The present information world has become more complex and still continues. Volumes of data are multiplying and come from various mediums such as podcasts, tweets, Wikis and blogs. Therefore, the speed with which everything is done has accelerated. A Company has to analyze, contextualize and shape information to enable them make decisions. Besides, globalization requires better information sharing not only with colleagues but also the entire world at large. The analytic cloud technology responds to challenges that information posts. Hence, they have enabled companies to get the opportunity to obtain a competitive advantage through business analytics and intelligence.
Analytic cloud technology is a service offer that enables company analysis and intelligence at ones location in analytic cloud deployment. Its goal is to make businesses better and strengthen the organization to enable all employees mostly those close to supplies and clients to make sound decisions. Analytic cloud computing technology is used both as an infrastructural management method and a delivery model for businesses. The delivery model offers one with a standardizing service. These services include analytic businesses that can be provisioned rapidly and accessed easily. Steps such as installing software and middleware, hardware production, and network provision are well simplified. Methodological Infrastructural management is built on resources that are virtualized and provides increased ability to scale and better economize. Therefore, it enables large volume with low cost of analytics possible. Transformation to analytic cloud and technology has made changes in business economic analytics, and intelligence. The increase in the speed of service provision by the use of the technology has enabled a range of new business possibilities and present data analytic management projects.
The smart analytic cloud technology service provides complete and consistent solutions that enable businesses to create a basic approach to providing company intelligence and analysis within an enterprise. For instance, the internal private cloud analytics support one petabyte data and enables above 200,000 employees worldwide to make material decisions. It enables this by offering them real-time information on supplies and customer, whether they are in their offices or at the working fields.Cloud technology has enabled computation of various problems that were initially unsolved. Problems such as correlation of sensor data, processing of large scale image, decryption/encryption, simulation, data mining and pattern recognition. All these problems can be solved by the use of analytic cloud technology.In addition, it allowed analysts to run over a large volume of data narrowing down to small and personalized analytic results. The company can then store this result in a rational traditional database. The result also allowed existing financial and reporting tools to remain unchanged but to gain from power of cloud in computation.
Similar capabilities are also applied in other organizations using this technology. Analytic data clouding incorporates data warehousing in such areas as data cleaning, data refreshing and data loading.
Since a data warehouse is involved in decision making, it is essential that correction is in the warehouse. Due to the large volumes of data involvement from various sources, the warehouse is prone to abnormalities and errors. In this case, tools that can be able to detect such errors should be available. The tools that are used in data cleaning include data migration tools, scrubbing tools and auditing tools.
Data migration tools
Data migration tools enable specification of basic transformation rules. These rules include replacing string, sex and gender. An example of this tool is the ware house manager.
Data scrubbing tools
These are tools that use specific domain-knowledge such as postal address in scrubbing data. They majorly exploit fuzzy and parsing techniques to achieve scrubbing from various sources.
Data auditing tools
These tools make it possible to discover relationship and rules by scanning data. Therefore, these tools are also referred to as data mining variant tools. For instance, a tool can determine suspicious pattern that a certain car dealer has not yet received complaints.
After extraction, transformation and cleaning have taken place, the data has to be loaded into the warehouse. In addition, preprocessing may be needed which include sorting, aggregation, summarization and check of integrity constraints to create tables in the warehouse. Therefore, the batch load utilities are involved in this purpose.
Refreshing refers to propagation of updates on the source data corresponding to the derived data and the base data available in the data warehouse. Here, two sets of issues need to be considered. They include, how and when to refresh. Normally, the data warehouse is refreshed periodically e.g weekly or monthly. However, propagation of every update can be done when some OLAP queries need them. The administrator set the refresh policy considering the needs of the user and traffic. The policy may vary with varying sources.
Advancement in IT has largely derived Company analytic cloud application. The benefits of analytic cloud technology go far above and beyond the obvious saving of cost on software and reducing maintenance burden. The significant role this technology can play within and around an organization with regard to driving productivity and innovation makes it increasingly the best option for businesses especially companies that want to remain agile and competitive.