Data Management (DP IB Business Management): Revision Note

Big data

• 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

Ways businesses collect big data

Customer loyalty programmes

• 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

• 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

Using data to make decisions

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

Common uses of data mining

• 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

• Invasion of privacy

  • Large-scale collection and analysis of personal data can make individuals feel uncomfortable or violated

  • This is especially concerning when the information is used without their clear or explicit consent

• Data breaches

  • Storing large amounts of data increases the risk of security breaches

  • Sensitive information, such as banking or health details, could be exposed or made public

• Discrimination

  • Decision-making based on mined data may unintentionally discriminate against certain groups

  • This can increase social and economic inequality, for example, between men and women

Evaluating the impact of technology on decision making and stakeholders

• 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 decision making and stakeholders

Legal, ethical and practical concerns

• Data security and privacy

  • Data breaches can lead to unauthorised access to sensitive information, causing financial and reputational damage

  • Businesses must follow strict data protection laws such as GDPR and CCPA to keep personal data safe

• Ethical use of data

  • Algorithms and AI systems can carry hidden biases, which may result in discrimination

  • A lack of transparency about how customer data is used can reduce trust in the business

• Employee training and adaptation

  • Rapid tech changes can create skill gaps, meaning employees may need extra training

  • Resistance to change among staff can slow down the introduction of new digital systems

• Data quality and accuracy

  • "Garbage In, Garbage Out" means that poor-quality or inaccurate data can lead to bad decisions

  • Ensuring data is accurate and reliable is essential for making smart business choices

• Dependency and reliability

  • Technical issues or system failures can disrupt business operations

  • Relying on third-party platforms can cause problems if key services become unavailable

• Environmental impact

  • The rise in tech use, especially data centres, leads to high energy use and raises sustainability concerns

  • Disposing of old tech equipment can harm the environment, particularly in low-income countries