Information Technology-Based Data Management in Retail


Data management has become an important part of organisational management. When appropriately integrated into the company’s business environment, IT-based data management offers a wide range of advantages in operations, marketing, HR, and finance. At the same time, irresponsible handling of data creates a number of major ethical considerations. The following paper discusses the specificities of data management and identifies the most apparent ethical considerations using retail as an example.

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Data Management


The operations aspect of retail involves a considerable number of individuals. The most active participants are employees, management, and suppliers. The suppliers typically submit data by inbound shipment of goods. The information may include the details of delivery, quantity, and cost of goods received at the warehouse. After this, the responsibility for data management is passed to the employees, who, depending on the type of technology used in the company, dispatch the necessary amounts of goods to respective departments or report to the management on the delivery. The data on goods relocated to the sales department is collected automatically. At the same time, data on employee performance is handled by the line manager. Finally, the management submits the data to the finance and marketing departments.

The main process associated with data management in retail is based on the management of goods. Upon arrival at the warehouse, goods are marked in the system as available. After this, the sales department is notified of the change in a surplus and can request the necessary items. The sales data is gathered automatically during the process and compiled into a set that can be retrieved by the marketing department for analytical purposes (Fernie & Sparks 2014). The system also tracks important variables such as expiration dates of goods in order to allow for more efficient management of resources. The inconsistencies in supply (e.g., an unforeseen shortage of items determined individually for each department) are tracked and sent to managers responsible for interactions with suppliers.

The majority of the described processes are performed using enterprise software solutions. The solutions in question are purchased as ready-made options or configured in accordance with specificities of retail operations. The platform can be run internally or hosted on the cloud. Some of the data (e.g., inbound shipments from suppliers with no compatible equipment) is submitted to the system manually, whereas the bulk is recorded automatically with the help of cross-compatible formats (Fernie & Sparks 2014). In addition, the technology in question is capable of disaggregating the data on consumer behaviour and employee performance to adjust the existing strategies and techniques.


The financial department involves two main groups of stakeholders. The first group includes employees that submit data to the financial department. Importantly, while the bulk of data is generated by their actions, only a fraction is collected and entered manually. The second group includes the members of the accounting department who gather, analyse and interpret data.

The processes relevant to financial data management include all actions that generate expenditures or profits, such as sales data, inbound shipments, marketing expenses, and operating expenses, among others (Einav & Levin 2014). The data from different sources is arranged to allow for its seamless processing. Once all necessary data for a given period is obtained, it is compiled into balance sheets and verified for accuracy and integrity.

At this point, the need may arise to identify new relevant directions for analysis or locate and eliminate redundant ones in order to optimise performance. The data is then processed using the tools available from the enterprise solution and reviewed to identify the most important trends, challenges, and advantages. The results are compiled in a meaningful format with the help of visual aids and presented to the management. Finally, the data that requires disclosure is presented in the form of a publicly available report or submitted to auditors. The majority of the described tasks are done with the help of statistical tools integrated into the enterprise solution. In certain cases, additional instruments can be used that support the format used in the organisation.

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The first main stakeholder involved in the marketing data management process is the customers. On the one hand, they serve as a primary source of data on consumer behaviour patterns, which can be used to develop or adjust marketing strategies and tactics. On the other hand, they comprise the main target of these strategies. The second group of people relevant to the process is employees of the marketing department who select tools to gather data, oversee the collection procedure, interpret the results, and issue recommendations on necessary changes.

The data collection process is facilitated through two main channels. First, sales data available through an enterprise management system is submitted to the marketing department. This data is disaggregated, which allows identifying various segments within the target audience and thus achieving necessary diversification of solutions. The second source of data includes dedicated tools intended for a direct inquiry. These tools include surveys and questionnaires. These tools either generate data in the digital format or require a conversion of the dataset after the completion of the research (Gandomi & Haider 2015). Evidently, the second method allows for a broader range of results to be obtained. In addition, the specificity of surveys ensures the relevance of the collected information. On the other hand, sales data is a more cost-effective method.

The tools for data collection include online survey services, statistical tools for qualitative and quantitative analysis, and enterprise management systems capable of collecting and submitting sales data. The bulk of data handling is automated, with minor exceptions such as manual input of qualitative data into respective analytical software. Once the necessary data is gathered, it is processed by statistical tools in order to identify behaviour patterns responsible for customer satisfaction rate. This information is submitted to the organisation’s managerial department and integrated into corporate decisions.

Human Resource Management

The main stakeholders of the human resource management process are the organisation’s employees and the HRM department. The second group is responsible for the appropriate application of talent within the organisation and the maximisation of employee potential. In order to achieve these goals, it is necessary to collect relevant data, identify necessary variables, and determine the preferred course of actions based on the results.

The HR-related data is collected via a wide array of tools. The most readily recognised ones are KPIs – metrics that are determined to be relevant to the success of employee performance. Depending on the type of the company and the specificities of the business environment, different combinations of KPIs can be identified, including average customer spend, sales per square foot, and gross margin, among others (Stone et al., 2015). The obtained KPIs are logged and tracked using a dedicated solution or the functions of the enterprise management system. The latter allows for seamless, automated handling of data obtained from employee activities. In addition, a KPI dashboard can be utilised, which organises the most relevant KPIs and findings and time indicates the emergence of issues.

Data Integrity

The data collected for business purposes need to comply with the criteria of timeliness, completeness, and accuracy. Compromising the data in any of the given areas may lead to a number of adverse issues. The most apparent undesirable effects are related to a decrease in performance and, by extension, the profitability of a business. A good example would be the data on employee satisfaction rate during the introduction of a new strategic approach. In this case, data completeness depends on the relevance of metrics used to determine the response of the employees to changes. This parameter can be ensured by identifying correlations in the available data and including the most relevant ones in the analysis.

Next, the accuracy of data depends on the appropriateness of data collection tools and the quality of the performed analysis. The former can be ensured by developing a tool that is suitable for the processor using one of the ready-made solutions known to be compatible with the goals of the evaluation. The latter is achieved by eliminating the possibility of human error during data input, analysis, and interpretation. The bulk of issues can be eliminated by automating the data collection and analysis via enterprise solutions (Martin, Borah & Palmatier 2017). Finally, the timeliness of the data is ensured by the computational capacity of the IT-based solutions. With the exception of big data management, analytical software is capable of generating results on the fly, increasing responsiveness to different factors.

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It is also important to recognise the legal implications of data integrity. The accuracy of financial data submitted for audit by independent organisations depends in part on the absence of honest errors. In this case, appropriately configured accounting software can ensure the consistency of results and minimise the risk of reporting flawed data, thus maintaining the value of the company’s shares on the stock market.

Ethical Perspective

Finally, it is necessary to recognise the ethical aspect of data management. In most cases, data necessary for marketing analysis contains sensitive information, which raises several major concerns. First, it is possible that the data in question is used for purposes that harm the party that submitted it. For instance, it would be trivial for a vendor to use the available contact information to deliver targeted advertisement without obtaining the consent of the owner, which is an apparent violation of personal privacy. It is also possible to imagine a scenario where the dataset collected for statistical research becomes available to a third party. This may occur as a result of an unintentional flaw in the system’s security or as a result of a deliberate attack. For instance, criminals may gain unlawful access to a vendor’s database of its customers’ financial credentials, putting all of their funds at risk. Alternatively, the data can leak to an external party as a result of insider activities (Hemphill & Longstreet 2016). Finally, an organisation may be tempted to sell the data to someone despite the absence of permission for such actions.

Several security measures can be identified that allow minimising these risks. First, the data in question needs to be encrypted using the algorithms and tools compliant with the industry safety standards. Second, the handling of data should require a confirmation from at least one trusted party in order to ensure the integrity of the actions. Third, the data should be depersonalised by removing sensitive demographic information that would pose the risk of disclosure. Admittedly this approach is applicable only to the cases where the removed information is irrelevant for the results of the analysis. Finally, and, perhaps, most importantly, the information needs to be disposed of appropriately after the desired results are obtained, which would prevent subsequent leaks.

As can be seen, all of the approaches require the allocation of time and resources. For instance, the processing and storage of encrypted data require additional expenses for familiarising the employees with the technology. Appropriate storage of data also necessitates dedicated software and hardware. At the same time, the conflict of interests introduces certain regulatory requirements to the process of data handling. Understandably, the organisation may be tempted to compromise their ethics and thus increase the profitability of a business. Nevertheless, in the long run, the existence of a transparent and robust data management system will improve the accuracy, timeliness, and completeness of the information while at the same time minimise the opportunity of a breach.


As can be seen, the applications of information technology to data management in retail are numerous. The current capabilities of enterprise-scale solutions create offer a number of improvements in the sales process, facilitate seamless data collection and submission, and help to timely locate and address barriers and shortcomings. In addition, they contribute to the accuracy of financial information. Next, these solutions can be used to identify emergent customer behaviour patterns, integrate the findings into the company’s strategy, and monitor the feasibility of changes. Finally, the integrity and safety of the data in question can be secured using IT-based tools and approaches.

Reference List

Einav, L & Levin, J 2014, ‘The data revolution and economic analysis’, Innovation Policy and the Economy, vol. 14, no. 1, pp. 1-24.

Fernie, J & Sparks, L 2014, Logistics and retail management: emerging issues and new challenges in the retail supply chain, Kogan Page Publishers, Philadelphia, PA.

Gandomi, A & Haider, M 2015, ‘Beyond the hype: big data concepts, methods, and analytics’, International Journal of Information Management, vol. 35, no. 2, pp. 137-144.

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Hemphill, TA & Longstreet, P 2016, ‘Financial data breaches in the US retail economy: restoring confidence in information technology security standards’, Technology in Society, vol. 44, pp. 30-38.

Martin, KD, Borah, A & Palmatier, RW 2017, ‘Data privacy: effects on customer and firm performance’, Journal of Marketing, vol. 81, no. 1, pp. 36-58.

Stone, DL, Deadrick, DL, Lukaszewski, KM & Johnson, R 2015, ‘The influence of technology on the future of human resource management’, Human Resource Management Review, vol. 25, no. 2, pp. 216-231.

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