Table of Contents
Introduction
Data management is now an essential aspect of corporate management. If properly integrated into the company’s overall business environment, data management using IT can provide a range of benefits in operations, HR, marketing as well as finance. In the same way the irresponsible handling of data can lead to significant ethical concerns. This article outlines the specifics of data management and provides the most prominent ethical concerns by using retail as an illustration.
Data Management
Operations
The operation aspect of retail requires many people. The most active are management, employees as well as suppliers. Suppliers typically provide data via inbound delivery of items. This information could include information about the delivery date, amount, and price of goods delivered to the warehouse. After that the responsibility for managing data is transferred to employees who, based on the technology utilized in the firm send the required quantities of products to departments or provide management with a report about the dispatch. Information on goods moved in the sale department are taken care of in a way that is automated. In addition the data regarding employee performance is managed by the manager in charge of line. In the end, management is responsible for submitting the information to marketing and finance departments.
The primary process involved in the management of data in retail is the management of the goods. After arriving to the storage facility, items are tagged on the computer system to indicate that they are’available. The sales department is informed of the change in surplus and may make the required items. The data on sales is collected in a way that is automatic during the process, and then compiled into a set which can be retrieved by the marketing department to use for analytical reasons (Fernie Sparks and Fernie 2014). The system also keeps track of crucial variables like expiration dates for goods to enable better management of resources. Inconsistencies of supply (e.g. unexpected shortage of goods that is determined separately in each section) are recorded and then sent to managers in charge of interactions with suppliers.
Most of the mentioned procedures are executed by using enterprise software. The software solutions can be purchased as ready-made alternatives or customized according to particular needs in retail business operations. The platform is able to be operated internally or hosted in the cloud. A small portion of the information (e.g. the inbound shipment from suppliers that do not have suitable equipment) is sent to the system in a manual manner and the rest is automatically recorded by using cross-compatible formats (Fernie and Sparks, 2014). Additionally the system can be used to disaggregate the information about consumer behavior and employee performance in order to modify the current strategies and tactics.
Finance
The financial department has two primary stakeholder groups. The first is the individuals who provide information to the department. In addition, although the bulk of the data generated is generated by these employees’ actions, only a small portion is manually collected and entered. The other group comprises accountants who collect, analyse and analyze information.
The procedures that pertain to financial data management encompass all actions that result in profits or expenditures like information about sales and inbound shipment, marketing costs, and operating costs, among others (Einav and Levin 2014). The information from various sources are arranged in order so that it can be used for seamless processing. After all the data needed to be used for a specific period has been found, it is put into balance sheets, and then checked for integrity and accuracy.
At this moment, the need could arise to find new areas of analysis, or to locate and remove redundant ones to maximize performance. The data is processed using the tools that are available within the enterprise solution and analyzed to determine the most significant developments, issues and benefits. The data is then compiled into an appropriate format using visual aids, and then presented to management. In the end, data required to be disclosed is provided by way of publically available report or presented to auditors. Most of the mentioned tasks are carried out using software for statistical analysis that is that are integrated into the enterprise software. In certain situations other tools can be employed to support the structure used by the organization.
Marketing
The primary stakeholder in the management of marketing data process is the customer. On the other aspect, they provide the primary source of information about the patterns of consumer behavior that can be utilized to create or modify marketing strategies and strategies. However they form the principal goal for these tactics. The other group of people who are relevant to the process are people employed by the marketing department who pick the tools they use to collect information, supervise the collection process, analyze the results, and provide suggestions for changes that are needed.
The process of collecting data is made easier by two primary methods. First, the sales data that is available from an Enterprise Management System are provided to the department of marketing. The data is then disaggregated, that allows for the identification of various segments of the targeted market and thereby achieving the necessary diversification of options. Another source of information contains tools that are specifically designed to answer direct questions. These tools include surveys as well as questionnaires. These tools may either produce data in digital format or require conversion of the data following the end of the study (Gandomi and Haider, 2015). It is evident that the second option permits a wider range of data to be collected. Furthermore, the specificity of the surveys guarantees the accuracy of the information gathered. However sales data can be a more economical method.
The tools used to collect data include online survey tools, statistical software for quantitative and qualitative analysis and enterprise management systems for collecting and transmitting sales data. The majority of data processing is automated, with some minor variations like manual input of qualitative data into analytical software. After the data needed is collected, it is processed by statistical tools to discover patterns in behaviour that are that are responsible for the satisfaction of customers. The information is then passed to the management department of the organization and then incorporated into corporate decision-making.
Human Resource Management
The primary stakeholders in the HRM process are employees of the company as well as employees and the HRM department. The HRM department is also responsible for the effective utilization for talent inside the company and the enhancement of employee potential. To reach these objectives, it is important to collect the relevant data as well as identify variables that are necessary and then determine the most appropriate strategy according to the findings.
The HR-related data is gathered using a myriad of tools. The most widely-used types are KPIs – measures that are deemed to be important to employee performance. Based on the nature of company and particulars of the business environment various combinations of KPIs are possible to identify, such as the average spend of customers and gross sales/square foot and the gross profit, for instance (Stone and others. 2015). The collected KPIs are tracked and recorded by using a dedicated software or functions of the ERP management software. This allows seamless and automated processing of information from employee actions. Additionally to this, a KPI dashboard is a good option to organize the most relevant KPIs , results and also the time of appearance of any issues.
Data Integrity
The information used for business purposes have meet the requirements of completeness, timeliness, and precision. Any compromise in any of the mentioned areas could result in various negative issues. The most evident negative consequences result in a decline in performance , and in turn, the financial viability of a company. One example is the information on satisfaction rates following the implementation of a new strategy. In this scenario the completeness of data is dependent on the validity of the measures used to assess the employee’s response to the changes. This can be achieved by determining correlations within the data that are available and then incorporating those that are most pertinent when analyzing the data.
In the next step, the accuracy of the data is dependent on the quality of the tools for data collection as well as the quality of the analysis performed. This can be achieved by developing a program suitable for the process employing one of the pre-designed tools that are compatible with the objectives of the assessment. This is accomplished by removing the chance of human error in data input, analysis and analysis. Most of the issues are eliminated through automation of the collection and analysis of data using enterprise solution (Martin, Borah & Palmatier 2017). In addition, the accuracy of data is guaranteed by the computing power of IT-based solutions. Except for big data management analytical software can produce results instantly which can be more responsive to various variables.
It is also crucial to recognize the legal implications of integrity of data. The accuracy of financial information provided for audit by independent bodies depends in the absence of truthful mistakes. In this instance, properly designed accounting software will ensure the validity of the results and reduce the chance of reporting incorrect data, thereby maintaining the value of shares owned by the company in the stock market.
Ethics Perspective
It is also essential to be aware of the ethical aspect of managing data. Most of the time, the information needed for marketing analysis is sensitive, and has a variety of issues. It is possible that the information in question could be used to cause harm to the individual who provided the information. For example, it could be naive for a company to make use of the information about the contact to provide targeted ads without the permission of the owner, which would be an obvious breach of the privacy of individuals. Also, it is possible to envision an eventual scenario in which the data used for statistical research is made accessible to a third party. This could happen as a result of an inadvertent flaw in the security of the system or in the event of an intentional attack. In the case of criminals, they can gain access to an untrusted vendor’s database that contains its clients’ financial details and put all the funds of their customers at risk. Additionally, the information could be leaked to an outside party due to insider activity (Hemphill and Longstreet 2016). In addition, an organization could be enticed to sell the data to a third party without permission for such transactions.
A variety of security measures are available for to reduce the risk. The first is that the information is required to be secured using algorithms and software that meet the safety standards for the industry. Furthermore, handling data must be confirmed by a trusted person to confirm the authenticity of the action. Thirdly, the data must be de-personalized by removing sensitive demographic data which could be a source of danger of being exposed. This method is, however, applicable only in the instances when the data removed is insignificant to the outcome that the research has produced. And, perhaps most importantly it is essential that the information be removed in a timely manner following the results achieved, which will keep it from being leaked again.
As you can see as a result, all approaches require the use in time and money. For instance the processing and storage of encrypted data requires extra costs to help familiarize employees with the latest technology. The proper storage of data requires dedicated software and hardware. However the conflict of interest creates certain requirements for the data handling process. It is natural for an organization to be enticed by the prospect of compromising their morality and improve the profit margins of their company. But over the long term having a clear and solid system for managing data will increase the accuracy, speed, and completeness of the data and at the same time reduce the risk of a data breach.
Conclusion
As you can see the potential applications of information technology in managing data in retail are diverse. The capabilities that enterprise-scale software solutions provide many improvements to the process of selling, allow smooth data gathering and submission and aid in the timely identification gaps and obstacles. Additionally, they aid in improving the quality of financial data. Additionally, these tools can be utilized to determine new patterns of customer behaviour and integrate the results into the company’s overall strategy and evaluate the viability of any changes. In the end, the security and integrity of the information in question can be protected by using technology-based methods and tools.
