Project Data collection

The project data collection plan serves as a vital link between the numerous individuals and departments involved in the execution of a particular project. This essay explores the development of a comprehensive project data collection strategy for Fix Insurance Group, a business that aims to stop insurance fraud by providing medical claims processing and management services that guarantee prompt payment, minimize billing errors, and prevent medical fraud to enhance the financial performance of medical insurance underwriters and service providers.
Through a system through a web-based application in certain modules, Fix Insurance Group offers electronic claims filing services in specific modules. This is a channel between the underwriters, policyholders and the service providers in claim processing.

Data needs

Data is a very valuable asset for its strategic importance for the running of business affairs in the digital world thus requires a keen address in the handling, managing and storing. The firm will run under departments of Human Resource, Marketing, accounts, sales and IT which to a great extent relies on each other to guarantee a reliable and a stable data collection process to achieve the groups’ goals.

Data needs for the firm involves running the departmental systems while reaching out to solving our clients’ troubles in the insurance sector. Ultimately, the goal is to determine the reliability of the claims process by ensuring Claims are not billed more than once thereby curbing insurance fraud. The needs are employee information, company assets, and expenses cost, purchases, sales team track record, goods performance, new customers and company expenditure.

The data details such as employees’ information will be used to create cloud account by IT department for remote access, printing staff/customer identity cards. Type of cover, date, cost, staff/customer ID will be used to evaluate performance through which the profits and loss statements are generated thereby coming up with the covers that are most needed and what additional cover that may be included.

Additionally the Claims, customer/staff ID, amount paid or due, service provider, builds up the status of every insurance cover making it a reliable trace route in case of fraud and also helps to know the Claims trends which are the most fraudulent claim and the common causes.

Type of data to be collected and who will use it

Employee information is important so as to link him/her to how much insurance Claims fraud is sold, while purchases and sales records account for the claims count totals after being processed necessary to rate and determine the number of fraud cases solved by the firm in a given period from the clients’ information.

How data management system will contribute to organizational collaboration

For the success of the groups goals the departments have to work together thereby reflecting on the organization collaboration from the data management system that links up the various people used at different points, handling the various levels of data that in the end build up to be a stable data collected for the running efficiently of the groups’ affairs.

Data management system is the link to organizational collaboration through the assessment of employee performances, getting the trends and tastes of clients making it easy to come up with marketing strategies, tracking the sales accounts for individual agents thereby it is easy to award bonuses. Simply the system facilitates collaboration through sharing of information between departments that lead to necessary decision making.

Innovative plan for collecting, analyzing and distributing data

The main objective of data collection, analyzing and distribution of data is enhancing efficiency in the simplest form possible. It is important to digitally transform the business by reaching closer to the customers through laying out a competitive advantage and optimizing the cost and the efficiency of IT through handling of company data.

Having the ability to consolidate, rationalize and come up with an easy flexible ICT base that will be advantageous for operational data stability and flexibility. By getting this right we achieve a high performance.

The advancement in technology brings up a host of options for the group to employ in the process of collection, analyzing and distribution of data. Cloud computing makes everything simple since by incorporating it we are not limited to a particular working location the operations are open to the world over just at the click of the browser thereby expanding is way simpler.

Employing virtualization in the distribution of data where the host machine serves the guest machines making sure the sharing of data is smooth speeding up things in case of peak period use or even maintenance by transferring the guest to a better suited hard drive.

Big data innovation plays a critical role in any business from the raw data collection to processing and analysis leading to insights, products, and better services thus it is a great component in the Fix insurance group data analysis. Additionally, other tools will be used, they include SMS notifications, plotting of trends graphs in sales and purchases, highest sales representation in a month under data collection plan.

Conclusion

The groups’ project data plans needs are in place, considered through the various high level strategy to be used in the collection, analysis, use and storage of the required data that will ensure an efficient running of activities digitally.









References

Irimia R, Gottschling M (2016) Taxonomic revision of Rochefortia Sw. (Ehretiaceae, Boraginales). Biodiversity Data Journal 4: e7720.Retrieved from https://doi.org/10.3897/BDJ.4.e7720. (n.d.). doi:10.3897/bdj.4.e7720.figure2f

Jones, S. (2011). ‘How to Develop a Data Management and Sharing Plan’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Retrieved from http://www.dcc.ac.uk/resources/how-guides

Proceedings DCC 97. Data Compression Conference. (1997). Proceedings DCC 97 Data Compression Conference DCC-97. doi:10.1109/dcc.1997.581948. Last accessed 3rd november2017