A poor estimate is often cited as the root cause for an IT-enabled digital transformation failure.\u00a0\u00a0The estimate itself is obviously not the cause for failure.\u00a0 Rather, it is the behaviors and actions that are driven by the poor estimate which ultimately deem the project a failure.\u00a0\u00a0Failure to develop an accurate estimate with the appropriate contingencies can lead to:\n\nAttempts to lower costs by assigning less capable talent than was assumed by the estimator resulting in flawed designs\/execution.\nReductions in scope to meet budget targets resulting in the inability to achieve the established business case.\nFlawed participation plans put in place leading to the inability to apply talent on time.\u00a0 Result \u2013 cascading of additional overspend.\nQuestioning of management\u2019s competencies by senior leadership. This can result in a dysfunctional governance model as the delegation of decision-making becomes inhibited.\n\nHow is it that so many IT-enabled digital transformation programs start with a low estimate?\u00a0\nWe will start by setting aside those estimates that were put forth with the knowledge that they were low.\u00a0 These low-ball estimates are sometimes provided by consultants working to get their foot in the door, or by executive sponsors working to gain approval for their programs.\u00a0 Excluding low-ball estimates, the primary cause of poor estimates tends to be a lack of experience and background of the leader.\u00a0\u00a0\nThe opportunity to assume a leadership role on a digital transformative program is typically a once in a career assignment.\u00a0 Typically, the success and experience of those chosen to be leaders has been centered on operational excellence or product\/market innovations \u2013 not large-scale transformation program management.\u00a0 This leads to the title of this blog.\nUnknown unknowns\nIn February 2002, Donald Rumsfeld, then the US Secretary of Defense, stated at a briefing: \u201cThere are known knowns.\u00a0There are things we know that we know.\u00a0 There are known unknowns.\u00a0 That is to say, there are things that we now know we don\u2019t know.\u00a0 But there are also unknown unknowns.\u00a0 There are things that we do not know we don\u2019t know.\u201d\u00a0\nConfused?\u00a0 Rumsfeld was lampooned for these remarks as most people interpreted them as nonsense.\u00a0 However, these statements, upon careful examination, make sense and can be directly correlated to poor estimates.\u00a0\u00a0More often than not, poor estimates are a direct result of unknown unknowns.\u00a0 So how does a newly ordained digital transformation leader come to grips with the unknown unknowns?\u00a0\u00a0Turn them into known unknowns!\nHalf of the battle of discovering your unknown unknowns is developing an understanding of the questions you should be asking that will drive the behaviors needed to discover the unknowns.\u00a0\u00a0Below are eight questions that digital transformation executives can use in assessing the overall quality of an estimate.\n1.\u00a0 Is there a fundamental understanding of the case for change and a clear definition of the business capabilities that are required to operate the business at the transformative level desired?\nThe answers to this should take the form of a complete scope definition that outlines the business processes involved, the geographic and business unit scope, the legacy systems involved, and the interactions with other corporate programs.\n2. Have we considered the cost of capture?\nHave we accounted for the additional costs that are required to maintain a new capability and capture the savings associated with a change? Costs could include new business functions created to maintain master data, or business process designs, the changes associated with new hiring or severance, or quick hit projects targeted at capturing short term wins.\n3. What are the consequences of our assumed deployment approach?\nHave we considered the costs of temporary bridges that need to be accounted for?\u00a0 Have we accounted for interim support and who will provide it?\u00a0 How do these assumptions impact the estimate?\n4. How have we developed our bill-of-material for construction and deployment?\nHave we established utilizing a complete template?\u00a0 What were baselines that we used for comparison purposes?\u00a0 What were the standards used for estimating? How were those standards validated?\n5. What is the expected business engagement model with the transformation?\nDo we fully understand the amount of participation that is expected to support data cleaning, training, deployment practice, support, etc.?\u00a0 Do we have an appreciation for the types of decisions that will be required, as well as who and how those decisions will be made?\n6. What project productivity is planned for the estimate?\nHow have we accounted for productivity improvements over time?\u00a0 What is the level of talent required to achieve standards for productivity?\u00a0 What are the assumptions regarding asset leverage as the project moves through its lifecycle?\n7. Have we taken into consideration special accounting considerations with large programs?\nHave we appropriately handled contingency budgets?\u00a0 Do we understand the potential impact of exchange rate fluctuations? Can interest payments be capitalized; will it be charged to the program?\u00a0 Is there a clear definition of capital and expense on program activities?\u00a0\u00a0 Will there be write-offs required of any existing company assets?\n8. How have we accounted for biases in the estimating process?\nEstimates are derived by formula and experience.\u00a0\u00a0Experience implies that judgment is a key factor.\u00a0 The quality of one\u2019s judgment is the product of training, environment, past projects, and peer influence.\u00a0 Understanding the biases that might have factored into the estimate will help in the identification of any potential blind spots.\nLow estimates = failed transformations\nUtilizing such a rigorous and diligent approach to understand and answer these questions will likely result in an estimate that is quite higher than the original estimate.\u00a0\u00a0However, given our premise that low estimates result in failed digital transformations, transformation leaders will have increased confidence in the probability of success with an estimate that has been properly vetted.\nThe task of converting unknown unknowns to known unknowns can be a humbling undertaking.\u00a0 Recognizing and acknowledging your own blind-spots can be tough to swallow, but wouldn\u2019t you rather understand your blind-spots before you start your journey rather than when you reach the precipice of a failure?