A comparative perspective on data strategies

One data strategy to rule them all

One data strategy to rule them all
  • January 28, 2021

Are you making the most of your data?

Do you know where your data journey goes to and does your data strategy provide a tangible benefit to your company? To get the most value possible from your data and data strategy, you need to understand where your company’s specific challenges lie and what action is required. This PwC study provides in-depth analysis of the five key factors that drive any successful data strategy and explains the three data strategy archetypes to which companies typically belong. Determining your company’s archetype allows you to draw specific conclusions, identify concrete measures for action and then capitalise on it.

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'When working with our clients on the definition of their data strategy and the implementation roadmap, we regularly face the question: ‘How have others done it?’. In response to this, we conducted our research which led to the findings in this study.'

Matthias Leybold, Partner, Data & Analytics, PwC Switzerland

The five priority topics

We surveyed more than 50 companies on four different continents and interviewed more than 20 experts and leaders in the fields of digitalisation, data analytics (D&A) and digital transformation – covering the areas of finance, pharma and life sciences, automotive, manufacturing, consumer goods and services, utilities and the public sector.

Our research revealed that five decision topics are the foundational elements of any data strategy, and they influence all subsequent data strategy decisions:

For any transformation initiative, culture and people are key. A company’s capability management defines how the organisation builds and retains access to high-profile D&A professionals.

To secure sustainable access to D&A talent, knowledge and technology, companies generally pursue three strategies:

  • Opportunistic capability management
  • Capability management limited to D&A
  • Integrated capability management


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On their data journey, companies usually start with a variety of data platforms, driven by single business units’ D&A initiatives and initial implementation. As D&A activities mature, a trend towards consolidation can be observed, as scattered platforms create costs in terms of maintenance, fees, interfaces, compliance and talent. The gold standard is to have one data catalogue – and one technology stack – for the enterprise and all business units, giving business units the leeway to develop their own instance within the catalogue. However, legacy platforms can represent a major obstacle in setting up an enterprise-wide, efficient and effective solution. 

We have observed three kinds of platform landscapes:

  • Multiple D&A platforms
  • One enterprise D&A platform
  • One enterprise D&A platform with controlled exceptions

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A company’s data access philosophy is a mirror of some of its more fundamental data strategy reflections. Is data basically available to ‘everyone’, or is data generally restricted? But it is not only about asking for permission or refusing access to data. It is also about what a company wants to do and achieve with D&A, and about finding out what is possible today that was not possible ten years ago. Finally, data access is also about getting the most out of your data while protecting it. The handling of access to data has two (extreme) dimensions:

  • Restricted by default
  • Available by default

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An enterprise’s D&A organisation and governance shows how a company manages its D&A resources and activities, which data projects employees are working on, how they interact, and how a company’s data governance is set up. Furthermore, the domain of D&A governance defines a company’s approach to the governance of its data and D&A activities.

An enterprise has three options when making a strategic choice about its D&A resources:

  • Decentralised with domain focus
  • Centralised with functional focus
  • Hub-and-spoke

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How D&A use cases and initiatives are managed within a company is largely defined by its operating model. Crucial questions concern the overview of use cases, the potential for consolidation and the development of templates. The goal is to share and manage knowledge and to decide which use cases should be implemented. As this final decision is made in the business unit, the name of the game is harmonisation, but not complete centralisation. 

In our research, three categories of D&A operating models emerged: 

  • Opportunistic processes
  • Standardised processes and KPIs across business units
  • Harmonised process landscape with standardised KPIs and interfaces

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“With this study, you will get a structured framework that guides you through the most important topics that make up a successful D&A strategy and supports you in making conscious decisions about your implementation roadmap.”

Dr. Gundula Heinatz Bürki, Managing Director, data innovation alliance

The three data archetypes

Foundational and decentralised

Archetype 1

The foundational, decentralised archetype represents companies and organisations that are either at the beginning of their data journey or have not yet developed or implemented an overall vision of what their D&A capabilities must include, what they want to achieve with their data initiatives and what their data-driven business model of the future should look like.

Such companies typically show a low level of data maturity. They are in an organic default position with decentralised and scattered data governance. They have not yet harmonised their principles; their tools and D&A activities are not coordinated.

Archetype 1


Archetype 2

The centralised archetype 2 represents companies and organisations that choose a uniform approach to governance for standardised processes. Archetype 2 companies typically follow uniform principles and have strong regulations for common tools and platforms. Their D&A activities are centralised. Such companies develop a strategic vision and put strong emphasis on harmonisation. They exhibit a more advanced and more conscious level of data maturity than archetype 1 companies.

Archetype 2

Embedded and decentralised

Archetype 3

The embedded, decentralised archetype 3 represents enterprises that have a high level of data maturity. These companies tend to have harmonised governance, while keeping flexibility in the individual business units. They have templates and a knowledge library for principles, tools and D&A activities. At the same time, they are flexible in their use and application. Archetype 3 companies have a developed strategic vision, characterised by a balance of harmonisation and flexibility.

Archetype 3

Download the study here

Do you want to learn more about how implementing an enterprise data system benefits your organisation? Download our comparative perspective on data strategies.


Contact us

Matthias Leybold

Matthias Leybold

Partner Cloud & Digital, PwC Switzerland

Tel: +41 58 792 13 96

Joscha Milinski

Joscha Milinski

Partner and Data Strategy & Management Leader, PwC Switzerland

Tel: +41 58 792 23 58

Nina Wolf

Nina Wolf

Senior Manager Data Transformation & Analytics, PwC Switzerland

Tel: +41 79 193 07 00