Project Plan Development - IT and WEB Development

Introduction

Data today is being generated from different sources that may include social media, information superhighway, transactional applications, and log files among others. A huge portion of this very data is generated and transmitted on a real time basis and also in a large scale. In order to derive meaningful value from such data sets, an enterprise should be in a position of deploying sophisticated data analytics techniques that combines natural language processing, data mining, machine learning, text analytics, and traditional Business Intelligence (BI) among others (Annant, et al., 2010).

Over the past decade cloud computing has emerged as a cost effective paradigm that can be used towards expediting gigantic data storage and analysis. Modern cloud infrastructure come engineered with capabilities to avail adaptive resource with minimal initial capital while at the same time scaling to huge quantities of commodity computing nodes. Going by the fact that data analytics is very resource intensive, this implies that it has the potential to transpire as a vital cloud based application.

Literature Review

Analytics as a service enables enterprises regardless of size to wholesomely to access and benefit from various remote analytics platforms available through the information superhighway. Flexible cost model associated with cloud solutions provide the ability to apply on-demand computing that is specific and highly targeted towards business and analytical requirements. Service users will also access the cloud based analytics experts together with their cumulative experience on demand. With predictive analytics deployed in such a manner, solving business problems is faster, easier, and the return on investment also gets improved.

Today organizations in different sectors are actively engaging themselves with Analytics as a service in order to come up with new analytical models that shine spotlight on their data assets and at the same time assist clients to gain valuable business insight with capabilities to deliver substantial Return on Investment (ROI), and this is mainly witnessed through activities that may include fraud prevention, response modeling, customer retention, and customer profiling and segmentation (Sheikh, 2013).

Analytics as a service enables organizations and system developers obtain real-time business insights from huge amounts of data without necessarily incurring massive IT investments. These business insights are accessible via simple user interface, and what makes the technology more ideal is that data is always protected though various layers of security that are replicated across multiple data centers that facilitate expedient exportation (Srinivasa & Bhatnagar, 2012).

Analytics as a service also enables an organization to run ad-hoc SQL like queries against data sets that comprises of billions of rows. This may be organization specific data or plainly data that has been shared from outside sources that may include partners or clients. Analytics and cloud based technology works best with interactive analysis of multi-terabyte datasets that enables an individual to append fresh data. As a result, business analysts may share insights with their organizations data courtesy of proactive applications that have integrated engine based dashboard systems and control access courtesy of fine grained access control list.

Analytics as a service from the cloud usage also includes, but it is not limited to the following;

· Segmentation analysis on millions of business clients in order to identify tactful legion for targeted marketing campaigns

· Proactive monitoring of the dash board system for seamless operations management with capabilities of drilling down into more problematic areas

· Mashing up different business data such as regional market spending in order to discover previously unknown business correlations

Some of the primary features often bundled with analytics as a service cloud storage platform includes;

Scalability: The cloud storage should scale seamlessly to hundred of terabytes with absolutely no human intervention being included

Speed and flexibility

· Ad-hoc queries on multi-terabyte datasets

· SQL like query syntaxes and intuitive User interfaces

Security and reliability

· User defined ACL’s that are used to control data access

· High availability and data redundancy

How organizations stand to benefit with analytics as a service of the clouds

Enhanced business insight courtesy of real-time data analytics

The organization is able to leverage on the enormous computing power in order to derive valuable business insight from the big data in milliseconds rather than waiting for hours or even days. Cloud real time analytics enables organization leverage on such massive data without upfront investment in different IT related resources (Jay, 2014).

Analyzing, Sharing, and Scaring big data

Organization executives do not need to spend their time learning complicated interface or their programmers leaning new complex algorithms because it is possible to access big query through simple user interfaces.

Secure, exportable data

The data stored on cloud based storage is highly available because it is replicated across several data centers and at the same time protected using enhanced levels of security which implies peace of mind.

Flexibility in terms of pricing structure

Running big data analysis does not require the operation of resource intensive data centers because the enterprise can terminate the contract anytime, there are no capital cost involved, and no upfront risks. The billing system is very simple because it comprises of only two components which are query processing and storage. You only incur cost for what you have consumed.

Proactive technical support

Such solutions also come with various levels of technical supports and this may include chatting systems, tool free numbers, and online user forums.

References

Anant Jhingran, Stephann Jou, William Lee, Thanph Pham, Biraj Saha, 2010, IBM Business Analytics and Cloud Computing: Best Practices for Deploying Cognos Business Intelligence to the IBM Cloud, MC Press LLC, 2010

Nauman Sheikh, 2013, Implementing Analytics: A Blueprint for Design, Development, and Adoption, Newness Publishers.

Srinath Srininavasa, Vasudha Bhatnagar, 2012, Big Data Analytics: First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012 : Proceedings, Spriner Publishers.

Jay Liebowitz, 2014, Business Analytics an Introduction, CRC Press.