminology, people have been talking about a revolution that could overthrow qualitative and quantitative analysis mythologies. Instead, we see both opportunities and challenges. Big data will play a role in the innovation journey from 0 to 1. Such journey requires data acquisition and analysis players like CBNData as well as those with business acumen, good with observation and interpretation of human behaviors, and quick in iterative progress, just like ourselves.

    Many tend to see the conflict between data and the anecdotes captured through interview. It seems to them data is objective but people are subjective. However, in acquiring and sorting out data, many steps require assumptions by the analyst based on industry experience. Take the online child nutrition product consumption survey we’ve conducted for instance. We make age 6 the cutoff age. In fact, there is no age labeling in CBNData’s existing e-commerce data. The reason we make age 6 the cutoff age is that our qualitative survey has found a significant lifestyle difference between pre-school and primary school children. Such qualitative assumption provides a framework in which we sort out and understand massive data involving behaviors. Without such framework, there is no way we can start the analysis. The framework may evolve as data analysis progresses. In qualitative analysis, we tend to interpret human intention and internal desire from stories told to us. The stories contain human feelings inspired in context, which provide the inspiration of product and experience innovation. Therefore, stories have the kind of power that data does not provide.

    CBNData boasts data from Ali, a key envy for most others. However, even CBNData does not have perfect data. We’ve seen e-commerce consumer preference data, but we cannot say whether such preference data also applies to offline consumption or WeChat platform. Sampling poses a problem to qualitative and traditional quantitative analysis. Big data pundits tend to brag about complete samples. However, when it comes to big data, sampling limitation and sampling choice are the key. When we approach innovation analysis, we pay special attention to leading users, who represent not the average people, but are opinion leaders who set the pace for the future and inspire innovation. We’ve seen many innovative products that have become popular starting from niche market products, an experience shared by Apple, Bluebottle Coffee, and MUJI. Therefore, being big is not everything about data, but targeting is the key.


    We aim for more than a research report when working together with CBNData. Our enthusiasm for big data comes to the desire for mastering the future. Though data helps with predicting trends, may uncertainties exist that may lead to results different from prediction. We cannot say that products created based on our research paper are bound to succeed in the market. We envisage later stages following product research, stages of market testing, feedback collection, and more iteration. Big data continues to play a big role when it comes to collecting feedbacks. Our experience for rapid design, error and trial, and itineration also continue to be important. We are just getting started with this exciting exploration journey, but the first step of embedding big data in product innovation flows is already exhilarating.

    Original Article 

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