Data to Inform

Donald Houde ~ Data Management consulting

Copyright Data to Inform. All Rights Reserved.

Using data assets to inform an organization’s decisions and strategies requires taking a fresh look at many data management related practices that may have been tightly woven into the way an organization has historically done business.  Amongst others, the reexamined practices may include:


  • who manages, owns, stewards, evaluates, oversees quality, collects, stores, protects, governs, offers/renders/delivers, designs, models, maps, transforms, and audits each and every data element,
  • what data is available (including its granularity), what data is needed, what is data’s credibility and what is data’s value proposition,
  • where does data currently exist and where in the organization should data exist,

  • when data is collected (including its periodicity) and when is it required,

  • why is each data element and data stratum actually needed,

  • how each and every data element is managed, owned, stewarded, evaluated, insured to be highest quality, collected/ingested, stored, protected, governed, offered/rendered/delivered, designed, modeled, mapped, transformed and audited.


Depending upon the breadth of the desired influence an organization’s qualitative or quantitative data will have upon decision making and strategic innovation, these type of data management (DM) questions may require consideration for all types and forms of an organization’s data; whether structured or unstructured, whether underpinned by open source (Hadoop, MySQL, etc.), or proprietary substructures (Microsoft SQL Server, Oracle, SAP, dBASE, etc.).  Any or all transactional data, operational data, spreadsheet data, disparate sourced data, social media feeds, news feeds, warehoused data, analytic data, de-normalized data, etc. may need to be included in the business needs, requirements and risk assessments. The ultimate objective to transform these potentially varied data repositories into insight-producing information is essential for an organization’s ability to successfully evolve:

  • ideas into implementations,
  • visions into strategic plans,
  • plans into executable devices,
  • solutions' fabrication into sustainable operations,
  • short term wins into transparent, measured, continuous progress.


Whether a particular organization’s roadmap to employing “data to inform” has simple objectives or includes Big Data enterprise-wide complexities, successfully navigating that roadmap includes incorporating the science and art of effective data management framework(s). The Data Management International (DAMA) Organization’s Data Management Book of Knowledge (DMBOK) defines DM as “…the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets.”


The science of data management fundamentally applies the DMBOK’s DM definition by involving a data centric set of tasks and techniques that are used to serve as a framework for collaboration amongst an organization’s data stakeholders in a manner that supports the structure, policies, and operations of an organization, and points to solutions that enable an organization to achieve its goals and align visions with their implementation. Beyond data management framing the accountabilities and responsibilities associated with critical data administering, governing, supervising and controlling practices, DM frames many of the guiding principles, procedures and best practices performed by an organization’s key roles and personnel. These key roles may include traditionally non-data related expertise, such as, systems, process, project product or QA analysts, enterprise or business architects, producers and developers, just to name a few.


The art of data management is included in effectively designing and applying experientially based expertise in areas characterized by conversations related to governance, data quality assurance, data coaching and effective data/information consumer utilization and acceptance. In that light, Donald Houde, President and founder of both  Houde Consulting and the visionary data transformational alliance, Data To Inform, has designed, produced, and implemented a successful, innovative Data Management framework. A repeatable framework that effectively expands and builds upon processes, procedures and best practices native to time tested, scrutinized DAMA process standards while weaving in data coaching and governance methods that transparently involve stakeholders from data providers, producers and managers through decision makers to the ultimate data users and consumers. A framework that adapts proven DM focused leadership and managerial paradigms to include processes that successfully acclimate a data transformational implementationroadmap to the dynamic nature of each organization’s culture, capability and environmental attributes that are equally influential upon any data initiative’s success.


Application of his framework involves all data stakeholders working as a collaborative team serving as researchers, elicitors and documenters who actively educe organizational data needs, goals and objectives. In the context of successfully executing the Data Management Framework of Donald Houde and Houde Consulting, it becomes immediately critical to work with the project management office and business analysts to define and clarify the accountabilities and responsibilities of each individual serving in one or more of the team’s roles.  Within the context of his framework, responsibilities are assigned to those producing the work, but accountabilities are assigned to the individual who the leadership or management team will go to if a problem is recognized with a specific task, deliverable, milestone or project. For example, when assembling an initiative’s responsibility assignment matrix the desire is to have a single resource be held accountable for each item in the RACI (Responsibility, Accountability, Consulted, Informed) chart.


Similar to a Business Analyst, performing Data Management Analysis requires the DM team to skillfully execute data coaching expertise to engage stakeholders and to formulate a course of action derived from the elicited, filtered list of data ingestion and consumption needs versus desires, requirements versus wishes and achievable versus unachievable visions. The Data Management Team and Governing Body(ies) must insure the identified needs and resulting solutions effectively transform data into idea generating insight spawning information that remains aligned with an organization’s data related vision, strategies, and roadmap. The resulting analysis serves as a foundation for understanding the gap between an organization’s current state/data capabilities and the organization’s information needs whose solutions may require enhanced technical solutions, improved processes, requisite organizational changes and/or strategic planning/policy development.


Finally, the Data Management framework of Donald Houde and Houde Consulting is aligned with Project Managerial Professional (PMP) and Business Analysis Best Practices to enable the Data Management skilled practitioners to work with their project and program execution colleagues to elicit, detail and document the data aspects of an organization’s:

  • vertical,
  • purpose,
  • culture,
  • stakeholders and customers,
  • strengths, weaknesses, opportunities and threats (SWOT),
  • "way of doing business"
  • Business Intelligence Roadmap
  • aspirations and vision, etc.


The body of work produced through the blending of DMBOK’s foundational, but adaptive, practices and the Data Management Framework of Donald Houde has resulted in taking many initiatives, from idea through implementation and closure, along a successful, auditable and transparent path. Additionally, this blend has had tremendous success at reenergizing and revamping existing projects by resetting their direction and moving them onto a track toward completion and ultimate success.


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