Peter Aiken

Peter Aiken Speaking Topics

• A 12-step program for improving organizational data literacy

  Organizational data debt acts as a drag on all activities. Far too many organizations undertook to 'get better with data' without realizing the foundation changes that needed to occur. They were left without lasting improvements. Making a commitment to become data-centric, data-driven, data-first, data-focused, data-first, data-provocative (the list goes on), must be seen as more akin to life changing events of which the ubiquitous 12-step is the most famous. Considering there are more than useful variants of the original (AA), it seems time to make one for improving organizational data literacy. The program describes:

  • Motivation for taking a broad approach,?
  • Required people, processes, and organizational activities,
  • Organized into 12-steps

  These steps are required if organizations are to effect the necessary changes. Twelve-step programs are ritualistic for a precise reason—to stop bad habits, you must carefully replace them with better habits. Committing to 12 steps upfront, we will find it easier to achieve the critical mass required. Organizations can embark on their data improvement journey with their eyes wide open."

• Data Literacy and the Organization

  More than half of work is accomplished by knowledge workers–usually defined as those who must “think for a living” [Davenport, 2005].  I contend that all knowledge workers work with data.  Since most learn about data individually (if at all), the opportunity to gain from communal or best practices learning has not been present.  Most refer to this as a lack of data literacy.  Whether applied at the individual or organizational level, literacy is a binary concept and our data needs are more varied.  Data proficiency and data acumen are more descriptive/useful terms and these should also be used to describe today's organizational data knowledge requirements.  This program will describe five specific data knowledge requirement levels and objective behaviors that must be demonstrated by those operating at each level.  Lack of this data knowledge has so far hindered society from fully realizing our collective potential benefits.  More importantly, organizations adopting these data knowledge requirements can directly and immediately improve organizational knowledge worker productivity. Delegates will:

  • Learn why the term data literacy is insufficient to describe the challenge and how the progression from literacy ? proficiency ? acumen is more operationally viable

  • Understand five data knowledge requirements levels in terms of their data leverage type, data skills type, ethical perspective and behavioral focus

  • Be able to match data knowledge requirements levels with types of organizational requirements

  • Begin to estimate the dollar ranges of potential knowledge worker productivity improvements in their organizations

• Monetizing Data

  Business and technology decision makers are not data knowledgeable and consequently do not always make good data decisions. (Most commonly organizations purchase technology as a first step when it most probably should be purchased last.) Because of these bad data decisions, organizational data assets remain undervalued and quality is reduced. Undervalued data assets that are of poor quality act as sand (our focus) in various organizational mechanisms - slowing things down, decreasing product quality, and increasing expenses. Organizations spend 20-40% of their IT dollars on issues arising from poor data management practices. This material will explore the process of assigning money (and other quantifications) to poor data decisions across a variety of sectors. As a result, organizations will become increasingly aware of the root cause of various organizational challenges and can evaluate accordingly. Tangible take-aways include: Understand assigning monetary costs to poor data. Although it’s new, it’s not rocket science and with practice, organizations can become quite good at it

  • Learn from real-world, cross-sector examples and see how these patterns readily transfer to other sectors
  • How to construct an improvable data logistics value chain to drive business value
  • Exploration of non-monetary data costs
  • Important legal implications

• Connecting Data Decisions to Value

  Everyone wants to use data to add value to their organizations. The really important question is: how can organizations achieve more effectively data practices? This has been difficult to correct because, to quote Einstein: The significant problems we face cannot be solved at the same level of thinking we were at when we created them. Poor data education has led to naïve understanding that, when combined with a technology-first, bias has prevented the vast majority of organizations from making tangible progress. This, in spite of significant investments in hype such as big data science. Before attempting data improvements, organizations must resolve flawed decision making about data issues. The 90–minute program (+30 minute facilitated discussion) briefly takes senior executives through a data awareness journey, transforming their thinking about data. It provides an opportunity to better understand the kind of people and process decisions that will most speed-up your organization's ability to better leverage data. Many examples illustrate the material.

• Exorcising the Seven Deadly Data Sins

  Far more organizations attempt to do more with data than succeed. Understanding common prerequisites to unrestricted data practices will help you determine the extent of these challenges in your organization and increase your chances of success.  Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers—aka the “Seven Deadly Data Sins”—and in the process will also:

  • Elaborate upon the three critical factors that lead to strategy failure
  • Demonstrate a two-stage data strategy ? implementation process
  • Explore the sources and rationales behind the ? “Seven Deadly Data Sins,” and recommend solutions