for large companies to engage with them in the first place. Multinational corporations often struggle even to
identify promising potential startup partners; startups, for their part, find it difficult to identify and reach
the relevant decision makers within the often-confusing hierarchies of gigantic multinational companies.
The challenge, for both sides, is all the more vexing in emerging markets. Furthermore, most academic studies of the challenges that large companies and entrepreneurial ventures face in partnering —
and the solutions the studies suggest — focus on mature markets, such as the United States and Europe.
Far less is known about how multinational corporations should engage with startups in emerging markets such as China and India. To understand how multinational companies have partnered successfully
with startups in emerging markets, the authors undertook a study in three major emerging market
economies: India, China, and South Africa. Their research uncovered four key factors, and they suggest
a strategy for addressing each factor.
The first key factor, the authors say, is the immaturity of the entrepreneurial ecosystem. Specifically,
most emerging markets are afflicted by constraints and “voids” in their institutions. The authors recommend that multinational companies address this factor through programs that compensate for the
immaturity of the entrepreneurial ecosystem, and they provide examples such as an IBM Corp. program
that provides training, mentoring, and events for startups in China.
The second key factor is the increasing appetite for entrepreneurship in some emerging markets.
Multinational companies can respond to that by committing resources to initiatives such as training to
accelerate the growth of startups — something Microsoft Corp. has done in India. The third factor is
that Western multinational companies are constrained by their status as outsiders in emerging markets —
a factor the authors suggest global companies address by working with local groups. Fourth, the authors
note, startups in emerging markets offer global companies access to innovative technologies.
REPRINT 58226. For ordering information, see page 4.
Why Big Data Isn’t Enough
Sen Chai (ESSEC Business School) and Willy Shih (Harvard Business School) pp. 57-61
As “big data” becomes increasingly integrated into many aspects of our lives, we are hearing more calls
for revolutionary changes in how researchers work. To save time in understanding the behavior of complex
systems or in predicting outcomes, some analysts say it should now be possible to let the data “tell the story”
rather than having to develop a hypothesis and go through painstaking steps to prove it. The success of
companies such as Google Inc. and Facebook Inc., which have transformed the advertising and social media
worlds by applying data mining and mathematics, has led many to believe that traditional methodologies
based on models and theories may no longer be necessary. Among young professionals (and many MBA
students), there is almost a blind faith that sophisticated algorithms can be used to explore huge databases and find interesting relationships independent of any theories or prior beliefs. The assumption is:
The bigger the data, the more powerful the findings.
As appealing as this viewpoint may be, authors Sen Chai and Willy Shih think it’s misguided — and
potentially risky for businesses that involve scientific research or technological innovation. For example,
the data might appear to support a new drug design or a new scientific approach when there isn’t actually
a causal relationship. Although the authors acknowledge that data mining has enabled tremendous advances
in business intelligence and in the understanding of consumer behavior — think of how Amazon.com Inc.
figures out what you might want to buy, or how content recommendation engines such as those used by
Netflix Inc. work — applying this approach to technical disciplines, they argue, is different.
The authors studied several fields where massive amounts of data are available and collected: drug
discovery and pharmaceutical research; genomics and species improvement; weather forecasting; the
design of complex products like gas turbines; and speech recognition. In each setting, they asked a series
of questions, including the following: How do data-driven research approaches fit with traditional
research methods? In what ways could data-driven research extend the current understanding of scientific
and engineering problems? And what cautions did managers need to exercise about the limitations and
the proper use of statistical inference?
Based on what they found, they developed some guidelines for using big data effectively: how to extract
meaning from open-ended searches, how to determine appropriate sample sizes, and how to avoid systematic