Data Management (HL IB Business Management)

• Big data refers to large volumes of data that inundate businesses on a day-to-day basis
• Businesses have more opportunities than ever before to gather vast amounts of data
• Big data can be used to understand customers better, make informed strategic decisions and offer more personalised services that meet customer needs

 New Ways Businesses Collect Big Data

E-commerce	Social media
Collection of data related to online purchases, search variables and purchase preferences	Interactions on platforms such as Facebook and Instagram can be analysed to identify trends, complaints and product popularity
Internet of Things (IoT)	Logistics
Data is gathered automatically from devices including smartphones, apps and smart appliances	Data gathered from location tracking tools can improve transportation and delivery processes

• Customer loyalty programmes are a way to gather large amounts of data on spending habits and behaviour of customers
  
  • Financial and transactional data includes information about payment methods and details of products purchased 
  • Interaction data relates to how customers engage with surveys, feedback on in-store and online shopping experiences and CCTV data such as queue monitoring or number plate recognition
  • Marketing data includes customer interaction with online marketing such as the opening of marketing emails and interaction with adverts while browsing the internet
     
• In return for allowing access to this amount of data loyalty schemes often offer discounts or reward points that can be very attractive to customers
  • Customers feel connected to a business that rewards them and are more likely to remain loyal over time
  • Loyal customers frequently recommend the business to others and provide meaningful feedback
  • Promotional costs may be reduced as there is less of an urgent need to attract new customers
     

• Loyalty schemes help a business to differentiate itself from rivals and allow for greater personalisation of promotional activity 
   
• Loyalty programs have several drawbacks
  • Operating loyalty schemes can be expensive - especially for businesses with limited resources
  • Customers may come to expect discounts which could devalue a businesses products 
  • Customers may be disinterested by too many loyalty programs
  • Storing customer data for loyalty programs raises concerns about privacy and data security

• Digital Taylorism involves using technology to carefully monitor workers' use of the tools and techniques for completing their work tasks
  • In 2022, 80% of large US corporations in the United States had their employees under regular surveillance
    
  • Examples include Amazon, FedEx and Deliveroo
    
    
• Pay and other financial rewards are linked to achieving performance targets
  
  • In some cases workers may receive sanctions based on data collected automatically
    • In 2020 Amazon workers complained of facing disciplinary action for taking toilet breaks during their shifts
      
      
• Technological innovations have made it much easier for managers to quickly and cheaply collect, process, evaluate and act upon vast amounts of employee performance information
  • In logistics computer systems control vehicle fleets and employees
    • Sensors track location, timing, driving and other aspects of performance
    • Complex algorithms and analytics software instruct truck drivers which routes to take as well as expected schedules
      
  • In retail employee performance data can be gathered from programs running in the background of the computerised cash register
    • Keystrokes can be logged, audio/video can be recorded and time taken to serve customers can be continuously collected
       

Benefits of Using data to Monitor Employee Performance

Benefit	Explanation
Coordination & control	Data can ensure that the right number of employees are available when needed Poor performance can be identified quickly and acted upon by managers
Training & staff development	Data can identify skills gaps and training needs for employees Data such as recorded customer interactions can be used as real-life training workshop materials
Employee engagement & rewards	Regular data-based feedback improves communication between managers and employees  Data can identify high-performing employees for rewards, boosting morale
Less management time required	Systems that automatically collect data can reduce the amount of time managers need to directly oversee the work of subordinates

Data Mining

• Data mining occurs when raw data is extracted from large data sets and converted into useful information
• This information is used to make data-driven decisions that reduce risk and help a business to increase revenue, reduce costs and improve customer relations
  
  

Diagram with the Common Uses of Data Mining

Data Mining can be used by a large retail business to plan marketing and production, identify purchasing patterns and profile customers

• Marketing Planning 
  • Identify successful marketing strategies
  • Determine market segments
    
    
• Sales Forecasting
  • Identify sales trends
  • Set revenue budgets based on past performance
    
    
• Consumer Profiling
  • Connect purchasing habits with demographic data
  • Target promotions that appeal to specific groups of customers
    
    
• Personalising loyalty rewards
  • Compare success of previous loyalty rewards 
  • Target rewards that appeal to specific groups of customers
    
    
• Market research
  • Predict future customer preferences based on past consumption
    
    
• Identifying purchasing patterns
  • Compare products bought at particular locations, times and combinations with other goods
  • Tailor product availability
    
    
• Research & Development
  • Allocate future spending on R&D based on extrapolation of past trends
    
    
• Production Planning
  • Identify supply chain disruptions
  • Prioritise availability of products based on past demand
     

Criticisms of Data Mining

Criticism	Explanation
Invasion of privacy	Large-scale collection and analysis of personal data can make individuals feel uncomfortable or violated when this information is used without their explicit consent
Data breaches	Storing large amounts of data increases the risk of security breaches where sensitive information such as banking or health details may be made public
Discrimination	Decision-making based on mined data may unintentionally discriminate against certain groups which could worsen social and economic disparities between, for example, men and women

• Technology has had a significant impact on business decision-making and stakeholders in recent years
  • Technology provides tools for data analysis which improves efficiency and communication
  • Innovation is driven by technological advances and provides a competitive advantage
  • Employees may benefit from these advancements through improved workplace experiences
    
    

Positive Impacts of Technology on Business Decision Making and Stakeholders

Impact	Explanation
Data-driven decision making	Vast amounts of data can be collected, processed and analysed This allows businesses to make informed decisions based on real-time insights and trends This leads to improved business performance and increased financial rewards
Efficiency & productivity	Automation and technology tools enhance operational efficiency and productivity This improves decision-making by streamlining processes and reducing human error Customers experience improved services and faster delivery times
Communication & collaboration	The use of video conferencing, collaboration software and messaging apps can speed up decision making
Customer experience	Automated tools such as chatbots and personalised marketing tactics made possible through data mining improve customer interactions Monitoring customer experiences means training can be tailored for employees
Innovation & adaptability	Technology provides tools for research and development Businesses can adapt to changing market conditions quickly as trends can be identified in real-time
Supply chain management	Technologies such as the Internet of Things have transformed supply chain management Businesses can track goods in real-time which improves the more efficiency of supply chains

 

Legal, ethical and practical concerns

Data Security & Privacy	Ethical Use of Data	Employee Training & Adaptation
Data breaches can lead to unauthorised access to sensitive information which could result in financial and reputational damage Business must follow strict data protection regulations such as GDPR and CCPA	Algorithms and AI-based systems can inherit biases which can lead to discrimination Lack of transparency in how businesses use customer data can damage trust	Rapid technological advancements may result in skill gaps among employees who may need training to keep up with the latest tools  Resistance to Change amongst employees may affect the introduction of digital processes
Data Quality and Accuracy	Dependency & Reliability	Environmental Impact
Garbage In, Garbage Out (GIGO) means that inaccurate or low-quality data can lead to poor decisions Ensuring data is accurate and reliable is crucial for making informed business decisions	System failures or technical glitches can disrupt business operations Businesses that use third-party services and platforms may struggle if critical services are no longer available	The growing use of technology - especially data centres - increases energy consumption which raises concerns about environmental sustainability Disposal of equipment is environmentally problematic - especially in poorer countries