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    Disruption threat

    Disruption threat

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    Data-driven transformation of the economy will lead to significant changes in the business environment of X. Therefore, it is critical to anticipate from where disruptions will challenge X, to allow X to prepare for it. The following provides visibility about the unlikeliness of being disrupted amongst 4 areas. A high unlikeliness of disruption score is positive, while low levels urge for a deeper exploration of the disruption field.

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    Disintermediation threat (vertical differentiation 1/2)

    :: Anticipating a shortening of the value chain where companies leveraging data bypass you as intermediate.

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    Why care about this?

    Data-driven transformation allows companies to reduce transaction costs along the value chain, leading for example to a “direct to customer” strategy. To survive you need to anticipate such desintermediation early to develop a counter strategy.

    With an average unlikeliness score of 18%, the threat for X to be disintermediated is quite high representing the highest disruption threat for X and requires further exploration.

    Unlikeliness to be disintermediated (100% = Very unlikely)

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    Bargain threat (vertical differentiation 2/2)

    :: Anticipating an increase of bargain power of partners before or after you in the value chain due to ownership of data.

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    Why care about this?

    Data becomes an increasingly important resource. Companies owning data might be reluctant to share it with others. It is critical to anticipate which players has ownership about data that is important to you and early on develop counter strategies to decrease data resource dependency and lock-in effects.

    With an average unlikeliness score of 25%, the threat for X to lose bargain power towards partners that own data that is or will be relevant to X is quite high and requires further exploration.

    Unlikeliness to lose bargain power (100% = Very unlikely)

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    Entrants from adjacent markets (horizontal differentiation)

    :: Anticipating a reduction of market entry barriers through data leading to entrants from adjacent markets

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    Why care about this?

    As companies increasingly leverage data as a core resource, traditional market entry barriers separating adjacent markets are diminishing. Anticipating which players from adjacent markets are likely to enter your market allows to prepare adequate response strategies for example by strengthening diminishing market barriers.

    With an average unlikeliness score of 28%, the threat of entrants from adjacent markets is slightly lower than the other disruption threats, but should not be neglected.

    Unlikeliness of new data-driven entrants from adjacent markets (100% = Very unlikely)

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    Expansion of international competitors (geographic differentiation)

    :: Anticipating data-driven expansions of international competitors to own markets.

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    Why care about this?

    The data-driven transformation of direct competitors in other markets leads to synergies across geographies and a reduction of expansion barriers. To strive, your company needs to anticipate which international competitors are likely to expand their presence driven by data to your markets.

    With an average unlikeliness score of 23%, the threat of geographic expansions to X’s markets from international competitors is quite high.

    Unlikeliness of geographic data-driven expansion of international competitors (100% = Very unlikely)

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