With growth in disruptive technologies and rapid increase of powerful mobile devices and other gadgets, an enormous data explosion is being witnessed which is accelerating at a great speed. The volume of data is not only growing rapidly but it is becoming more and more varied, complex and less structured. As a result, the term ‘Big Data’ has evolved and growing big in the minds of every entrepreneur who desires to extract critical insights and derive business benefits out of it. This data is characterized as magnificent, multifaceted, and highly difficult to analyze.
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While some organizations are planning to take initiatives to handle Big Data, others have already started working on it. However, most organizations lack in an articulated strategy for this Big Data execution. A study conducted in June’13 established that 64% of the corporations were investing or planning to invest in Big Data technology in 2013, 30% had already invested while 19% decided to invest within the next year, and an additional 15% planned to invest within 2 years.
This clearly shows the potential along with the underlying risk caused by this strategy gap, and it is this risk factor that makes enterprises act cautious about investing in Big Data initiatives.
The corporations that are investing or planning to invest in Big Data initiatives have found that a combination of competencies that execute this Big Data implementation is really difficult to pull together. This implies varied competencies like data management and analysis, predictive modelling, visualization, and industrial experience are to be implemented in conjunction to achieve the best results.
Also, it is to be noted that the right mix of technologies may deliver the promised results; but, at the same time, the leaders must choose and incorporate a specific business goal for interlocking the set of data sources and technology. It is this association that makes the initiative unique. It not only brings about linear improvements but also radical changes in the prevailing business processes and can even result in absolute transposition for long-lasting benefits. Resultantly, the Big Data project tends to focus on collecting, assimilating and processing the available information which also involves focusing on two important questions:
What benefit can be generated from the given data?
Will the value generated will be more than what it costs us to collect, assimilate and process it?
In response to the above questions, Big Data providers have created a strong ground via the use of cloud computing at the core. It enables corporations to analyze the feasibility and relevance of Big Data solutions in their IT operation sphere at a fraction of the cost that they might have to invest in developing in-house capabilities.
Basically termed “Big-data-as-a-service” (BDaaS), it refers to those services that provide analysis of complex and huge data sets, typically over the cloud platform as a primarily managed service. From the point of view of delivery, it perfectly combines the cloud, the application platforms and the SOA with analytics for deriving real-time predictions and excellent decision-making.
The adaptation of Big Data on the above technological grounds precedes with Hadoop infrastructure which was actually a major stepping stone in this regard; but it has its own limitations for Small and Medium Businesses, the SMBs. This is because they do not have the resources to create such an expensive infrastructure in-house. Moreover, the know-how of how to use Hadoop is limited which makes it important for Big Data services to be user-friendly; and for SMBs, more cost-effective. BDaaS can effectively overcome these issues by exploiting the flexibility of cloud computing.
BDaaS proves to be more cost-effective as compared to the traditional setup where you need to pay for hardware resources that often remains underutilized and requires regular maintenance and upgrades. On the other hand, Cloud offers services on a pay-per-use basis. This provides enough flexibility to SMBs.
BDaaS lets the business owners set up applications in minutes, instead of working for months to construct the entire infrastructure and the related software. Also, it eliminates the need to hire a data scientist as providers offer easy-to-use tools and extend technical support during the project lifecycle.
As such, it is difficult to predict which Big Data solution businesses will commonly agree upon, but a majority of Big Data service providers are now providing a cloud version of their platform, and it can be said envisaged that it will emerge as a safe bet for SMBs to invest in.
