1. Module 3 Data Management’s Role in Acquisition
In this module, we will discuss the role of data management in the acquisition process. “Acquisition” used to mean “purchase”. Today, it can mean digital access and even formal delivery and acceptance of data digitally, by organizations and users. Designing and operating the proper DM solution is the outcome of defining a system that meets data requirements, and includes an understanding of factors which condition and enable the scope and range of the DM solution. By understanding the desired outcome, the data manager can structure a solution that is appropriate and effective.
Guidance includes application and evolution of DM within government contracting.

2. Module 3: Data Management and Acquisition
Learning Objectives:
Awareness of DM’s role from contract conception forward
Interpreting guidance to establish good DM methods and processes
Learning Outcomes:
How to define the DM program that meets user needs
Knowing areas of constraint, and special aspects of DM for the lifecycle
Presentation:
Explanatory Material:
A section which addresses awareness of data management’s role from concept to retirement of a system, and the commensurate guidance which provides the focus for a successful solution. The student will be familiar with how a contemporary DM program is developed to sustain user needs, and have working knowledge of the areas which are constrained in that solution, and why.
References:
GEIA-859, Data Management, Principles 2 and 3

3. Acquisition to sustainment focus – data to sustain life cycle needs
DM and Acquisition
DM’s purpose is to identify and provide data
Responsive to customer needs
Digitally, if possible
Acquiring minimum essential data
Access versus acquisition versus acceptance
Premises that are central to contemporary DM
Rules-based processes that present options and sometimes cost consequences for the user
Acquirer and Provider roles
Data delivery can be digital access and data rights can be purchased for digital products
Industry to government
Industry to industry
Government to government
Presentation:
Explanatory Material:
In the past, data was acquired almost without consideration to cost, use, or obligation. This was due, primarily, to the fact that larger defense budgets provided nearly unlimited funds to procure data – even when it was not necessarily required for the system development effort. It was easier to call for all data to be delivered, in the form of a technical data package. This data was archived against what were often unclear intentions or understanding about the content and the use of that data. The technical data package was a major cost element since it included the manual labor to compile the documentation, the cost of the medium, and possibly the cost of the providers intellectual property. This process provided months worth of work for the organization that provided it since it was usually delivered in hardcopy form.
As technology and time have evolved, it is possible for data to be acquired digitally, to be accessed without additional costs, to be presented for acceptance – all functions of the traditional data management suite of services. Today, data can be accessed and purchased online, and in the case of additional costs for access, the user clearly sees that costs are levied for data not originally ordered by the acquirer. Data rights are protected through rules-based security processes, and data marking.
References:
GEIA-859, Data Management, Principles 2 and 3
EIA-HB-859, Section 1.1, Data Management Overview, 4.0 Acquisition and Preparation
Acquisition to sustainment focus – data to sustain life cycle needs

4. Acquisition to sustainment focus – data to sustain life cycle needs
DM and Acquisition
Internally or externally
Customers come from both sources
Ownership versus stewardship role
Format contract or terms of agreement
Acquisition
“Delivery”
Planning
Presentation:
Explanatory Material:
It is important for the data manager to know that customers are internal and external – and that the DM role is that of a data steward who protects the interests of those who are owners, buyers, or users of the data. While formal contracting is still very much alive in the DoD environment, the environment where IPTs and collaboration provide significant detail is becoming more common, especially for systems integration.
The broader perspective of DM is interested in the entire lifecycle of data, from acquisition down the road to sustainment of the system in the field until the final disposition of the products and beyond. This evolution of the DM role in the enterprise supports the changing technology in a way that wasn’t possible previously, reducing cycle times and cost of data, reducing re-engineering and multiple design efforts for the same product by improving the ability to retrieve and secure the data.
References:
GEIA-859, Data Management, Principles 2 and 3
GEIA-HB-859, Section 2.3, Data Management Process Description, 2.4 Data Management Implementation over the Program Life Cycle.
Sustainment
Acquisition to sustainment focus – data to sustain life cycle needs

5. Alternative Ways to Provide Data
Hard copy
However, increasingly electronic
Access to a database, PDM system, or repository
Data is used through “views”, or mined for information to gain insight and knowledge into program status
Ad hoc reports or standard report formats
“Data product” is a dynamic representation, in this environment
Collaborative development
Iterative data production, review, acceptance, and disposal between the acquirer and the provider
Presentation:
Explanatory material:
There are several ways to provide data, and all of them incorporate elements of the current digital environment. Data used to be provided in hard copy format, though that is done less frequently today. The costs of hard copies are the primary drivers to digital format alternatives. These alternatives include digital format on physical media, and data access. Dynamic access to data is preferred, allowing for collaboration and exchange of data products during development.
This type of environment supports the concept of a view of data that can be tailored to meet the needs of the individual user. The “view” may be comprised of metadata, access records, custom reporting as well as the actual data files. The fact that the view is dynamic, allows for ad hoc changes based on the user’s need at any given moment.
Dynamic access to data brings with it issues related to the security of the data, and how it is managed in a collaborative environment. The module on proprietary data and intellectual property provides some guidance on how to address this.
References:
GEIA-859, Section 2, Section 3, Section 6
GEIA-HB-859, Section 2, Planning for Data Management, Annex B, Intellectual Property
New aspects in the contemporary DM model or process

6. Alternative Ways to Provide Data
Data planning that is deliberately linked to acquisition strategy and data concept of operations
How the data will be used, for what purposes, and by whom
Data requirements authentication process
Preceded by a risk analysis
What if the data is purchased and no delivery or access is established?
Are there risks in overprocuring data? (obsolete data, insufficient data)
Presentation:
Explanatory material:
The new DM approach includes forward planning for data, with emphasis on the acquisition strategy, life cycle considerations, and system use. An understanding of these factors assists the data manager to determine the types and formats of data that will be needed. In many instances, the acquisition strategy doesn’t include provisions for tracking, roles and responsibility for reviews and responses, storage, access, retrieval, or distribution. If these items aren’t addressed adequately with clear responsibilities assigned for each aspect, it will be difficult to measure success or value of the strategy, and there will be no accountability. In the industry environment, this strategy includes measuring the success or failure of suppliers to meet requirements. One of the biggest issues with acquisition strategies is that they end with contracting. The strategy should set the stage for the entire data contracting process which should be a closed loop process with metric reporting on the various critical points. What those points are is dependent on the strategy for a particular acquisition and is based on the unique requirements or needs of the program or the customer.
While this discussion addresses contracting for deliverable data, an acquisition strategy for data could be applied to purchase of a car, home, cable service or any variety of things that are purchased by all of us everyday. The formality and complexity of the acquisition strategy and concept of operations for such purchases will be dependent on the requirements/value to the user. An example – cable television service that doesn’t include the Food Network may not be selected as a provider for a person who likes to cook.
An important part of the contemporary DM model is the risk analysis – which allows a view into whether or not data should be purchased or simply accessed along the life cycle, as well as a determination of whether or not a disproportionate amount of money could be spent on data which becomes obsolete, or which evolves over the life cycle.
References:
GEIA-859, Principles 2 and 3
GEIA-HB-859, Section 3.3, Development of Data Management Strategy and Architecture, 3.4, Data Management Process and Infrastructure Design.
New aspects in the contemporary DM model or process

7. Data Acquisition Enablers
Review project strategy
and planning
Establish
general
requirements
for data
Develop data
strategy and
data concept
of operations
Determine
specific data
requirements
Authenticate
data
requirements
Award
contract
for data
Context?
Decision support?
Too early?
Obsolete?
Perform
data risk
analysis
Presentation:
Explanatory Material:
The process includes a review of the project strategy, and an assessment of the general needs and use for data across the lifecycle functions. We begin with establishing the data requirements – usually done through a data call to affected functions and areas within the organization. From a determination of the general requirements, it’s possible to develop a a data strategy, or a mapping of what data will be used by whom. Redundant data needs can be normalized during this step. The data concept of operations allows this mapping to evolve to what formats and frequencies the data should be provided, in order to assist with insight into program development. Specific data requirements emerge to complement the data strategy and concept of operations. Data requirements are then validated, and a risk analysis is needed to determine if and when data should be purchased or accessed, and what point on the timeline the data is sufficiently mature for purchase, if that is the decision made. A formal or working agreement codifies the data requirements to be passed on to the acquiring organization. This exchange of information clarifies the expectations and needs of both the provider and the acquirer of the data, and also allows concrete costs to be assigned and understood.
For the government practitioner, it should be understood that data requirements in this context includes the process of Data Calls, consolidation of requirements, Rights in Data, other required markings and distribution statements.
References:
GEIA-859, Principles 2 and 3, Annex D, Practice 2, 3, 4, and 5.
GEIA-HB-859, Section 3.3, Development of Data Management Strategy and Architecture, Section 3.4, Data Management Process and Infrastructure Design, Appendix B, Intellectual Property
Review the project strategy and determine the general needs for data
over the product life cycle

8. Assessing Risk Two major considerations Other risk factors
Over-provisioning
Providing data that is not useful or providing data prematurely, to the detriment of its accuracy and potential value
Under-provisioning
Failure to provide data when it is needed
Other risk factors
Inability to retrieve data (bad cataloguing or metadata development)
Loss of data (natural disaster, misplacement, or theft)
Obsolete data (retaining data that has no value)
Compromise of intellectual property
Presentation:
Explanatory material:
In conducting a risk analysis, there are two major considerations – acquiring too much, or the wrong, data, and failing to provide or acquire data that is critical to life cycle needs (as in data for spares, repairs, or reprocurement needs). Other risk factors range from the inability to retrieve the data, due to poor identification of the data or due to poor management of the data, over time - and the failure to protect data, whether it results from poor marking of the data to ineffective data control, or compromise of data which has been identified and stipulated as competitive edge, intellectual property, or proprietary in nature. The challenge of identifying and protecting data in the digital environment is especially difficult and must be rigorously performed by acquirers and providers, alike.
This activity may be more challenging in the government environment where levels of management responsible for decisions may be farther removed from the user of the data. This challenge provides an opportunity for Data Managers to step in and fill the gap by providing data and history or metrics to support risk assessments and mitigation strategies. Providing good planning documentation increases the value of the Data Manager in both the industry and government environment.
References:
GEIA-859, Principle 2, 6 and 7.
GEIA-HB-859, Section , Perform Risk Analysis, 6.0 Data Retention and Disposition, 6.5, Maintenance of Data Assets and Associated Indices, Annex B, Intellectual Property

9. Data Environmental Assessment
Understand
strengths
Understand
threats
Understand
stakeholders
Understand
weaknesses
Understand
opportunities
Understand
power
sources
Presentation:
Explanatory material:
Development of a strategy begins with considering the role of data management in the enterprise and developing the DM strategic plan. The data strategy and data concept of operations are key in determining the size, shape, and focus of the DM solution. That’s best done through a look at who wants what solution, to what use, and why. The data environmental assessment measures those factors. From this assessment of constituent elements of the DM solution, a clear understanding of the DM environment emerges.
Industry and government Data Managers have historically approached this concept in very different ways and environments. While the problems are the same in both environments, the methods used to find resolution will depend on the dynamics of the environment in which the strategy and planning are being done.
The Data Manager must think creatively to develop the vision and mission for the DM team. Thinking beyond what DM personnel must do to satisfy requirements, thoughts should turn to brainstorming, facilitated working groups and other possible means to spur thinking beyond the known requirements and identify ways to provide additional value to the organization. Focus first on areas of need that are unmet and apply skills of Data Managers to fill the gap and meet the need.
One example might be providing a service that hasn’t been required but the output information eases the decision making process. A metrics report provided to Project Leaders wasn’t asked for until it was provided to one manager for his monthly project review. Within six months, all project leaders requested the report as part of their standard project review package. Since only audited data is part of the report, the project lead has valid data to status his project that was previously “hit and miss”.
References:
GEIA-859, Principle 3
GEIA-HB-859, Section , Analyze the Environment, , Perform Gap Analysis
Understanding
of DM
Environment

10. Quiz Questions - Module 3
Alternative ways to provide data rather than hard copy include ..
a. Access to a database
b. Collaborative development environments
c. Data strategies and data concepts of operation
d. Data requirements authentication processes
e. All of the above
f. None of the above
Data is not purchased in industry to industry transactions.
True or False?
Developing data strategy and a concept of operations is key to determining solution context.

11. Quiz Questions – Module 3
Acquisition strategies include:
a. Types and formats of data needed
b. Provisions for tracking
c. Roles and responsibilities for reviews
d. Storage, access, retrieval
e. A and C
f. All of the above
g. None of the above
Acquisition strategies are used everyday by people everywhere.
True or False?
Risk analysis is designed to support buy or access decisions.

12. Quiz Questions – Module 3
The major considerations in risk assessment for DM are ..
a. Systems engineering and test and evaluation data needs
b. Data obsolescence
c. Theft or other loss of data
d. Compromise of intellectual property
e. Underprovisioning or poor planning for the system in the field
f. All of the above
g. None of the above?
Understanding the data environmental factors involves identifying strengths and weaknesses that are present.
True or False?
The data strategy and concept of operations are key in determining the size, shape and focus of the DM solution.