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How to Implement Lean BI

Tuesday, February 12, 2013

Before delving into what Lean BI is, it is important to address what Lean BI is not.

Many people hear the word “Lean” and it conjures up images of featureless tools, limited budgets, reduced development and the elimination of jobs. Dispelling those myths out of the gate is crucial in order to garner support for implementing Lean BI from the organization and the BI team. If team members feel that by becoming lean they are working themselves out of a job then they will not support your efforts. If your customers feel that they will receive less service or be relegated to using suboptimal tools then they may not support your efforts as well.

So, what is Lean BI? Lean BI is about focusing on customer value and generating additional value by accomplishing more with existing resources by eliminating waste. Lean BI is a set of principles and practices that have been influenced by three main concepts:

  1. Lean manufacturing.
  2. Systems theory.
  3. Agile project management.


Implementing Lean BI is a journey, not a destination. The first step in implementing Lean BI is to understand that becoming Lean occurs incrementally over time, not all at once. It requires a change in not only the way in which most implement BI today, but also requires challenging some of the assumptions of the past. It can be as simple as following five principles.

  1. Focus on Customer Value

Value is defined as meeting or exceeding the customer needs at a specific cost at a specific time and, as mentioned in my last article, can only be defined by the customer. Anything that consumes resources that does not deliver customer value is considered waste. Examples of waste in BI organizations include:

  • Dashboards and reports that are developed but never utilized.
  • Data integration processes that run but aren’t required.
  • Indexes that are created on nonutilized database fields.
  • Data that is retained on primary storage but never used.
  • Rework due to outdated or incorrect business rules.

It can be difficult to define customer value since we tend to focus on the solution and the customer tends to focus on the problem. Sometimes we focus on providing solutions looking for a problem. For example, often BI developers attempt to anticipate what a customer requires based on past requests. It can often be the case that the BI team believes that it’s more familiar with the data of a functional area than the users are. Combined with the knowledge of the capabilities of BI tools, it is difficult to resist defining value for the customer. While value can only be defined by the customer, the BI team can help augment that value. For example, BI teams can demonstrate the capabilities of the toolsets, educate the users on when to use different types of graphs and charts and point out inconsistencies in the data. How do we know when something we do does not add value? Generally, it is under-utilized.

How do BI teams identify greater value up front and eliminate, or at least reduce the amount time working on tasks that, while emanate directly from the customer, add little value? Part of the solution is to instill a BI program governance model, which Steve Bell in the book “Lean IT” defines as the collective set of procedures, policies, roles and responsibilities, and organizational structures required to support an effective decision making process. The governance model is designed to link IT to the business by not only involving business users in the stewardship of the data, but also by having the prioritization of the work established by the executive team. It’s both a bottom-up and top-down process that engages both the business and IT.

The other part of the solution is to break down requests into projects and maintenance requests, and only work on projects that add customer value. For example, projects can be defined as any task or group of related tasks requiring more than eight hours of development time. For these efforts, ensure that they tie directly to customer value by identifying the result to be measured. Many BI practitioners will say that most projects can’t be measured, but in the book, “How to Measure Anything,” author Douglas Hubbard states “when a [person] believes something to be immeasurable, attempts to measure it will not even be considered. As a result, decisions are less informed than they could be. The chance of error increases. Resources are misallocated, good ideas are rejected, and bad ideas are accepted. Money is wasted.

Another tool to identify value and reduce waste is to create values stream maps. Value stream maps help us to identify the value up front as well as eliminate waste along the process. The BI value stream is the set of all specific actions required to bring a specific project, process or task to completion. We can map each action along the value stream to help identify waste.

The first step in analyzing the value chain is determining the steps that do not add customer value. As identified by James Womac and Daniel Jones in “Lean Thinking,” generally, three types of actions are identified along the value stream:

  1. Many steps will unambiguously create value (VA).
  2. Many steps will create no value but are unavoidable (NNVA).
  3. Additional steps will be found to create no value and are immediately avoidable (NVA).

In this example, which is fairly typical of a traditional BI project, the most obvious area of waste is the time spent waiting. Approximately 31 percent of the total project time was time spent waiting for both signoff and resource availability. BI teams also should question the time spent during the detailed design phase. How much of that time is for creating documentation for the developers versus documentation for the customer and/or BI team? They should really question all the steps along the value chain to determine whether they add value and where they can be improved. In the short term, improvement may be achieved via rapid iterations, integrated testing, prototyping, and other agile practices. In the longer term, the implementation of development standards, common processes and procedures, feedback loops and business driven metadata repositories will likely lead to a reduction in waste and greater value.

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Source: Information Management. Author - Steve Dine

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