Tech Frontiers

Big Data Is No Magic Bullet

ARCHIV - Ein Messebesucher geht am 16.03.2015 an einem Leuchtdisplay auf der CeBIT in Hannover (Niedersachsen) vorbei. Der Digitalverband Bitkom hat seine Wachstumsprognosen für das Jahr 2015 angehoben. Der Umsatz in der ITK-Branche soll demnach um 1,9 Prozent auf 156 Milliarden Euro wachsen. Foto: Peter Steffen/dpa (zu dpa "ITK-Branche erwartet erstmals mehr als eine Million Beschäftigt" vom 22.10.2015) +++(c) dpa - Bildfunk+++
Big data.
  • Why it matters

    Why it matters

    While having access to Big Data certainly helps, long-term corporate success also depends on the quality of data analysis combined with innovative business models and ideas.

  • Facts


    • Precise data analysis can help firms improve efficiency and budgeting.
    • The question of whether access to data gives firms a sustainable competitive advantage is the focus of a joint report by French and German competition authorities.
    • Tools for compiling and analyzing large amounts of information are widely and cheaply available to companies of all sizes.
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Big Data is the next chapter in technological development. Thanks to the cheaper and greater capacity and performance by computers, many innovative companies are relying on complex data analysis from day one.

But they are also faced with the question of whether access to data also provides them with a sustainable competitive advantage. This question is also the subject of a joint paper by the French and German competition authorities: the Autorité de la concurrence and the German Federal Cartel Office.

We believe that Big Data provides companies with fewer sustainable advantages than one would assume at first glance. That’s because at the heart of the analysis is the old realization that a competitive advantage can exist only when it can’t be replicated on the market.

Quite often, startups enter a market with a clear data disadvantage, but quickly grow and become a challenge to established competitors or even overtake them.

First of all, the tools for compiling and analyzing large amounts of data are already widely and cheaply available to companies of all sizes. 

And second, there are also alternative sources of data besides those a company can gather on its own. Data isn’t a rival good that may only be possessed or consumed by a single user. Nor is it used up during processing. It can always be used by others. 

Take Comcast’s information on TV viewing habits, or Bluekai’s data on user behavior, for example, which are both are available for purchase on the broader market. The availability of such data sets allows newcomers to the market the possibility of gaining the same insights as established players.

Without a doubt, companies are better able to understand their customers through data analysis. But it’s also clear that the data itself has little value of its own. Companies still need competent employees and organizational structures to implement the relevant field experiments, algorithms or analyses to work out useful information.

That means the true value of data depends on how it is analyzed and evaluated. 

Big Data companies are exposed to the competition as well. Quite often, startups enter a market with a clear data disadvantage, but quickly grow and become a challenge to established competitors or even overtake them. They compensate for the data advantage the established competitors have with other resources or capabilities.

WhatsApp, for example, took on the traditional telecommunications corporations that have a lot of data on the use and price preferences of their customers. Instead, WhatsApp offered a simple-to-use and extremely cheap messaging service.

The possession of Big Data is not a key factor for success in the digital markets. Instead, success is driven by innovative business models and ideas that respond to customers’ needs. In this sense, data is no magic bullet with the ability to eliminate competition and put unimaginative companies in the lead.

Having access to data helps, but success is determined by the quality of the analysis.


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