IBBusinessDPBusiness ManagementHLRevision Notes5. Operations Management5.9 Management Information SystemsData Management

Data Management (DP IB Business Management)

Revision Note

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Lisa Eades

Expertise

Business Content Creator

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

 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

  • 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

Exam Tip

Not all loyalty schemes are powered by technology

Businesses may reward customers with stamps each time they spend money with them, rewarding their loyalty with free or discounted products

However they are unable to access the volume of data possible with IT-based systems and, therefore, these forms of loyalty card should only be considered as a marketing tactic

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

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

Exam Tip

Consider how you would feel if your work were closely monitored through the use of technology

This is an excellent topic to include your own opinions and experiences - both positive and negative

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

Diagram: 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
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

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 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

 

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

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Lisa Eades

Author: Lisa Eades

Lisa has taught A Level, GCSE, BTEC and IBDP Business for over 20 years and is a senior Examiner for Edexcel. Lisa has been a successful Head of Department in Kent and has offered private Business tuition to students across the UK. Lisa loves to create imaginative and accessible resources which engage learners and build their passion for the subject.