William McKnight - Information Management: Strategies for Gaining a Competitive Advantage with Data

Andy Palmer

to become an analytics-driven organization, but how.

William McKnight tells us how beautifully, in this definitive book. Its an easily understood, action-oriented guide for Information Managers who want to help their organizations compete on data analytics in an era of Big Data and rapid technological innovation.

Successful analytics-driven organizations will build information architectures that match analytics workloads in the context of key questions that people in their organizations need to answer in order to create value on the front lines of their business. The best architectures will be loosely coupled so that they can absorb rapidly changing new technologies to meet competitive challenges and opportunities. Just like the the one-size-fits-all information architecture is dead.

I faced this reality personally while building out analytical informatics infrastructure as SVP and CIO at Infinity Pharmaceuticals and later as Global Head of Software and Data Engineering for Novartis Institutes for Biomedical Research (NIBR). It was also starkly clear when bringing new database and analytics products to market as a founder, advisor and investor in startups like Vertica, Data-Tamer, VoltDB and Cloudant.

This book provides an invaluable framework for making sane decisions about which technologies and approaches are right for you in building your information architecture. Youll learn how to focus your resources on solving the problems that will have the biggest impact for your business this month, quarter, and year. Competing on analytics is not about big multi-year projects: its about having an impact on decision-making every day, week and month. And ensuring that when you do have a significant strategic decision to make, that you do so with the context of all the data/information available to your organization.

Everyone involved in Information Management can benefit from this book: from Information Architecture experts and business-process owners to IT pros to the C-Suite. It covers the changing role of traditional analytics architecture e.g., the data warehouse and DBMSs and the incorporation of new analytics architecture, from column-oriented approaches and NoSQL/Hadoop to rapidly evolving IT infrastructure like cloud, open source and mobile.

Ultimately, however, this book isnt about technology or even about analytics. Its about people and empowering them.

Analytics starts with questions, from real people at all levels of an organization. What are the 100 (or 200 or 500) questions that would create significant value if the people in your organization could answer them with the support of all the data available in your organization at any given time?

The questions that need to be answered arent just the broad strategic questions that C-level execs talk about, but also the very tactical questions. In the past, most enterprises have focused on the former, because it cost too much and was a huge pain to extend analytics to anyone but business analysts or senior management. The technologies discussed in this book are making analytics practical for people throughout an enterprise. Democratizing analytics is a key trend that I see every day.

Great analytics provides the CONTEXT for all business people to create value throughout their day so they can make more-strategic decisions on tactical matters. Think about product support. Pretty prosaic stuff, right? Not really. When someone calls in for product support, whats the value of knowing that caller represents a top 5% customer or whether shes even a customer? How do the support people know how to prioritize requests without analytic context?

The Information Managers job over the next 20 years is to provide analytical context for every employee in the company. So that he or she can make the best decisions about how to allocate his or her time and the companys resources.

To the great information, experience and clear-thinking advice that William shares here, Id like to add some personal observations.

Always start with the questions. What are the questions that the people in your organization find most interesting and want to answer? Avoid data engineering projects that take quarters or years. Instead, embrace projects that are focused on collecting and answering very specific questions with high-quality data, using repeatable and sharable queries of data that interconnect sources across the company and leverage both external (publically available) and internal data.