Analytics, Interfaces, & Cloud Technology
The use of analytics and cloud technology is a new advance in computing which allows data collection and analytics to be done using higher processing speeds. It allows organizations to take full advantage of the huge amount of data that is collected through web analytics services. Miller Inc. collects a huge amount of data which is difficult to process. It also takes a very long time to process the data since the data is dynamic in nature. There is also a challenge in keeping data secure since the company needs to ensure its business and competitive advantage is protected. Through providing analytics services through cloud technology, Miller Inc. will be able to leverage the data driven frontier of cloud analytics. Basically, the company will be able to leverage the use of technological and analytical tools and techniques which it will design for its clients. These will help clients to be able to extract their much needed information from the vast amounts of data that the company collects.
One major benefit of cloud computing is that it will ensure privacy of collected data by allowing access only to authorized clients. Privacy and security are two major strengths of cloud computing and Miller Inc. will experience this benefits by implementing cloud technology Buyya, Broberg, & Goscinski, 2010.
Cloud technology will also create avenues for better data management and visualization through quicker and more resource-economical multivariate analysis on the data sets. It will also allow for massive centralization and distribution of the different data sets in order to solve complex problem for consumers. Cloud technology also provides the advantage of scalability meaning that the number of cloud computing services that the company provides will be able to grow as the company continues to grow. Cloud analytics will allow Miller Inc. To use complex algorithms to leverage the company's wide expertise with data collection and analysis towards providing better solutions for clients. This will include bringing on a team of experts who will be able to provide new solutions for analytics and reporting and generally making sense of the data. This will help the organization to provide consumers with more ways of effectively using the data collected through the system by providing tailored applications for each end user.
For Miller Inc., cloud computing will also help to provide a powerful abstraction method for the data that is collected. This method will also be scalable. Virtualized infrastructure as a service will be able to be provided where the company leverages converting complex data into fine-grained information for resource management. Cloud computing will also allow Miller Inc. To run data analytics applications in the cloud on extremely large data sets allowing the company to gain traction as the underlying infrastructure will be able to meet the extreme demands of the company's scalability. Typically, the cloud computing applications will leverage a framework that decomposes large computing queries into smaller parallelizable computations. The underlying storage architecture which is provided through the data warehouse will be able to fit perfectly into this computing framework Antonopoulos & Gillam, 2010()
Cloud computing uses a system architecture that comprises of cluster of low-cost servers that are large distributed largely in concert. These are designed to work with a server virtualization layer, data storage and parallel programming library. Several huge advantages comes from this cloud computing architecture. First is the cost savings for the company since the economics of developing, implementing and maintaining a cloud computing analytics service are much lower. Secondly the system is scalable meaning more computers can be added as the company grows. Third, the system will be highly reliable since it will be able to handle failures in the system even if they are frequent. Another advantage is the efficiency that cloud computing will add to Miller Inc.'s analytics system. This is because the cloud computing system will use less computer, network and disk resources.
Cloud computing also creates the advantage of recovering transactions and progressing interrupted transactions in the event of multiple disk or node failures. Since cloud computing allows for Miller Inc. To use replicated data sources across multiple nodes, in the event of a disk or node failure, the undergoing computation can simply be restarted on another node. Cloud computing also leverages the data warehousing model that is adopted by Miller Inc. And allows specialized file systems to be used based on the ability of the organization to handle multiple infrastructure failures Jamsa, 2011()
Cloud computing also allows Miller Inc. To leverage the use of utility computing meaning that the company will be able to intelligently store huge chunks of data in smaller storage units which reduces storage space and distributes the data among the computations reducing the amount of time spent in processing transactions. Cloud computing will also allow Miller Inc. To develop an interface for administrators to deploy, execute, and integrate statistical scoring models, decision tress and regression models to be able to understand the data better.
Cloud computing will also allow Miller Inc. To forecast its resource demand through its scalability. This is because the company will be able to monitor the system effectiveness with ease and thus allow them to make noticeable reductions in budgetary allocations for computing resources. It will also allow the company to provide analytics as a service (AaaS) to its clients by providing tailored solutions for the clients. This will include drafting of product roadmaps as well as market analysis which were traditionally harder to accomplish.
Recommended service provider
It is recommended that Miller Inc.'s cloud computing system be built on Google's GFS (Google File System) which allows for data-intensive applications such as Google's MapReduce framework to be utilized. This is because the operations that are conducted in Miller Inc.'s system are highly data-intensive and therefore the MapReduce framework will provide the advantage of decomposing the computations into smaller parallel computations thus reducing the resources utilized and processing...
First is that they can co-locate data computations which reduces usage of network resources considerably. This will reduce Miller Inc.'s bandwidth costs considerably. Another advantage is that it provides easy ways to modify the file system being used in the cluster through altering data allocation or information on the data layout which allows for better analytics to be run on the data.
Another advantage is that since Miller Inc.'s data analytics applications are read -- and write-intensive, the system will be able to provide block granularity that is required for prefetching data and accessing disks. Therefore it will provide solutions for writing and reading data locally faster. One disadvantage of using Google's GFS system is that it does not play well with traditional cluster file systems. The company will thus need to develop, test and implement the data warehouse full before thinking about utilizing this framework.
Project plan
Task
Person in-charge
Time
Planning
Prepare project goals and objectives
Project manager, IT staff
1-week
Review project goals and objectives
Project manager, IT staff
2 days
Review and recap proposals and contracts
Project manager
3 days
Assess opportunities and risks
Project manager
2 days
Identify constraints and other obstacles
Project manager, IT staff
3 days
Identify required nonhuman resources
Project manager
2 days
Review scope of project
Project manager
2 days
Identify the procedure for monitoring and evaluation of the project
Project manager
1 day
Project high-level scope of the project
Project manager
2 days
Assemble core project team
Identify required skills from organization
Project manager
2 days
Identify required skills from other project stakeholders e.g. IBM who are the owners of Oracle
Project manager
3 days
Analyze availability of staff
Project manager, administrator
2 days
Nominate project team members
Project manager
1 day
Review team member's availability
Project manager
1 day
Team members accept responsibility
Project manager, all team members
1 day
Analysis
Analyze and design the project organizational units
Project manager, administrator
2 days
Analyze organizational roles
Project manager
2 days
Analyze stakeholder relationships and roles
Project manager
3 days
Analyze and design network identify policies
Project manager, all team members
2 days
Analyze and design policies and workflows for provisioning
Project manager, administrator
3 days
Analyze and design high level methods for importing data
Project manager
2 days
Analyze software and hardware architecture
Project manager
2 days
Design
Review project goals and objectives with team members
Project manager, all team members
2 days
Develop project plan
Project manager, all team members
Identify resources required
Project manager, all team members
2 days
Review project plan with identified resources
Project manager, all team members
3 days
Design test strategy
Project manager, all team members
1 day
Project communication
Develop communication plan
Project manager, all team members
1 day
Identify and publish project updates
Project manager, all team members
1 day
Publish problems for issue tracking
Project manager, all team members
1 day
Publish scope control instruction
Project manager, all team members
1 day
Publish schedules of the team and team members
Project manager, all team members
1 day
Develop team status reporting rules
Project manager, all team members
1 day
Identify and publish functional responsibility of team members
Project manager, all team members
1 day
Implementation
Confirm suitability of workspaces
Project manager, all team members
1 day
Confirm stakeholders are on board
Project manager, all team members
1 day
Confirm availability of resources
Project manager, all team members
1 day
Conduct final checks before project starts
Project manager, all team members
1 day
Project implementation
Data warehouse design
Project manager, all team members
1-week
Obtain database schema
Project manager
1 day
Review database schema
Project manager, all team members
1 day
Obtain proposed file layout
Project manager
1 day
Review proposed file layout
Project manager, all team members
1 day
Map data to proposed database schema
Administrator
1-week
Entity relationship modeling
Project manager, all team members
1-week
Develop entity relationship diagram
Project manager
1 day
Check conformity of ERD to database schema
Project manager, all team members
1 day
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