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Anti-Patterns in Big Data convergence with M2M


It is almost an open secret right now that the next big trend in Big Data space would be the adoption in M2M and ‘Internet of Things’ arena. If any major indicators of the battle that would happen here need to be gauged, then one needs to look at major GE investment in Pivotal and Intel’s own distribution of Hadoop.

However, as the buzz goes around, there are still some M2M (machine to machine) anti-patterns which seem to be hyped as the inhibitors.
Defining Anti-pattern:
“there must be at least two key elements present …:
  • Some repeated pattern of action, process or structure that initially appears to be beneficial, but ultimately produces more bad consequences than beneficial results, and
  • An alternative solution exists that is clearly documented, proven in actual practice and repeatable.”

Let’s analyze 4 key notions and assuming them to be anti-patterns, counter them with possible solutions trade-off.

1)      The silos of Internet of Things

While it is true that the current focus is ‘Internet of Things’, the reality is we still have only ‘Intranet of things’. This means that the data exists in enterprise or organization silos only and it is rare to see devices of 1 organization talking to devices outside the boundary to devices of another organization.

  • Data Mashups – It may be a while before data will co-exist in some industry wide warehouse. Till that time, data mashups can be of excellent assistance. Therefore, it makes excellent sense to utilize the current data in silos, mash them up with other available sources and drive business insights from them.


2)     Trust among talking Machines

 ‘Trust’ is a widely debated topic is any sort of information exchange. In case of social networks, developing ‘trust’ still involves manual intervention. For instance, we share the data (status, pics, comments) on most social networks like Facebook only with trusted group of friends. The friend requests itself are accepted on social networks only from ‘trusted’ people. The same concept extends over in machine to machine requests albeit with less manual intervention and intelligence.

  • Analogous implementations – Many industries like financial services have successfully implemented trust based model for systems. Similar analogous beginnings for M2M may be easier to replicate and succeed.


3)     Skill grill

Both M2M and Big Data implementations are specialized vendor implementations. There is a scarcity of solutions, vendors and skilled work force currently for it.

  • Training - While it is a big talking point that Big Data, Hadoop skills are still far and few, the same discussion also extends over to M2M skills. As in Hadoop, where training and community outreach has been increasing penetration, so is it for M2M.


4)     Money to money

Most projects are guided by a strong monetization factor and the ROI figures dominate the proposal clearance. Currently, M2M is still in rudimentary stage and enterprise still needs to en-cash over investments being made.

  • Data as an asset- As a known business model, enterprises are known to reap rich rewards on their data assets. What may be of monetary value to one CSP (for instance), the same may be a competitive differentiator and strategic asset for the other. Consequently, blind focus on immediate returns may not yield any benefit.


While each firm may employ its own methods and strategies to deal with the M2M bubble, it is evident that Big Data technologies like Hadoop can be a key differentiator in creating the niche platform base. To that end, we will keep monitoring the space as the projects evolve over to create differentiated solutions.

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Top image source: Navigant Research

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