How Data Works in Businesses

Mohamed Zaki of the Cambridge Service Alliance spoke to us about monetization of data in the business world. These notes are based on his talk.


  • “Data is the new oil”
  • Question is how to monetize data / capture value of data
  • Two distinct venues where data and big data can have an impact:
    • Optimization of existing services
    • Data-driven business models

Optimization of Existing Services


  • Education:
    • Competency-based learning
    • Personalized learning adapted to learning style
    • Student as a customer!
  • Asset heavy industry:
    • Sensors to monitor machinery to predict and warn of failure early
  • Defense:
    • Availability / outcome-based contracts: Contracted to keep machinery / planes available for an amount of time
    • Sensors are used to collect data and monitor and predict availability
  • Service offerings:
    • Monetization through service contracts

Barriers to Using Data

  • Complex service networks (CSNs) describe intricate customer structures

Internal barriers (cultural, people issues):


  • Political work likely needs to happen first
  • You will likely need support of top management:
    • You need someone at the top that shares and supports your data-driven vision, that cares about the strategy, your values, and that understands revenue potential
  • Interpret your data-driven approach as a strategy:
    • ‘Me too’: We want to catch up with competitors when it comes to data-driven decisions / defensive strategy
    • Innovation: We want to innovate in the sphere of data-driven decisions
    • Branding of your data-driven approach is important when communicating it within your company
  • Incentivize close collaborative work between departments and yourself
    • Important due to siloed data
  • Work collaboratively
    • Collect feedback about the usefulness of your models
    • Iterate over modelling execution and feedback
  • Share best practices across your company
  • Generally, data scientists cannot work by themselves, need a cross functional team, and both soft and political skills to convince others of the value of their data-driven approaches


  • Data accessibility: Possible legislative barriers to data transmission
    • Saudi Arabia disallow transmission via satellites
  • Data governance, quality, and integration
  • Data ownership and sharing
    • E.g. data collected by sensors is not usually regarded as an asset when selling machinery (but ownership becomes a problem later)
  • Sensitive data: Oftentimes this cannot be shared and the expert working on the data needs to be brought into company to work there directly
  • Data is often locked up in silos: Marketing, sales etc. all have their own data
  • Performance measurement: Most CSNs stop as soon as their customer is happy without pursuing optimization, performance evaluation

External Issues

  • You need to demonstrate what value your data-driven approach adds to everyone in the network

Data-Driven Business Model (DDBM)

  • Data source is important
  • Key activity of business: aggregation, modeling, prediction, etc.
  • Offering of the business: data and knowledge
  • Target customers: B2B, B2C
  • Revenue model:
    • asset sale / rent / lease (asset is data and/or knowledge)
    • advertise
  • Study: A Taxonomy of Data-Driven Business Models


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