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.

Introduction

  • “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

Sectors

  • 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):

Advice

  • 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

Issues

  • 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

Resources

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