Peter Aiken
VCU/Founding Director
Richmond, Virginia, United States
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Peter Aiken, PhD is an acknowledged Data Management authority, an Associate Professor at Virginia Commonwealth University, President of DAMA International, and Associate Director of the MIT International Society of Chief Data Officers. For more than 40 years, Peter has learned from working with hundreds of data management practices in more than 30 countries including some of the world's most important. Among his 12 books are the firsts: making the case for data leadership (CDOs); the first focusing on data monetization; and on modern strategic data thinking and the first to objectively specify what it means to be data literate. International recognition has resulted from these and a (pre-Covid-19) intensive worldwide events schedule. Peter also hosts the longest-running data management webinar series from Dataversity.net. Starting before Google, before data was big, and before data science, Peter has founded several organizations that have helped more than 200 organizations leverage data–specific savings have been measured at more than $1.5B USD. His latest is Anything Awesome.
Area of Expertise
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.
Making Data Work More Useful - progressing from literacy to proficiency to acumen
Without boring you citing a bunch of statistics can we agree that data is playing an increasingly important part of all government organizations? After all, your government organization should be viewed as all about data until it’s not about just data (some organizations actually make stuff). This day-long program will help everyone achieve a base-line understanding of
• Basic data and data use concepts in today’s work
• The critical importance of context when evaluating data
• How data is most useful in specific interactions with people, process, and technology - contextualized in a data lifecycle phase
• Several readily accessible technologies
• How to apply some of these today’s environment (email, file organization, tagging)
• The need to incorporate ethics throughout data work
No background is required but do hang-on tight as we rapidly move through a series of increased perceptions. Your perception of, and acumen with data will progress from literacy to proficiency to specialization as the day progresses. Topically, the team will collectively:
1. Learn to recognize that data is always in context and the attached implications,
2. Understand why it is important to know data lineage,
3. Comprehend why tracking and accounting for data is increasing part of today work environment,
4. Explore valid data exploitation for personal and professional use, and
5. Recognize and actively test for the ethical use of data.
Crafting Strategy for Your Data, - your most powerful, yet underutilized and poorly managed organiz
Data is not just another resource. It is your most powerful, yet poorly managed and therefore underutilized organizational asset. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Lack of talent, barriers in organizational thinking, and seven specific data sins exist as organizational prerequisites to be satisfied before (a measurable) 9 out of 10 organizations can begin to achieve the three primary goals of an organizational data strategy – these are to:
• Improve your organization’s data.
• Improve the way your people use data.
• Improve the way your people use data to achieve your organizational strategy.
In this manner, your organizational data strategy can be used to best focus your data assets in precise support of your organization's strategic objectives. Once past the prerequisites, organizations must develop a disciplined, repeatable means of improving the data literacy, standards, and supply as business objectives in specific areas that become the foci of subsequent data governance efforts. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complimentary concepts are covered including:
• A cohesive argument for why data strategy is necessary for effective data governance
• An overview of prerequisites for effective data strategy, as well as common pitfalls that can detract from its implementation, such as the “Seven Deadly Data Sins”
• A repeatable process for identifying and removing data constraints, and the •
• importance of balancing business operation and innovation while doing so
Exorcising the Seven Deadly Data Sins
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. 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 talk will discuss these barriers—the titular “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 and alternative approaches
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
Information Management Edmonton
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VA Data Symposium
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Global AI Summit
tbs
CDOIQ
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-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.
DGIQ West 2022
90% or more of organizational data challenges are people and process – importantly not technology-related. Strengthening our focus on non-technology aspects of data governance will be seen as crucial. An increasingly important aspect of data governance concerns data ethics. While not a case study, we will present three "incidents" describing how they were discovered, their handling, and lessons learned from each that led to specific operational changes/improvements. Each is detailed through the complete lifecycle. Our presentation focuses on people and process issues encountered with each incident. We believe these three illustrate aspects that are rarely documented much less presented. Offered as examples of good resolution process, we will show how:
– Data ethics are not confined to any specific industry
– Data governance processes must have clear objectives and roles
– Not solving ethics issues will cause only more challenges
Information Conference 2022
Achieving Data Acumen: Improving Workforce Data Literacy (and therefore performance!)
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:
1. Learn why the term data literacy is insufficient to describe the challenge and how the progression from literacy ➜ proficiency ➜ acumen is more operationally viable
2. Understand five data knowledge requirements levels in terms of their data leverage type, data skills type, ethical perspective and behavioral focus
3. Be able to match data knowledge requirements levels with types of organizational requirements
4. Begin to estimate the dollar ranges of potential knowledge worker productivity improvements in their organizations
DAMA Calgary
I will be giving a talk on Data Strategy that presents a different take, based on General Eisenhower's famous quote:
"In preparing for battle,
I have always found
that plans are useless,
but planning is indispensable"
The program presents data strategy as more of a process than a product.
DAMA Calgary invites everyone to join this free event on 19 May 18:00 UTC
Data Literacy (Closed Event)
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-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 Summit Boston
Panel: The future of dataops
Here is an improved abstract:
Gartner states that DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. This panel will discuss:
A brief history of ‘betterment’ approaches focusing of course on DataOps
The essential elements of DataOps (plan do check act)
It’s impact on data specific roles (architect/analyst/management)
Continuing to focus on these key elements will help to improve the ability of organizational to leverage their data.
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