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This chapter is from the book

What Is Agile Analytics?

Throughout this book I will introduce you to a set of Agile DW/BI principles and practices. These include technical, project management, and user collaboration practices. I will demonstrate how you can apply these on your projects, and how you can tailor them to the nuances of your environment. However, the title of this section is "What Is Agile Analytics?" so I should probably take you a bit further than the mountaineering analogy.

Here's What Agile Analytics Is

So here is a summary of the key characteristics of Agile Analytics. This is simply a high-level glimpse at the key project traits that are the mark of agility, not an exhaustive list of practices. Throughout the remainder of this book I will introduce you to a set of specific practices that will enable you to achieve agility on your DW/BI projects. Moreover, Agile Analytics is a development style, not a prescriptive methodology that tells you precisely what you must do and how you must do it. The dynamics of each project within each organization require practices that can be tailored appropriately to the environment. Remember, the primary objective is a high-quality, high-value, working DW/BI system. These characteristics simply serve that goal:

  • Iterative, incremental, evolutionary. Foremost, Agile is an iterative, incremental, and evolutionary style of development. We work in short iterations that are generally one to three weeks long, and never more than four weeks. We build the system in small increments or "chunks" of user-valued functionality. And we evolve the working system by adapting to frequent user feedback. Agile development is like driving around in an unfamiliar city; you want to avoid going very far without some validation that you are on the right course. Short iterations with frequent user reviews help ensure that we are never very far off course in our development.
  • Value-driven development. The goal of each development iteration is the production of user-valued features. While you and I may appreciate the difficulty of complex data architectures, elegant data models, efficient ETL scripts, and so forth, users generally couldn't care less about these things. What users of DW/BI systems care about is the presentation of and access to information that helps them either solve a business problem or make better business decisions. Every iteration must produce at least one new user-valued feature in spite of the fact that user features are just the tip of the architectural iceberg that is a DW/BI system.
  • Production quality. Each newly developed feature must be fully tested and debugged during the development iteration. Agile development is not about building hollow prototypes; it is about incrementally evolving to the right solution with the best architectural underpinnings. We do this by integrating ruthless testing early and continuously into the DW/BI development process.3 Developers must plan for and include rigorous testing in their development process. A user feature is "Done" when it is of production quality, it is successfully integrated into the evolving system, and developers are proud of their work. That same feature is "Done! Done!" when the user accepts it as delivering the right value.
  • Barely sufficient processes. Traditional styles of DW/BI development are rife with a high degree of ceremony. I've worked on many projects that involved elaborate stage-gate meetings between stages of development such as the transition from requirements analysis to design. These gates are almost always accompanied by a formal document that must be "signed off" as part of the gating process. In spite of this ceremony many DW/BI projects struggle or founder. Agile DW/BI emphasizes a sufficient amount of ceremony to meet the practical needs of the project (and future generations) but nothing more. If a data dictionary is deemed important for use by future developers, then perhaps a digital image of a whiteboard table or a simple spreadsheet table will suffice. Since our primary objective is the production of high-quality, high-value, working systems, we must be able to minimize the amount of ceremony required for other activities.
  • Automation, automation, automation. The only way to be truly Agile is to automate as many routine processes as possible. Test automation is perhaps the most critical. If you must test your features and system manually, guess how often you're likely to rerun your tests? Test automation enables you to frequently revalidate that everything is still working as expected. Build automation enables you to frequently build a version of your complete working DW/BI system in a demo or preproduction environment. This helps establish continuous confidence that you are never more than a few hours or days away from putting a new version into production. Agile Analytics teams seek to automate any process that is done more than once. The more you can automate, the more you can focus on developing user features.
  • Collaboration. Too often in traditional projects the development team solely bears the burden of ensuring that timelines are met, complete scope is delivered, budgets are managed, and quality is ensured. Agile business intelligence acknowledges that there is a broader project community that shares responsibility for project success. The project community includes the subcommunities of users, business owners, stakeholders, executive sponsors, technical experts, project managers, and others. Frequent collaboration between the technical and user communities is critical to success. Daily collaboration within the technical community is also critical. In fact, establishing a collaborative team workspace is an essential ingredient of successful Agile projects.
  • Self-organizing, self-managing teams. Hire the best people, give them the tools and support they need, then stand aside and allow them to be successful. There is a key shift in the Agile project management style compared to traditional project management. The Agile project manager's role is to enable team members to work their magic and to facilitate a high degree of collaboration with users and other members of the project community. The Agile project team decides how much work it can complete during an iteration, then holds itself accountable to honor those commitments. The Agile style is not a substitute for having the right people on the team.

Guiding Principles

The core values contained in the Agile Manifesto motivate a set of guiding principles for DW/BI systems design and development. These principles often become the tiebreaker when difficult trade-off decisions must be made. Similarly, the Agile Alliance has established a set of principles for software development.4 The following Agile Analytics principles borrow liberally from the Agile Alliance principles:

  • Our highest priority is to satisfy the DW/BI user community through early and continuous delivery of working user features.
  • We welcome changing requirements, even late in development. Agile processes harness change for the DW/BI users' competitive advantage.
  • We deliver working software frequently, providing users with new DW/BI features every few weeks.
  • Users, stakeholders, and developers must share project ownership and work together daily throughout the project.
  • We value the importance of talented and experienced business intelligence experts. We give them the environment and support they need and trust them to get the job done.
  • The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
  • A working business intelligence system is the primary measure of progress.
  • We recognize the balance among project scope, schedule, and cost. The data warehousing team must work at a sustainable pace.
  • Continuous attention to the best data warehousing practices enhances agility.
  • The best architectures, requirements, and designs emerge from self-organizing teams.
  • At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.

Take a minute to reflect on these principles. How many of them are present in the projects in your organization? Do they make sense for your organization? Give them another look. Are they realistic principles for your organization? I have found these not only to be commonsense principles, but also to be effective and achievable on real projects. Furthermore, adherence to these principles rather than reliance on a prescriptive and ceremonious process model is very liberating.

Myths and Misconceptions

There are some myths and misconceptions that seem to prevail among other DW/BI practitioners and experts that I have talked to about this style of development. I recently had an exchange on this topic with a seasoned veteran in both software development and data warehousing who is certified at the mastery level in DW/BI and data management and who has managed large software development groups. His misunderstanding of Agile development made it evident that myths and misconceptions abound even among the most senior DW/BI practitioners. Agile Analytics is not:

  • A wholesale replacement of traditional practices. I am not suggesting that everything we have learned and practiced in the short history of DW/BI systems development is wrong, and that Agile is the new savior that will rescue us from our hell. There are many good DW/BI project success stories, which is why DW/BI continues to be among the top five strategic initiatives for most large companies today. It is important that we keep the practices and methods that work well, improve those that allow room for improvement, and replace those that are problematic. Agile Analytics seeks to modify our general approach to DW/BI systems development without discarding the best practices we've learned on our journey so far.
  • Synonymous with Scrum or eXtreme Programming (XP). Scrum is perhaps the Agile flavor that has received the most publicity (along with XP) in recent years. However, it is incorrect to say that "Agile was formerly known as eXtreme Programming," as one skeptic told me. In fact, there are many different Agile development flavors that add valuable principles and practices to the broader collective known as Agile development. These include Scrum, Agile Modeling, Agile Data, Crystal, Adaptive, DSDM, Lean Development, Feature Driven Development, Agile Project Management (APM), and others.5 Each is guided by the core values expressed in the Agile Manifesto. Agile Analytics is an adaptation of principles and practices from a variety of these methods to the complexities of data-intensive, analytics-based systems integration efforts like data warehousing and data mart development.
  • Simply iterating. Short, frequent development iterations are an essential cornerstone of Agile development. Unfortunately, this key practice is commonly misconstrued as the definition of agility. Not long ago I was asked to mentor a development team that had "gone Agile" but wasn't experiencing the expected benefits of agility. Upon closer inspection I discovered that they were planning in four-week "iterations" but didn't expect to have any working features until about the sixth month of the project. Effectively they had divided the traditional waterfall model into time blocks they called iterations. They completely missed the point. The aim of iterative development is to demonstrate working features and to obtain frequent feedback from the user community. This means that every iteration must result in demonstrable working software.
  • For systems integration; it's only for programming. Much of our effort in DW/BI development is focused on the integration of multiple commercial tools, thereby minimizing the volume of raw programming required. DW/BI tool vendors would have us believe that DW/BI development is simply a matter of hooking up the tools to the source systems and pressing the "Go" button. You've probably already discovered that building an effective DW/BI system is not that simple. A DW/BI development team includes a heterogeneous mixture of skills, including extraction, transformation, load (ETL) development; database development; data modeling (both relational and multidimensional); application development; and others. In fact, compared to the more homogeneous skills required for applications development, DW/BI development is quite complex in this regard. This complexity calls for an approach that supports a high degree of customer collaboration, frequent delivery of working software, and frequent feedback—aha, an Agile approach!
  • An excuse for ad hoc behavior. Some have mistaken the tenets of Agile development for abandonment of rigor, quality, or structure, in other words, "hacking." This misperception could not be farther from the truth. Agility is a focus on the frequent delivery of high-value, production-quality, working software to the user community with the goal of continuously adapting to user feedback. This means that automated testing and quality assurance are critical components of all iterative development activities. We don't build prototypes; we build working features and then mature those features in response to user input. Others mistake the Agile Manifesto as disdain of documentation, which is also incorrect. Agile DW/BI seeks to ensure that a sufficient amount of documentation is produced. The keyword here is sufficient. Sufficiency implies that there is a legitimate purpose for the document, and when that purpose is served, there is no need for additional documentation.

In my work with teams that are learning and adopting the Agile DW/BI development style, I often find that they are looking for a prescriptive methodology that makes it very clear which practices to apply and when. This is a natural inclination for new Agile practitioners, and I will provide some recommendations that may seem prescriptive in nature. In fact you may benefit initially by creating your own "recipe" for the application of Agile DW/BI principles and practices. However, I need to reemphasize that Agile Analytics is a style, not a methodology and not a framework. Figuratively, you can absorb agility into your DNA with enough focus, practice, and discipline. You'll know you've reached that point when you begin applying Agile principles to everything you do such as buying a new car, remodeling a bathroom, or writing a book.

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