Business analytics on a cluster is used to develop insights into business performance by analyzing historical data. Results of these analyses are then used for planning and decision making. Companies test theories by applying statistical methods to data such as projected growth, migration rates, consumer buying patterns and related historical data. This allows the researcher to project the future performance of everything from the sale of goods and services to the probable success or failure of a new business or market.
Our relationships with the tier one vendors and ISVs allow us to customize a system to meet your needs in processing the massive data required in business analytics. We can design and implement a system for you whether you are looking for a new system or to upgrade your current system. NSG specializes in providing 2 – 64 node clusters for the SMB market and can design a system that offers you the best value for your budget.
|• New Market Analysis||• Cross Sell Strategies|
|• Optimize Growth Rates||• Competitive Analysis|
|• Profitability||• Customer Management|
Market analysis is used to determine the viability of a product, service or market, both now and in the future. Organizations evaluate the future potential of a market by gaining an understanding of evolving opportunities and threats. These are then compared to that organization's strengths and weaknesses to make operational decisions. Parameters such as demographics, competition, market indicators, and client characteristics must all be analyzed in unison to make informed decisions. Common output projections of market analysis are:
|• Market Size||• Growth rate|
|• Market trends||• Cost|
|• Profitability||• Customer Characteristics|
|• Scenario Analysis||• Loan Analysis||• Interest Rate Simulations|
Decision Support, also called a Decision Support System (DSS), is used for business and organizational decision making. A DSS system will allow you to access and manipulate data over a time period to determine trends that affect company operations. DSS Systems require the input of massive amounts of data to produce meaningful output. Operational and profitability decisions can then be made on either an automated or human basis. A few examples of DSS systems data include:
• Sales figures between different periods
• Projected revenue based on estimated sales and expenses.
• Projected inventory level requirements
• Man power projections
• Inventory control