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
This framework highlights the major areas required to run a successful analytics program.
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.
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