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Importance of Data Management for Effective Decision-Making in the Restaurant Industry

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Introduction: Data-Driven Decision-Making Through Reliable Information Management

The data management process is important as it minimises potential errors and establishes policies to build trust for effective decision-making. It provides reliable and up-to-date data which helps an organisation to bring diversification as per customer needs and market change. It improves the process of risk and opportunity identification according to current trends which is necessary for optimising different business strategies. The Forest Side is a UK restaurant which has a revenue of approximately 8 billion euros. The data management process is important for the company to achieve customer satisfaction. In this report, the importance of data management has been elaborated. Recommendations have been given for an effective data management process. The limitations and benefits of the data management process have been defined to get the best outcomes. Recommendations have been given to reduce the risk of The Forest Side to improve productivity.

Contribution of effective data analytics and insight into business decision-making processes

Recommendation and justification for data management processes and systems for effective decision-making

Process of effective data access procedure

Figure 1: Process of effective data access procedure

Data Management process:

  1. Data collection: It is recommended that relevant data be identified and adhered to from different sources including sales data, customer feedback and social media. Market research and financial airports as well. It is justified as the process will help to gather data related to intangible and tangible assets (Gharaibeh et al. 2017). Following them, The Forest Side can update its menus and bring diversification with an effective price model and receive the benefits of stakeholder satisfaction.
  2. Data storage: After collecting data they should be stored in an accessible and secure location. This includes a data warehouse, secure database or password-encrypted cloud-based storage solution. It will be beneficial to store data securely and prevent them from legal consequences.
  3. Data processing: Data should be processed to organise them for performing statistical data representation. It should be applied to inform recent trends and cutler references to the management of The Forest Side. it will also help them to make personalised dishes from different continents.
  4. Data analysis: Data should be analysed to identify the correlation and key rands which will help in informed decision-making. A data visualisation tool can be used for managing the data which involves different software (Saiz and Rovira, 2020). It will help The Forest Side to include technological applications which are necessary for managing the business intelligence dashboards.
  5. Decision-making: It is the final step which will help The Forest Side in informed decision-making for product development, diversification, operation improvement and marketing campaigns as well.

Data management system:

  1. Data warehouse: It is recommended that a safe system for data processing and storing will be applied to support and design decision-making. It will provide an integrated and centralised view to store data on The Forest Side.
  2. Business intelligence tool: It will indicate software applications which will help in managing. It will help with visualisation which is required to generate the business report (Corti et al. 2019). The Forest Side needs to apply them to analyse and manipulate data for increasing product and cash flow for an extensive opportunity.
  3. Customer relationship management (CRM): It will help in managing customer relationships which are required for analysing customer preferences, customer behaviour and purchasing behaviour as well. It will help in data-driven decision-making related to sales, customer service and marketing.
  4. Enterprise resource planning (ERP): This is recommended to integrate different processes and functions within the company in a single system. The Forest Side can implement a centralised database for managing information.
  5. Predictive analysis: It is a set of techniques which helps in analysing historical data. It helps to predict future events following which operation management of The forest Side can be handled (Van et al. 2017). It helps in optimising business processes and forecasting demand for business profit.

Benefits and limitations of data management for decision-making

Reasons for applying the data management process

Figure 2: Reasons for applying the data management process


  • Improved data quality: The application of data management ensures data consistency and accuracy. It leads to better quality data which confirmed effective decision-making.
  • Enhanced data accessibility: Data becomes accessible and centralised which is required for informed decision-making (Dubovitskaya et al. 2017). It will recover data with fast management.
  • Increased data security: The data management system helps in managing data with sensitive information control and end-to-end encryption.
  • Better decision-making: It will help in accurate data management which provides insights to decision-makers including C-suite managers of the company.
  • Improved collaboration: Data management with technological inclusion helps in communication and collaboration. It fosters collaboration with different teams and thus helps to gather data from different sources (Raguseo, 2018). For instance, The Forest Side needs change in different operations which can be improved with collaborative measures.
  • Increased efficiency: It automates data management functions which help to reduce manual effort. It increases efficiency and leads to better business outcomes.


  • Quality of data: Data quality is an important measure of effective decision-making. Data may become inaccurate, incomplete or outdated will lead to an incorrect decision (Wang et al. 2017). So, it is important to ensure data accuracy and quality as well to mitigate the challenges.
  • Cost management: A technological software application may become expensive for a company. An SME may face financial resource issues to applying software for data management. An additional cost is associated with system maintenance, employee training and development and system update benefits. This can be solved with effective resource management tools and technological monitoring.
  • Integration: The software application of data management may not integrate with other applications and systems. It may lead to data silos and different difficulties. It can be solved by centralising data and managing information for the business development of The Forest Side.
  • Data security: The risk for cyber-attacks and data breaches may occur which increases the risk for data security (Martin et al. 2017). The Forest Side needs to protect sensitive information as it helps with adequate security measures.
  • Scalability: A data management system needs to be replaced and upgraded timely with increased data volume. It may lead to additional costs and implementation challenges. The Forest Side needs to take the ownership of knowledge management for better data operations.

Various data analysis methods and techniques that could inform business decisions

Use of data management processes and systems for effective decision-making

Data management processes help restaurant operations which will ensure product flow and reduce financial risk as well.

  1. Tracking sales: The data management system can track sales revenue records in real-time. It provides valuable knowledge and insights to the operation manager (Saggi and Jain, 2018). For example, they can track which menu is more popular and which is not. This will help The Forest Side to change its menus and shape price decisions.
  2. Inventory management: With the data management process inventory levels can be tracked. Thus, the restaurant manager can ensure that there are enough ingredients present or not. It can optimise order prices with faster delivery and reduce waste as well.
  3. Customer data management: A data management system will be able to identify and analyse customer preferences. It will help to define marketing efforts, service improvement and menu changes (Wamba et al. 2017). It will help to maintain a positive relationship with the customers which is required to track the purchasing behaviour of the customers and satisfy them by fulfilling their demands.
  4. Employee management: With a data management system, it can manage and track employee schedules which helps in performance management and talent management as well. Staffing levels can be tracked to identify training and development opportunities.
  5. Financial reporting: A data management process will help in publishing financial reports. It will help the company in revenue, profit margin and expense management. It will guide The Forest Side in informed decision-making and identify the teas for development.

It has been evaluated from the above that technological implementation helps in effective decision-making. It is required to collect customer data and arrange operational management as per demographics and purchasing behaviour. This information will help managers to better manage promotional activities, menu planning and inventory management as well. It will guide pricing strategy and product diversification as well (Zhang et al. 2017). By analysing sales data, The Forest side can define inventory management and seasonal also as well. It will help in agile resource management which is necessary to create a network and regain lost sales. Employee performance can be tracked to give them additional training. For instance, employees in hospitality management of The Forest Side, need to know menu engineering including different serving styles. By analysing employee performance with data management, the company can ensure better progress on the regional platform.

Appropriateness of selected data analysis methods for informed decision

Attributes of data analytics

Figure 3: Attributes of data analytics

Different techniques of data analysis:

  1. Descriptive analytics: It helps in analysing the historical data sets of the company including customer data and sales. The method is appropriate for identifying trends and purchasing behaviour (Stark, 2022). It will help The Forest Side in future decision-making. The method has been selected and marked appropriate for The Forest Side because it will help in managing future decisions which will prevent the company from business risk.
  2. Predictive analytics: Machine learning and statistical algorithms are used in this method where the decision is designed to predict and work according to future outcomes. It will help a company to identify potential opportunities and risks as well. Machine learning is an important application which will help The Forest Side to avoid business risk. For instance, ratio analysis after seasonal sales will guide the company in inventory management.
  3. Data visualisation: It involves a visual representation which will help in managing complex information. It is required to identify different patterns from raw data. It helps to progress in brainstorming which is necessary for managing business operations (Pagoropoulos et al. 2017). The data representation can be appropriate as it will help C-suite managers to maintain transparency among stakeholders. They will understand the change requirements and the areas for development as well.
  4. Business intelligence: Different software is used to analyse data in a user-friendly format. It provides real-time insights and knowledge to business managers which is required for effective decision-making. It will help The Forest Side to include AI for data management. The scheduler of delivery updates and employee management can be improved with the application of AI (Mendling et al. 2018). For example, the application of AI will help in creating a chatbot, it will respond to customer queries regarding table booking or pre-booking charges for any event. Thus, The Forest Side can achieve customer satisfaction with communication and collaboration strategies.
  5. Machine learning: In this technique, different algorithms are used to signify different patterns. It forms a relationship between tangible and intangible data which is essential for customer segmentation, product recommendation and fraud detection. Technological implementation is essential for the company to bring product diversification. For example. The customers prefer different continental dishes. Market research and development will help them to analyse the actual dish and help them to analyse customer purchasing behaviour.
  6. Stimulation modelling: The method involves creating different models which are required for stimulating real-world scenarios (Horváth and Szabó, 2019. Different strategies can be applied by The Forest Side to incense its potential. The step is necessary to eliminate business challenges by helping them with risk management scenarios.

Strengths and limitations of a range of data analysis methods and techniques for informing decision-making

Descriptive analytics


  1. The descriptive analysis provides insights into historical data which helps in analysing in organising company operation management. It will help The Forest Side to identify different trends, patterns and anomalies.
  2. It will help them to benchmark their performance with technological implementation. It needs to be improved with time (Wolfert et al. 2017).
  3. It provides a baseline for other types of software analytics including prescriptive analytics and predictive analytics.


  1. It is limited to historical data which may not reflect effective information to follow future trends (Buhalisand Sinarta, 2019).
  2. It may not identify the root cause of the problem as it is unable to define the causal relationship between two variables except for correlations.

Predictive analytics


  1. It improves the accuracy of future events which contributes to better decisions.
  2. It will increase competitive advantage through the data from predictive analytics. It will help to anticipate customer needs.
  3. It is a cost-saving method which will reduce waste and improve efficiency.


  1. It heavily depends on data quality and contributes to the completeness of the model.
  2. It will help to maintain ethical standards within the business which will ensure that decisions taken by the management are unbiased.

Data visualisation


  1. It makes data understandable and helps in the brainstorming process (Tao et al. 2019).
  2. It enhances communication and collaboration with stakeholders including employees and team members which is necessary for business decision-making.
  3. It helps in generating hypotheses and exploring data for informed decision-making.


  1. It can mislead business operation management if designed poorly.
  2. It may become time-consuming to create a detailed visualisation with specialised tools and skills

Business intelligence


  1. It can provide timely and accurate business information which is necessary to manage the revenue stream of the company.
  2. It will contribute to better performance of the company which is required to signify and resolve bruises issues.
  3. It helps to enhance the efficiency of the company and save employees from repetitive manual tasks.


  1. It may limit the scope as it primarily focuses on structured data from company internal sources. It does not rely on or princess unstructured data and may miss out on further opportunities.
  2. Business intelligence solutions may become expensive, especially for SMEs. They may not have the appropriate resources to implement technology and AI in all possible fields.

Machine learning


  1. It will help to achieve business efficiency with different machine learning models.
  2. It will promote automation within the company which reduces the need for manual intervention.


  1. Sometimes, the model of machine learning implementation can increase complexity which leads to poor performance.
  2. It may have issues with interpretability which affects decision-making.

Stimulation modelling


  1. It has a predictive ability which is necessary for working with external and internal information (Ivars et al. 2019).
  2. It provides flexibility with a wide range of experimentation and observation.


  1. It needs a significant amount of information to process data management.
  2. It may raise uncertainty by predicting market trends and analysis for a business (Ivars et al. 2019).

How different approaches to data analysis influence decision-making and an organisation’s ability to strategic objectives

The choice of decision for selecting an approach depends on the specific needs of the company. Skilled data analysis performance helps a company to achieve success within the company. For instance, The Forest Side is suffering from the issues of financial risk and challenges with operational management. It is reducing their productivity and profit margin as well (Tupa et al. 2017). The implementation of data analytics will help them to manage external and internal information. It is important to select and analyse information for further data management. The approaches can be taken by the company to conduct detailed market research and analysis. For example, The Forest Side increase its operational efficiency with a technical data management system to improve revenue streams and menu engineering. The strategies can be taken by collaborating with the stakeholders of the company. According to Pappas et al. (2018), a strategic approach can be designed with technological implementation and bringing automation as it helps in effective decision-making. A descriptive analysis approach can be taken by The Forest Side to implement statistical techniques and improve future trends.

Steps of data analytics to resolve consequences

Figure 4: Steps of data analytics to resolve consequences

The Forest Side may take a predictive or descriptive analytics approach to improve its decision-making am operational efficiency with a strategic approach. Descriptive analytics will help to understand historic sales, customer preferences and their purchasing behaviour. Predictive analysis will help to forecast future trends which will help in decision-making. A qualitative or quantitative data analysis approach can be applied by the company to manage its performance following the market trends. Qualitative data will interpret and analyse non-numerical data from customer reviews and feedback (Pappas et al. 2018). It will help them better understand what is important for a company for data-driven decision-making. On the other hand, quantitative data can be collected from customer surveys. It will help them to clarify their financial audits and revenue streams for better management. Exploratory and confirmatory data analysis approaches can be taken by The Forest Side to find a definite pattern of change. An exploratory approach will help to analyse patterns and trends of customer choice. On the other hand, the confirmatory data analysis approach will help the restaurant to test specific analyses which is necessary for informed decision-making.

It has been evaluated from the above that different approaches can be applied for business improvement. Technological implementation is necessary to increase innovation within the company. Stakeholder analysis and collaboration will help in the manual process of decision-making. On the other hand, technology will promote data-driven decision-making which will lead them to reduce different risks of The Forest Side.

Importance of ethics and conduct in data analytics and management

Examples of effective or poor ethical behaviours and conduct in data management and potential consequences

Effective ethical behaviours and conduct in data management

Effective ethical behaviour includes respect for the privacy of consumers. The confidentiality of the consumers should be maintained by The Forest Side. Data should be kept confidential and stirred in a password-protected cloud environment. It should be protected from any unauthorised disclosure or access with the application of effect security measles. It will protect the company from cyber hacks and cybersecurity risks (Habeeb et al. 2019). Access control and data encryption should be implemented to safeguard data. On the other hand, informed consent is another example of data management conduct ethically. Permission from the consumer should be taken before collecting and storing their preferences.

Poor ethical behaviours and conduct in data management

A data breach is an example of poor data management and it reflects unethical behaviour. Data breaching refers to leaking the personal information of customers who used the services of a company (Talesh, 2018). For example, if The Forest Side is unable to secure data adequately, it will increase the risk of unauthorised access and cyber hacking. It will lead to comprising and breaching of the personal information of stakeholders. In terms of achieving the desired outcomes, if the data is manipulated it will be unethical.


The consequences will lead them to a negative and unethical organisational environment. It will give rise to stakeholder dissatisfaction and increase internal conflict. The company may face legal consequences as well for unethical conduct (Hariri et al. 2019). It includes fines, damage to organisational reputation and lawsuits. The company will face the challenges of financial losses. It will affect the tangible and intangible assets of the company.

Legal and business consequences of unethical data analytics and management

Lawsuits are e major legal consequences which will be faced by the company. Stakeholders or consumers who are harmed may file legal cases against the company. They may demand criminal charges against the company which will affect the assets and reputation of the organisation. Organisations which will violate data privacy may face the consequences of significant fines. For instance, according to “The European Union’s "General Data Protection Regulation” (GDPR), they can be fined up to 20 million euros for serious violations of data and conducting data breaches. “Data Protection Act 2018” reflects the law of data collecting, analysing and storing which provides additional information for restricting data breaches. The company or person assimilated with unethical data management may have to give a penalty and serve imprisonment. “Freedom to Information Act 2000” gives the right to access information held by local councils and government agencies in the UK.


data mismanagement will lead to the loss of trust among stakeholders. It will reduce the retention rate of the company and the company will face financial and reputational challenges in the regional and global platform. Unethical data analytics reflects that customer data has been misused by the company which will give rise to public outcry and damage the brand image (Bag et al. 2020). Due to unethical data practices, the company may have to pay significant penalties. Loss of trust among stakeholders signifies unethical behaviour.

Data management processes that allow for improved decision making in everchanging business environments

Appropriate data management processes to improve decision-making

Data management practices should be implemented by the company for effective decision-making and to eliminate different challenges. An effective process is required to reap the befits. The process has been signified The Forest Side manages data collection and data storing as well.

  1. Identifying the business goals: The business goals should be identified after reviewing the data. Market research and development will help the company to identify the goals and resources to complete them. Communication and collaboration with stakeholders will help them in measuring gaols. The approach is important to collect, store, manage, clean and analyse the data. It will help them to implement data analytics steps and software with improved data security.
  2. Focusing on data quality: A data management system should provide reliable data s they can princess their decision-making. The Forest Side is facing financial risk and issues with operational management. Hence, they need to process their decision in this sector to improve the cash flow of the company. After creating goals, data accuracy should be reviewed. Qualitative and quantitative data are important for managing effective decision-making. The Forest Side should review sales data and customer data to reduce challenges in financial management and improve irrational efficiency as well.
  3. Data access: Legal and business ethics should be maintained by the company to manage its ethical standards. It will prevent them from legal and business consequences. IT teams should be trained in effective data analytics and data management prices. It will help them to manage data security and improve operational efficiency with a definite and ethical process. Besides, the data should not be manipulated at any terms to get the desired outcomes.
  4. Applying the right technology: The company should select an ultimate process to choose the right technology. Effective data management princess should be implemented to improve reliability, accessibility and transparency within the company (Roh et al. 2019). It will help to streamline the process and contribute to data governance.
  5. Establishment of data governance: Data infrastructure should be implemented to get the best benefits from the prices. It will help the to manage their performance ethically. There are four perspectives of effective data governance. They are as follows:
  • Data quality: The Forest Side should ensure that the data is complete, current and accurate which will lead them towards a better data analytics and data management process.
  • Data security: Data security should be minted for ethical business outcomes and avoid legal consequences.
  • Data privacy: Permission should be taken from the consumers before collecting and string data to improve privacy measures. It should be stored in a protected environment to avoid the consequences of hacking.
  • Data transparency: An ethical data management environment should be promised by The Forest Side and the report should be shared with relevant shoulders to manage transparency with them.

Tactical data management processes for organisational strategic decisions and objectives

The Forest Side must ensure data management and technological application by managing transparency and accountability. It will help them to meet their organisational goals and improve the financial assets and reputation of the company as well. Effective data management princess to maintain accountability and transparency include:

  1. Collecting and analysing data: The data should be collected from internal sources to assess company performance. It includes inventory data management. Sales data, employee performance, data from 360-degree feedback and customer feedback and reviews. The data can be used to identify appropriate processes and make informed decision-making in an agile method. Different software and cloud storage can be used for data collection and data string as well.
  2. Implementing POS system: A point of sales system (POS) system should be implemented to track inventory management and sales. It will help to track sales data depending on which the company may change its operational management and pricing strategies as well.
  3. Developing key performance indicators (KPI): KPIs are the metrics that will help to analyse the financial and non-financial health of the company. Following different metrics the company may improve their operational strategies and customer management. It will help them to reduce error rates and improve the operational process as well. Different KPIs include sales per square foot, employee turnover rate and customer satisfaction rate. A regular assessment of KPI metrics is required to assess their performance and manage their success on the regional platform.
  4. Training and development: It is required to gain employees for technological application and data management. It will help them to participate in the appropriate data management process that has been mentioned above. It will be assessed how their performance is affecting workflow and the effectiveness of data-driven decisions (Roh et al. 2019). It will help the employers and leaders to assess the challenges they are facing to meet organisational goals. Performance management, talent management and reward management programs are required for The Forest Side to improve colouration with employees, who are one of the major stakeholders of the company.
  5. Improving communication: Communication and collaboration with stakeholders should be improved for managing the goals of the company. Technological implementation will enrich their productivity, cash flow and brand image. Different communication tools can be used by the management including websites, a messaging application, emails and video conferencing. It will facilitate communication among stakeholders including external and internal which is necessary for a tactical decision-making approach with data analytics.
  6. Create reports: The company should analyse data and create reports which should be shared with relevant stakeholders. It will help them to manage a transparent relationship with stakeholders. They can monitor the performance of the employees in a periodical gap. The reports can be published online where the stakeholders can access data as per requirement.


It has been concluded from the above that data management is important for a company to ensure an effective business process. Data management and data analytics are important for a company to improve their performance on the regional platform. Data management with technological solutions has been recommended to The Forest Side to reduce their financial risk and improve effective organisational performance. It will help in managing Customer Relationship Management (CRM). There are different approaches to data management which help a company. Business and legal consequences which can occur due to data management applications will be discussed with employers. The tactical data management process helps in the strategic data management process which will help in receiving customer data and managing them for further development and profit.


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