Organisational Data Science

Neil D. Lawrence

10ds Show and Social

The Challenge

Institutional Character

Bezos memo to Amazon in 2002

The API Mandate

  • All teams will henceforth expose their data and functionality through service interfaces.
  • Teams must communicate with each other through these interfaces.

  • There will be no other form of inter-process communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.

  • It doesn’t matter what technology they use.
  • All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.

Duality of Corporation and Information

  • What is less written about is corporate structure.
  • This information infrastructure is reflected in the corporation.
  • Two pizza teams with devolved autonomy.
  • Bound together through corporate culture.

Amazon and Agility

  • Company prides itself on agility.
  • Operates through a system of devolved autonomy
    • Teams have defined inputs and outputs.
  • Corporate culture bonds them togethether.
    • Customer obsession
    • Ownership


  • Over 800 years old.
    • Less commonly thought of as agile.
    • Reliable institutional character.
    • Amazon in comparison is more mecurial.

Organisational Data Science

  • Claim:
    • For data driven decision making to work there must be an interplay between institutional character and the information infrastructure.

Cultural Transplant

  • Claim:
    • Transplanting another organisation’s decision making infrastructure directly leads cultural rejection from the new host.

From Amazon to Policy

  • Amazon lessons apply in policy.
  • Pandemic advice, DELVE group.

SciOps: Operational Science

  • Difference between
    • Answering a question
    • Giving your best guess on a tight time frame

F1 Strategy

  • Similar challenges seen in F1 strategy.


1. Executive Awareness

Executive Awareness

The first challenge is Executive Awareness.

Data Maturity Assessment

  • Emerging from the DELIVE Initiatives Data Work (The DELVE Initiative, 2020)
  • Recommendation for Data Maturity Assessments (Lawrence et al., 2020)

2. Executive Sponsorship

Executive Sponsorship

  • Direct sponsorship from the most senior executive.
    • This has a cultural effect as well as a direct effect.
  • Bring about through involvement
    • develops understanding of capabilities of data science in exec team.

Pathfinder Projects

  • In executive context: an important project that is interdepartmental.
  • Should involve the CEO, CFO, CIO and data science team (or equivalents).

3. Devolved Capability

Data Science Champions

  • Data science knowledge needs to be devolved to domain experts.
    • This gives scalability and ensures cultural compatibility.
  • Programme of data science champions
    • Centrally educated, but redeployed into their domains.

4. Intersectional Projects

Departmental Planning

  • Integrate data science in planning.
  • At Amazon teams asked to say how they are using machine learning in planning documents.
    • Raises awareness in departments, but can lead to superficial efforts.

Greenfield vs Brownfield

  • What are opportunities for UK in enabling data-driven innovation?
    • Greenfield innovation: entirely new businesses that emerge from the technology.
    • Brownfield innovation: reform and improve existing businesses by integrating technolgy.

Solow Paradox

You can see the computer age everywhere but in the productivity statistics.

Robert Solow in Solow (1987)

Retrofitting Infastructure

  • Imagine retrofitting the flush toilet to a 19th century tenement block.
  • Or the work done to transfer from town gas to natural gas: 40 million appliances and 14 million customers.
  • Moving to a carbon-neutral energy system.

5. Relevant Education

Bridging Data Science Education

  • Important to integrate education into the domain.
  • Bolt-on education is widespread (and can be leveraged).
  • But domain specific education critical for effective deployment.



  • Challenge
    • institutional character and information infrastructure are interlinked.
    • transplantation of a data-science culture from outside can lead to rejection.
  • Solution
    • Information infrastructure and institutional character need to co-evolve.


  1. Institution wide data maturity assessments.
  2. Executive sponsorship of the data science core.
  3. Pathfinder projects that are intersectional.
  4. Data science champions who come from the domains of deployment.
  5. Bridging courses that integrate institutional questions of important within data science education.

Key Theme

  • These are actions that deliver a short term benefit while encouraging longer term cultural change.



Lawrence, N.D., Montgomery, J., Paquet, U., 2020. Organisational data maturity. The Royal Society.
Solow, R.M., 1987. We’d better watch out: Review of manufacturing matters by stephen s. Cohen and john zysman. New York Times Book Review.
The DELVE Initiative, 2020. Data readiness: Lessons from an emergency. The Royal Society.