Data Analyzing Tools And Techniques - Essay Example

Data Analyzing Tools And TechniquesAvailability of heavy and lengthy data related to all business fields and ventures has forced the organizations devise such tools that could present the facts and figures in a precise but comprehensive mode, so that steps could be taken in the wake of the information gathered for various departments and categories of the corporate firms and companies. Consequently, data analyzing tools and techniques witness a steady growth continuously. An overwhelming majority of the companies of national and international reputation are regularly applying these statistical and technological methodologies, particularly enterprise resource planning systems, customer relationship management efforts, point-of-sale scanner data in retail stores and web transaction data, for calculating their corporate ventures, profit volumes, cost analysis and employees performances.

However, these statistical presentations are unable to draw out in-depth coverage of the actual information even after the allocation of huge investment on data analysis. Consequently, alternative devices are being taken into serious consideration to transfer the knowledge along with statistical presentation of the entire data.The present study presents the model that successfully presents the model that accepts the challenge of transferring the data to knowledge and results in a comprehensive and refined way by encompassing every characteristic of the given information separately.

It neither drops any information from analysis, nor does it conceals basic knowledge the actual data intends to communicate; hence, it is of great value for taking steps towards right direction while taking decisions and devising strategies at different organizational levels at large. Policy making always relies upon accurate facts in recruitment, placement, promotions and even dismissal of employees; it is equally significant for examining of corporate culture, profit rate and visits and satisfaction scales of the customers regarding a particular organization as well.

The model is based upon five key competencies including software technology skills, statistical modeling and analytic skills, knowledge of the data, knowledge of the business and communication/partnering skills, all of which are essential for data analysis. Since over three fifth of the managerial staff views organizational and cultural factors as the greatest barriers to achieving targets, the model provides all-inclusive presentation about organizational culture too. Particularly, data related to sales volume is most important for managerial staff, comprehensive and analytical record of which is essential for future business plans in technological, transformational and analytic processes.

These processes are extremely helpful in providing the data relevant to the needs, requirements, demands and positions of the wide range of customers of the companies. Since customers are most precious asset for an organization, every company looks for the strategic schemes to capture the attention of the old customers on the one hand, and explore the new ones on the other. Hence, the model turns out to be extremely beneficial in respect of analyzing the corporate state of affairs and taking decisions subsequently.

The model is also supportive for monitoring the behavior and performance of employees in order to improve their output and dedication. Moreover, the data-to-knowledge model will certainly reveal the financial position of the companies in the light of which all future schemes could be brought into action.