Eight to Late

Sensemaking and Analytics for Organizations

Preface to “Data Science and Analytics Strategy: An Emergent Design Approach”

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As we were close to completing the book (sample available here), Harvard Business Review published an article entitled, Is Data Scientist Still the Sexiest Job of the 21st Century? The article revisits a claim made a decade ago, in a similarly titled piece about the attractiveness of the profession.2 In the recent article, the authors note that although data science is now a well- established function in the business world, setting up the function presents a number of traps for the unwary. In particular, they identify the following challenges:

  • The diverse skills required to do data science in an organisational setting.
  • A rapidly evolving technology landscape.
  • Issues around managing data science projects; in particular, productionising data science models – i.e., deploying them for ongoing use in business decision- making.
  • Putting in place the organisational structures/ processes and cultivating individual dispositions to ensure that data science is done in an ethical manner.

On reviewing our nearly completed manuscript, we saw that we have spoken about each of these issues, in nearly the same order that they are discussed in the article (see the titles of Chapters 5– 8). It appears that the issues we identified as pivotal are indeed the ones that organisations face when setting up a new data science function. That said, the approach we advocate to tackle these challenges is somewhat unusual and therefore merits a prefatory explanation.

The approach proposed in this book arose from the professional experiences of two very different individuals, whose thoughts on how to “do data” in organisational settings converged via innumerable conversations over the last five years. Prior to working on this book, we collaborated on developing and teaching an introductory postgraduate data science course to diverse audiences ranging from data analysts and IT professionals to sociologists and journalists. At the same time, we led very different professional lives, working on assorted data- related roles in multinational enterprises, government, higher education, not- for- profit organisations and start- ups. The main lesson we learned from our teaching and professional experiences is that, when building data capabilities, it is necessary to first understand where people are – in terms of current knowledge, past experience, and future plans – and grow the capability from there.

To summarise our approach in a line: data capabilities should be grown, not grafted.

This is the central theme of Emergent Design, which we introduce in Chapter 1 and elaborate in Chapter 3. The rest of the book is about building a data science capability using this approach.

Naturally, we were keen to sense- check our thinking with others. To this end, we interviewed a number of well- established data leaders and practitioners from diverse domains, asking them about their approach to setting up and maintaining data science capabilities. You will find their quotes scattered liberally across the second half of this book. When speaking with these individuals, we found that most of them tend to favour an evolutionary approach not unlike the one we advocate in the book. To be sure, organisations need formal structures and processes in place to ensure consistency, but many of the data leaders we spoke with emphasised the need to grow these in a gradual manner, taking into account the specific context of their organisations.

It seems to us that many who are successful in building data science and analytics capabilities tacitly use an emergent design approach, or at least some elements of it. Yet, there is very little discussion about this approach in the professional and academic literature. This book is our attempt at bridging this gap.

Although primarily written for business managers and senior data professionals who are interested in establishing modern data capabilities in their organisations, we are also speaking to a wider audience ranging from data science and business students to data professionals who would like to step into management roles. Last but not least, we hope the book will appeal to curious business professionals who would like to develop a solid understanding of the various components of a modern data capability. That said, regardless of their backgrounds and interests, we hope readers will find this book useful … and dare we say, an enjoyable read.


You can buy the book from the Routledge website. If you do, please use the code AFL01 for a 20% discount (code valid until June 2023). Note that the discount has already been applied in some countries.

Written by K

April 12, 2023 at 6:06 am

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