It's been a while since my last blog post, but I wanted to update everyone on a project that's been keeping me busy recently.
Last year Pearson Publishing commissioned me to write a book on big data / data science. They asked me to write it for executive audiences (non-technical), and to make it very practical for business use. Writing the book was quite a task, but it was a great exercise in summarizing the industry experience I'd gathered in my years leading data science teams at various companies (including eBay).
My book, titled Big Data Demystified, was released in the UK in February of this year, and it has just recently become available in the USA on Amazon
I divided the book into two parts, the first being a general overview of big data, AI, data science, along with an overview of related technologies and applications. The second part is a how-to for companies looking to use these tools and technologies to improve business results.
Here is an overview of the chapters:
Chapter 1: The Story of Big Data
How big data developed into a phenomenon, why big data has become such an important topic over the past few years, where the data is coming from, who is using it and why, and what has changed to make possible today what was not possible in the past.
Chapter 2: Artificial Intelligence, Machine Learning, and Big Data
A brief history of Artificial Intelligence (AI), how it relates to machine learning, an introduction to neural networks and deep learning, how AI is used today and how it relates to big data, and some words of caution in working with AI.
Chapter 3: Why is Big Data Useful?
How our data paradigm is changing, how big data opens new opportunities and improves established analytic techniques, what it means to be data driven, including success stories and case studies.
Chapter 4: Use-Cases for (Big) Data Analytics
An overview of twenty common business applications of (big) data, analytics, and data science, with an emphasis on ways in which big data improves existing analytic methods.
Chapter 5: Understanding the Big Data Ecosystem
Overview of key concepts related to big data, such as open-source code, distributed computing, and cloud computing.
Chapter 6: How big data can help guide your strategy
Using big data to guide strategy based on insights into your customers, your product performance, your competitors, and additional external factors.
Chapter 7: Forming Your Strategy for Big Data and Data Science
Step-by-step instructions for scoping your data initiatives based on business goals and broad stakeholder input, assembling a project team, determining the most relevant analytics projects, and carrying projects through to completion.
Chapter 8: Implementing Data Science - Analytics, Algorithms and Machine Learning
Overview of the primary types of analytics, how to select models and databases, and the importance of agile methods to realize business value.
Chapter 9: Choosing Your Technologies
Choosing technologies for your big data solution: which decisions you’ll need to make, what to keep in mind, and what resources are available to help make these choices.
Chapter 10: Building Your Team
The key roles needed in big data and data science programs and considerations for hiring or outsourcing those roles.
Chapter 11: Security and Governance
Principles in privacy, data protection, regulatory compliance, and data governance, and their impact from legal, reputational, and internal perspectives. Discussions of PII, linkage attacks, and Europe’s new privacy regulation (GDPR). Case studies of companies who have gotten into trouble from inappropriate use of data.
Chapter 12: Launching the Ship - Successful Deployment in the Organization
Case study of a high-profile project failure. Best practices for making data initiatives successful in your organization, including advice on making your organization more data-driven, positioning your analytics staff within your organization, consolidating data, and using resources efficiently.
For those of you looking for a highly technical book, this isn't it. However, it does contain valuable advice for running data science projects within an organization and is thus useful for both technical and non-technical audiences.