Mind+Machine. Vollenweider Marc

of data analytics that drives companies to hold too dearly to the idea of the power of data and makes the implementation more complex than required, and the marketing hype engines making promises that no technology can fulfill.

      Based on hundreds of client interactions at Evalueserve and with my former colleagues in the strategy consulting world, it became increasingly clear that there is a strong unmet need in the general managerial population for a simplified framework to enable efficient and effective navigation of information-heavy decision-support processes. Simplicity should always win over complex and nontransparent processes – the analytics space is no exception.

      I want to demystify analytics. I'll start with the fundamental observation that terms such as big data and artificial intelligence are getting so much attention in the media that the bricks-and-mortar topics of everyday analytics aren't getting the attention they deserve: topics such as problem definition, data gathering, cleansing, analysis, visualization, dissemination, and knowledge management. Applying big data to every analytics problem would be like taking one highly refined chef's tool – a finely balanced sushi knife, for example – and trying to use it for every task. While very useful big data use cases have emerged in several fields, they represent maybe 5 percent of all of the billions of analytics use cases.

      What are the other 95 percent of use cases about? Small data. It is amazing how many analytics use cases require very little data to achieve a lot of impact. My favorite use case that illustrates the point is one where just 800 bits of information saved an investment bank a recurring annual cost of USD 1,000,000. We will discuss the details of this use case in Part I.

      Granted, not every use case performs like that, but I want to illustrate the point that companies have lots of opportunities to analyze their existing data with very simple tools, and that there is very little correlation between ROI and the size of the data set.

      Mind+Machine addresses end-to-end, information-heavy processes that support decision making or produce information-based output, such as sales pitches or research and data products, either for internal recipients or for external clients or customers. This includes all types of data and information: qualitative and quantitative; financial, business, and operational; static and dynamic; big and small; structured and unstructured.

      The concept of mind+machine addresses how the human mind collaborates with machines to improve productivity, time to market, and quality, or to create new capabilities that did not exist before. This book is not about the creation of physical products or using physical machines and robots as in an Industry 4.0 model. Additionally, we will look at the full end-to-end value chain of analytics, which is far broader than just solving the analytics problem or getting some data. And finally, we will ask how to ensure that analytics helps us make money and satisfy our clients.

      In Part I, we'll analyze the current state of affairs in analytics, dispelling the top 12 fallacies that have taken over the perception of analytics. It is surprising how entrenched these fallacies have become in the media and even in very senior management circles. It is hoped that Part I will give you some tools to deal with the marketing hype, senior management expectations, and the jargon of the field. Part I also contains the 800 bits use case. I'm sure you can't wait to read the details.

      In Part II, we'll examine the key trends affecting analytics and driving positive change. These trends are essentially good news for most users and decision makers in the field. It sets the stage for a dramatic simplification of processes requiring less IT spend, shorter development cycles, increasingly user-friendly interfaces, and the basis for new and profitable use cases. We'll examine key questions, including:

      ● What's happening with the Internet of Things, the cloud, and mobile technologies?

      ● How does this drive new data, new use cases, and new delivery models?

      ● How fast is the race for data assets, alternative data, and smart data?

      ● What are the rapidly changing expectations of end users?

      ● How should minds and machines support each other?

      ● Do modern workflow management and automation speed things up?

      ● How does modern user experience design improve the impact?

      ● How are commercial models such as pay-as-you-go relevant for analytics?

      ● How does the regulatory environment affect many analytics initiatives?

      In Part III, we will look at best practices in mind+machine. We will look at the end-to-end value chain of analytics via the Use Case Methodology (UCM), focusing on how to get things done. You will find practical recommendations on how to design and manage individual use cases as well as how to govern whole portfolios of use cases.

      Some of the key questions we'll address are:

      ● What is an analytics use case?

      ● How should we think about the client benefits?

      ● What is the right approach to an analytics use case?

      ● How much automation do we need?

      ● How can we reach the end user at the right time and in the right format?

      ● How do we prepare for the inevitable visit from compliance?

      ● Where can we get external help, and what are realistic cost and timing expectations?

      ● How can we reuse use cases in order to shorten development cycles and improve ROI?

      However, just looking at the individual use cases is not enough, as whole portfolios of use cases need to be managed. Therefore, this part will also answer the following questions:

      ● How do we find and prioritize use cases?

      ● What level of governance is needed, and how do we set it up?

      ● How do we find synergies and reuse them between the use cases in our portfolio?

      ● How do we make sure they actually deliver the expected value and ROI?

      ● How do we manage and govern the portfolio?

      At the end of Part III you should be in a position to address the main challenges of mind+machine, both for individual use cases and for portfolios of use cases.

      Throughout the book I use numerous analogies from the non-nerd world to make the points, trying to avoid too much specialist jargon. Some of them might be a bit daring, but I hope they are going to be fun reading, loosening up the left-brained topic of analytics. If I could make you smile a few times while reading this book, my goal will have been achieved.

      I'm glad to have you with me for this journey through the world of mind+machine. Thank you for choosing me as your guide. Let us begin!

      ACKNOWLEDGMENTS

      My heartfelt thanks to Evalueserve's loyal clients, employees, and partner firms, without whose contributions this book would not have been possible; to our four external contributors and partners: Neil Gardiner of Every Interaction, Michael Müller of Acrea, Alan Day of State of Flux, and Stephen Taylor of Stream Financial; to our brand agency Earnest for their thought leadership in creating our brand; to all the Evalueserve teams and the teams of our partner firms MP Technology, Every Interaction, Infusion, Earnest, and Acrea for creating and positioning InsightBee and other mind+machine platforms; to the creators, owners, and authors of all the use cases in this book and their respective operations teams; to Jean-Paul Ludig, who helped me keep the project on track; to Derek and Seven Victor for their incredible help in editing the book; to Evalueserve's marketing team; to the Evalueserve board and management team for taking a lot of operational responsibilities off my shoulders, allowing me to write this book; to John Wiley & Sons for giving me this opportunity; to Ursula Hueby for keeping my logistics on track during all these years; to Ashish Gupta, our former COO, for being a friend and helping build the company from the very beginning; to Alok Aggarwal for co-founding the company; to his wife Sangeeta Aggarwal for introducing us; and above all to my wonderful wife Gabi for supporting me during all these years, actively participating in all of Evalueserve's key events, being a great partner for both everyday life and grand thought