There has been a surge of structured and unstructured data, particularly for financial businesses, arising as a result of cloud computing. This increment is putting IT department of enterprises like banks and stock trading firms under the gun. The assumption was that business analytics, Big Data, and cloud computing is the road map for success. However, information overload is complicating the effort to pull valuable business insight from these three domains.
The resultant scenario is a cavalcade of options to manage Big Data influx to remove the burden on the profit driven intentions of an enterprise.
Strategies to manage Big Data
In order to improve the state of Big Data handling (and thereby making you able to better serve your clients) four approaches can be used. The target is to explain how the IT infrastructure of any enterprise can be reformed to make better choices. And the key focus is on how Big Data can be used and assimilated for productive usage.
1. Keeping the architecture compact
The simplification begins with the architectural stability. The IT managers at financial firms can look at whether recent options such as Hadoop Clusters, NoSQL database or similar tools can fit into and are secure enough for the enterprises’ existing system architecture.
Hadoop is already being used to simplify cloud and Big Data platform. Using MapReduce and Hadoop Distributed File System (HDFS) is becoming a popular choice for IT teams to manage cloud based Big Data. Modification should only be done when necessary, since the stability of architecture is paramount.
2. Outsourcing fiscally significant activities
A secret of smooth business analytics is finding the right balance between outsourcing and development of internal IT structure. Outsourcing should be focused on activities of fiscal necessity. The reason is that internal IT structure is cost intensive, and may burden profitability.
Contract management is example of one such activity. One Example of a financial platform provided by Contract Logix shows that outsourcing benefits companies by reducing the time within contracts are managed. Since the outsourced firm would be managing the contracts, the enterprise’s valuable human resource and IT capital is saved. In addition, there is no need to custom built contracts for each individual client since this procedure generates a database based on the enterprise’s preferences.
3. Minimizing the ‘Cloud’ vault of Big Data
A misconception within modern businesses and the financial sector is the thinking that Big Data equals competitive advantage. The more the data a company injects into its IT center, the more intricate and well expanded should the IT infrastructure needs to be. However, the right approach is that the ‘enterprise data warehouse’ should be compact and prioritize the business’s transaction system for analysis, rather than looking to understand the market as a whole.
A new technology in development reduces the cost needed to manage offline data warehouses. Being developed at Stanford, it envisages smaller cluster and better retention. Such breakthroughs would allow cloud computing and Big Data to be managed at lesser costs.
4. Improving data scalability
A recent effort from the 451 Group has resulted in the formation of a subway map simulation that depicts the plethora of Big Data choices available. The detail is appealing, since it color codes all data products available for companies.
One important inference from such resource is that enterprises should index file systems as per business needs. New systems such as NoSQL database as well as NewSQL hybrids are good at data scalability. NoSQL has become popular, since it process data into graph databases and also generate key value stores.
In the modern IT spheres, particularly for companies managing financial documents and market trends, where complexity is a rampant existence, simplicity would be a market advantage. The four point list provided above is a road map for enterprises to reform their IT structure and ensure a flexible information system.
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