I have been working with advanced analytics since 2001 and have watched the market evolve and mature over the intervening years. Where once only banks focused on predictive analytics to manage risk, now companies across all industries do. The role of advanced analytics in managing the customer journey has gone from innovative to mainstream. The time to develop and deploy advanced analytics has gone from months to seconds, even as the amount of data being analyzed has exploded. Leading companies see analytics as a core competency, not just a point solution, and innovators are increasingly looking at data as a source of future innovation. How to become data-driven and win with analytics is on everyone's to-do list, and books like the one Tho has written are critical in developing a practical plan to achieve data-driven, analytic innovation.

Tho and I met a few years ago when we co-presented on analytics through our work as faculty members of the International Institute for Analytics. We have a shared interest in the technologies and approaches that are both driving the increased use of analytics in organizations and responding to the increased demand from organizations of every size and in every industry.

All organizations have data, and we live in an era where organizations have more of this data digitized and accessible than ever before. More digital channels and more devices generate digital exhaust about our customers, partners, suppliers, and even our equipment. Government and third-party data are increasingly accessible, with marketplaces and APIs making yet more data available to us. Our ability to store and analyze text, audio, image, and video data expands our reach yet further. All these data stretch our data infrastructure to the limit and beyond, driving the adoption of new technologies like in-database and in-memory analytics and Hadoop. But simply storing and managing the data is not enough. To succeed, we need to use the data to drive better decision making. This means we need to understand it, analyze it, and deploy the resulting insights so that they can be acted on. These new technologies must be integrated into an end-to-end data and analytic life cycle if they are to add value.

Over the years, I have spoken with literally hundreds of organizations that are using analytics to improve their decision making. I have helped train thousands of people in the key techniques and skills required for successful adoption of analytic technology. My experience is that organizations that can think coherently about their decision making, especially their day-to-day operational decision making, and can see huge benefits from making those decisions more analytically— from using their data to see what will work and what will not. Such a data-driven approach to decision making drives a degree of innovation in organizations second to none. Succeeding and innovating with analytical decision-making, however, requires a coherent approach to the analytic life cycle and the effective adoption of data management and analytic technologies.

With this book, Tho has provided an overview of the analytical life cycle and technologies required to deliver data-driven analytic innovation. He begins with an overview of the analytical data life cycle, the journey from data exploration to data preparation, analytic model development and ultimately deployment into an organization's decision making, involved to transform data into strategic insights using analytics. This sets the scene for chapters on the critical technology categories that are transforming how organizations manage and use data. Each of these technologies is considered and put in its correct place in the life cycle supported by real customer examples of the value to be gained.

First, in-database processing integrates advanced analytics into a database or data warehouse so data can be analyzed in situ. Eliminating the time and cost of moving data from where it is stored to somewhere it can be analyzed both reduces elapsed time and allows for more data to be processed in business-realistic time frames. Improved accuracy and reduced time to value are the result.

In-memory analytics delivers incredibly fast response to complex analytical problems to reduce time to analyze data. Increasing speed in this way allows for more iterations, more approaches to understanding the data, and greater likelihood of finding useful insight. This increased speed can also be used to analyze fast-changing or streaming data without waiting for the data to be stored somewhere.

Finally, the Hadoop big data ecosystem allows for the collection and management of more data (both structured and semi-structured) than ever before. Organizations that might once have thrown away or archived data perceived as low value can now store and access data cost-effectively. Integrated with traditional data storage techniques, Hadoop allows for broader and more flexible data management across the organization.

These new approaches are combined with an overview of some more traditional techniques to bring it all together at the end with a description of the kind of collaborative data architecture and effective analytic data life cycle required. A final chapter discusses the impact of cloud computing, cyber-security, the Internet of Things (IoT), cognitive computing, and the move to “everything as a service” business models on data and analytics.

If you are one of those business and IT professionals trying to learn how to use data to drive innovation in your organizations and become leaders in your industry, then you need an overview of the data management and analytical processes critical to data-driven success. This book will give you that overview, introduce you to critical best practices, and show you how real companies have already used these processes to succeed.

James Taylor is CEO and principal consultant, Decision Management Solutions, and a faculty member of the International Institute for Analytics. He is the author of Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics (IBM Press, 2012). He also wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden and The Microguide to Process and Decision Modeling in BPMN/DMN with Tom Debevoise. James is an active consultant, educator, speaker, and writer working with companies all over the world. He is based in Palo Alto, California, and can be reached at [email protected].