• Initially described any internet enabled transactions

  • However rapid growth and adoption has lead to it also encompassing M-commerce, which represents transactions made on mobile devices, typically utilising the same technologies as eCommerce

  • Advantages

    • Cost-effective
      • E-commerce is very cheap compared to running a shop, you don’t need as many staff and you’re never closed.
    • Increased Demand
      • You are not restricted to a set geographical area as people from anywhere in the world can view your store and make purchases
  • Disadvantages

    • Greater competition
      • Because of the ease of entry into eCommerce, there are many competing companies and existing firms may also transition to an online market. This can make it very hard to have a USP.
    • No tactile experience
      • Customers aren’t able to feel or physically see any products, and for things like clothing this can greatly increase the number of returns that have to be made. This can lead to reduced customer satisfaction and increased costs in terms of returns processing.

Big Data

  • The rapid growth of the use and analysis of data by businesses is often expressed in terms of its high volume, velocity and variety

  • Working particularly well in tandem with technologies such as eCommerce, big data enables the collection of vast quantities of data about a user enabling extremely targeted marketing as well as research.

  • Advantages

    • Improved decision making
      • Businesses can process huge quantities of data to make much more informed decisions, reducing the need for guesswork and the risk associated with decision making.
    • Improved security
      • Businesses can improve their security by building profiles of customers, detecting anomalies and identifying potential threats before they become a problem. For instance, if an employee was stealing stock, the use of big data would likely be instrumental in identifying the offender.
  • Disadvantages

    • Invasion of privacy
      • Many customers and employees will not be happy with having massive amounts of data compiled on them and used for obscure business purposes. People may wish to opt out or see how their data is used.
    • Security Risk
      • Data is a valuable target for a hacker, so if an organisation stores a huge quantity then they are also a target. This means that if data is breached then information about customers and employees may be published or stolen, seriously harming the business reputation.

Data mining

  • Much of data mining is based on statistical techniques.

  • It is the process of analysing a large dataset and performing an operation to produce a new dataset, usually with the goal of discovering new insights.

  • Advantages

    • Effectiveness
      • Data mining is highly effective when there is enough accurate data to handle, and when it is handled properly. It can help businesses to improve their decision making techniques.
    • Speed
      • Data mining can be automated and work in real time, enabling a business to make decisions based on data from now rather than from last quarter.
  • Disadvantages

    • Fallible
      • If the large dataset has questionable accuracy or consistency, then the insights produced through data mining may be skewed and provide incorrect data.
    • Open to interpretation
      • Data can always be interpreted in different ways, meaning that no matter how accurate data is it is still vulnerable to humans making mistakes.

Enterprise Resource Planning (ERP)

  • ERP typically refers to the core business processes that tend to be automated.

  • Operates through business software

  • Advantages

    • Reduced labour costs
      • Menial administrative tasks can be automated by a computer rather than performed by a person, leading to greater accuracy and lower costs.
    • Closer scrutiny of business activities
      • If everything is tracked by ERP, then discrepancies or issues can be identified far faster and with much greater accuracy.
  • Disadvantages

    • Not human
      • ERP systems function on a set of basic, set rules. If a situation requires a more nuanced response, ERP systems will likely not perform well. A human may be needed to handle some edge cases.
    • Limited
      • Only more basic business operations can be automated with ERP as it requires predefined algorithms.
  • Examples include:

    • Access
    • Odoo
    • StoreIQ

Key Adoption Pressures

  • Serve existing customers better
  • Reach new customers in new segments and locations
  • Offer new ways of delivering products and services using digital technology
  • Reduce costs by integrating digital technology into operations
  • The need to respond to digital innovation by competitors
  • Access, analyse and action data that provides key insights into customer needs and business performance

Key Impacts of Data Mining


  • The ability to discover insights about customers and produce targeted advertising campaigns with higher success rates than traditional marketing.


  • Provide a much closer insight into employee performance, reducing the required work for HR to produce equal or better quality information to use in HR decisions.


  • Much easier to improve efficiency as data mining can be used to sift through vast data-lakes and identify small inefficiencies in operational processes.


  • As finance is largely numerical, it makes it much simpler to identify and analyse trends, potential patterns and gain insight than in more qualitative areas. Because of this, data mining can be used to flag potential issues to finance personnel. Machine learning could be utilised heavily.