Startups know that what customers say they want isn’t necessarily what they want. The same principle is critical to the success of your BI project.
As a proponent of agile data warehousing and business intelligence, I am constantly looking for new techniques for delivering value to customers faster, adapting to their feedback, and evolving toward the right business solutions (regardless of initial requirements). The recent book, Lean Startup by Eric Ries (Ries, 2011) and it has rocked my world. In the short time since this book hit the shelves in September, 2011, it has exploded in popularity. Be forewarned, this book is about entrepreneurship and high-tech startups. It isn’t about data warehousing, BI, or analytics … or is it?
Most startup companies fail. Startups typically rely on a good plan, a solid strategy, and thorough market research. The problem is that startups operate with a high degree of uncertainty. They don’t yet know who their customers are or what their product should be. They can’t predict the future. “Planning and forecasting are only accurate when based on a long, stable operating history and a relatively static environment. Startups have neither,” Ries points out.
Corporate DW/BI/analytics initiatives have much of this same uncertainty. Our customers are highly diverse, from across the enterprise, acting in varying roles, and having rapidly changing goals. For many reasons they can’t accurately tell us what “products” they want, and they don’t fully understand what our BI systems can do. Moreover, our BI strategies and “market research” are based on yesterday’s business needs, which are in a constant state of change.
My clients routinely tell me stories about building dozens of BI reports based on customer spreadsheets or requests, only to discover that most of these are rarely or never used. Like startups, enterprise BI initiatives don’t have a long, stable operating history or a static environment.
Ries points out that “The fundamental activity of a startup is to turn ideas into products, measure how customers respond, and then learn whether to pivot or persevere.” Lean Startup techniques follow a Build-Measure-Learn feedback cycle. This cycle begins with an idea or hypothesis immediately followed by building a minimal viable product (MVP). Customer response to this MVP is carefully measured and the resulting data provides the basis for learning and adjustment. The goal is to move through this cycle as fast as possible, and as many times as necessary to converge on the product that customers want.
This cycle is aimed at quickly building something, getting it in the hands of customers, and measuring their behaviors. “Instead of making complex plans that are based on a lot of assumptions, you can make constant adjustments with a steering wheel called the Build-Measure-Learn feedback loop.,” Ries explains. In this cycle, we start with assumptions, build the smallest/simplest product, test our assumptions, and decide whether to pivot or persevere.
We must learn what customers really want, not what they say they want or what we think theyshould want. We must discover whether we are on a path that will lead to growing a sustainable data warehousing, business intelligence, or analytics program.
Data warehouse and BI program leaders are entrepreneurs within the enterprise. It is the job of these entrepreneurs to quickly determine which efforts are value-creating and which are wasteful. By fostering Lean Startup thinking within your BI department, you can effectively establish a pattern of learning and adapting, which is the essential measure of progress for startups. It’s time to start thinking of your DW/BI/Analytics initiative as a startup. Will it be a successful one or not?
Eric Ries, The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York: Random House, 2011.