Skip to main content

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.

----------------------------------
Top image source: Navigant Research

Comments

Popular posts from this blog

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…