This is an excerpt from the book, “Secrets of Analytical Leaders: Insights from Information Insiders,” by Wayne Eckerson. The analytical leaders profiled in this book describe many keys to success. This chapter summarizes their keys to running an effective analytics program, while subsequent chapters dive into greater detail on the themes and subjects highlighted here.

Ironically, the analytical leaders spend as much time discussing how to manage people, projects, and processes as they do technology and architectures, which they view as enablers. Specifically, Tim Leonard emphasizes the need to understand the business and talk its language, while Dan Ingle focuses on building applications quickly through agile development approaches. Others, including Darren Taylor and Amy O’Connor, underscore the importance of obtaining strong executive sponsorship, while Kurt Thearling and others emphasize the need of getting a quick win to establish credibility and momentum for an analytics program. And every analytical leader emphasizes the importance of curating data and moving beyond insights to action.

Change management. Analytics requires both strong analytical leaders and executives who are willing to make a long-term commitment to its success. Analytics is not a one-time project; it’s a program—or as some say, a journey that requires a long-term investment of time, money, and expertise. It requires organizations to treat data as a corporate asset and invest in building an analytical infrastructure. Moreover, it requires workers to change the way they view and manage data, and frame and make decisions. This involves changing core processes as well as modifying individual and group habits, which is hard to do. Ultimately, as Amy O’Connor emphasizes, analytics is an exercise in change management.

Steps to Success

To succeed with analytics, organizations need the right culture, people, organization, architecture, and data. (See Figure 5-1.) This is a tall order. Putting these pieces in place involves more than just technical expertise; it requires an organizational overhaul that has to start at the top and ripple through the rest of the organization. There is as much “soft” stuff involved in succeeding with analytics as “hard” stuff. That’s why most of the analytical leaders profiled in this book spend much time discussing selling, marketing, and teamwork as they spend talking about technology and tools.

Figure 5-1: Analytical Framework

The Right Culture. Culture refers to the rules—both written and unwritten—for how things get done in an organization. These rules emanate primarily from the words and actions of top executives. Business executives must have a vision for analytics and the willingness to invest in the people, processes, and technologies for the long haul to ensure a successful outcome. Technical executives must be able to talk the language of business and recruit business people to work on their teams. They also need to manage all components of the analytics program, from data warehousing to business intelligence to advanced analytics.

The Right People. It’s impossible to do analytics without data developers and analysts. Data developers build and maintain the data structures (e.g., data warehouse, data marts, master data management, BI semantic layers) and create complex reports and dashboards. Analysts, on the other hand, explore the data and generate reports and dashboards to answer ad hoc questions asked by the business. Hiring and retaining the right people is not easy. Both data developers and analysts require a passion for data, along with a blend of people skills, technical expertise, and business knowledge.

The Right Organization. Every company needs to cultivate a federated organizational model to succeed with analytics. Centrally, it needs a center of excellence that establishes and inculcates best practices for building analytical applications and provides a forum for team members to share ideas and techniques. Departmentally, it needs embedded data developers who can quickly build data-driven solutions as well as embedded analysts who can quickly address ad hoc questions. Sometimes, these are one and the same person, but not always. In addition, a federated organization needs to manage shared data as an enterprise resource while empowering departments to build their own reports, dashboards, and analytical models. This dual focus requires some tricky organizational choreography that most companies have yet to master.

The Right Process. A hallmark of an outstanding analytical program is that it has standard processes and procedures for doing things, such as managing projects, developing software, gathering requirements, communicating across business functions, deploying analytical models, handling job errors, designing and changing data models, evaluating and selecting new tools and technologies, and ingesting external data, among other things. However, analytical managers must be careful not to overburden their teams with too many processes and standards that impede agility and undermine flexibility, as Eric Colson cautions later in the book. [Editor’s note: Part II of Eckerson’s book deals with various analytical processes: managing people (Chapter 10), developing software (Chapter 11), delivering insights and action (Chapter 12), and developing analytical models (Chapter 13).]

The Right Architecture. Every analytical organization needs tools and technologies to do its work. The ideal architecture creates a data assembly line in which data flows from sources to targets to applications, each tailored to different departments and types of users. It extends existing data warehousing environments with new database processing platforms and complements top-down monitoring with bottom-up ad hoc exploration. It also provides the right tools to the right people so they can generate or consume data-driven insights. Finally, it implements agile processes that accelerate software development while maintaining data consistency and models across business units—a sizable challenge that few organizations have yet to master.

The Right Data. Analytics also requires data that is in the proper shape and condition. It must be complete, accurate, timely, relevant, and consistent or business people won’t trust it and stop using it, even if their organization has invested millions of dollars in data-centric tools and technologies. Organizations also need to invest in the right kinds of data—internal and external, structured and unstructured—that business people need to answer critical questions. They also need to treat data as a corporate asset that is as precious as cash or people.

Commentary from Analytical Leaders

Ken Rudin: You succeed with analytics when you stay focused on the end goal. It isn’t enough to find patterns in the data and highlight trends and outliers in fancy charts, or deliver insights that can potentially drive business value. Your analysts must actually create business value... (To read more insight from Rudin, click here.)

Tim Leonard: To succeed with analytics, you need to put as much emphasis on the “business” as on “intelligence.” I rose up through the technical ranks and learned the hard way that you can’t be perceived as an IT person. You need to be perceived as a business person who uses technology to solve business problems... (To read more insight from Leonard, click here.)

Eric Colson: The key to success starts with getting the right people. I’ve learned that it’s far more important to hire people with the right personal qualities than the right technical skills. You want people who are curious, creative, tenacious, and passionate about what they do... (To read more insight from Colson, click here.)

Dan Ingle: My keys to success are pretty straightforward: 1) build things iteratively and incrementally using an agile development process, 2) adapt to circumstances and not be wedded to a particular solution or methodology, and 3) foster teamwork to increase productivity and effectiveness... (To read more insight from Ingle, click here.)

Kurt Thearling: There are numerous things that organizations overlook when implementing advanced analytics. One is the importance of curating your data—that is, deciding what data to make available for analysis, and organizing that data so it’s easy for users to find and access. This is not just data quality, which is also important, but for different reasons... (To read more insight from Thearling, click here.)

Darren Taylor: The three keys to creating a successful analytics program are: 1) obtain strong executive understanding and support, 2) deliver quick, meaningful business wins, and 3) make one person and group accountable for the program... (To read more insight from Taylor, click here.)

Amy O'Connor: The way that we’ll succeed at Nokia is to create a culture that treats data as our most critical asset. The keys to making this happen are executive support, evangelism, and collaboration. I don’t have technology at the top of this list because that part comes easy to people at Nokia... (To read more insight from O'Connor, click here.)

Summary

There are many factors involved in running a successful analytics program. But providing the right culture, people, organization, architecture, and data are the basis for success. Eckerson examines the outer layers of the framework in Part II of his book—that’s the “soft stuff” of people, projects and processes.