Chapter 13
INTEGRATING THE DATA CULTURE
Culture must be integrated the same as architecture.
Chapter 1 described the rampant data disparity that exists in most public and private sector organizations today and how to create a comparate data resource that meets the business information demand. Chapter 2 described the integration of data resource management, including data architecture and data culture. Chapters 3 through 12 described the concepts, principles, and techniques for developing a comparate data resource with a Common Data Architecture.
The current chapter describes the concepts, principles, and techniques for developing a cohesive data culture with a Common Data Culture. Complete data resource integration must include development of both a comparate data resource and a cohesive data culture. Developing either without the other will not achieve data resource integration and will not provide substantial, persistent benefit to the organization.
The human side of data resource management must be included with the architectural side to ensure success. Many organizations have attempted architectural change without cultural change, with less than fully successful or persistent benefits and increasing disparate data. Culture change must be included with architectural change to achieve persistent benefits.
CONCEPTS AND PRINCIPLES
The data culture concepts and principles were presented in Chapters 1 and 2. Those concepts and principles are summarized below in preparation for integrating the data culture of an organization.
Data Culture Concepts
Culture is the act of developing the intellectual and moral faculties; expert care and training; enlightenment and excellence of taste acquired by intellectual and aesthetic training; acquaintance with and taste in fine arts, humanities, and broad aspects of science; the integrated pattern of human knowledge, belief, and behavior that depends upon man’s capacity for learning and transmitting knowledge to succeeding generations; the customary beliefs, social forms, and material traits of a racial, religious, or social group.
Data culture (1) is the function of managing the data resource as a critical resource of the organization equivalent to managing the financial resource, the human resource, and real property. It consists of directing and controlling the development, administering policies and procedures, influencing the actions and conduct of anyone maintaining or using the data resource, and exerting a guiding influence over the data resource to support the current and future business information demand.
Data culture (2) is the component of the Data Resource Management Framework that contains all the activities, and the products of those activities, related to orientation, availability, responsibility, vision, and recognition of the data resource.
The data culture integration concept is to resolve the fragmented data culture and create a cohesive data culture for the management of a critical data resource. A thorough understanding of the current fragmented data culture leads to its resolution and the creation of a cohesive data culture. That cohesive data culture, along with a comparate data resource, support formal data resource management.
A fragmented data culture is a data culture that is broken apart into separate pieces that are unrelated, incomplete, and inconsistent. It is similar to a disparate data resource, and leads to the creation of a disparate data resource. A fragmented data culture cannot effectively or efficiently manage an organization’s data resource.
A cohesive data culture is a data culture composed of business processes that are integrated to effectively and efficiently manage an organization’s data resource. The business processes are seamless, consistent, and work together in a coordinated manner to develop and maintain a comparate data resource.
The Common Data Culture is a single, formal, comprehensive, organization-wide data culture that provides a common context within which the organization’s data culture is understood, documented, and integrated. It includes all components in the Data Culture Segment of the Data Resource Management Framework for a reasonable data orientation, acceptable data availability, adequate data responsibility, expanded data vision, and appropriate data recognition.
A common data culture (lower case) is the actual data culture built by an organization for the proper management of their data resource. It’s based on the concepts, principles, and techniques of the Common Data Culture. It provides the overarching construct for a common view of the organization’s data culture. All variations in the data culture are understood within the context of a common data culture. The preferred data culture is defined within the context of a common data culture. Data culture integration is done within the context of a common data culture.
Data culture integration is the thorough understanding of the existing fragmented data culture within a common data culture, the designation of a preferred data culture, and the transition toward that preferred data culture. It’s the act or process to integrate and coordinate the organization’s data management function and processes into a cohesive data culture. It resolves a fragmented data culture by getting people to work together to build and maintain a comparate data resource.
Formal data culture integration is any data culture integration done within the context of a common data culture. Informal data culture integration is any data culture integration done outside the context of a common data culture. It usually does not resolve variability in the data culture and seldom leads to a cohesive data culture.
An integrated data culture is a data culture where all of the data management functions and processes in an organization are integrated within a common context, and are oriented toward developing and maintaining a comparate data resource. Data culture variability has been resolved and data resource management is performed consistently across the organization.
Data culture transition is the transition of an organization’s data culture from a fragmented data culture state, through a formal data culture state, to a cohesive data culture state. It’s a pathway that is followed from a fragmented data culture to a cohesive data culture. It’s unique to each organization, depending on their existing data culture and desired data culture.
Data Culture Principles
The following data culture principles are summarized from Chapter 2. Specific principles are described below for each of the data culture components.
Data culture integration must be done in concert with data resource integration. Ideally, data culture integration should be done before data resource integration. However, that approach is seldom feasible because it’s difficult to implement a new data culture, then wait before applying that new data culture to management of the data resource.
All components of the cohesive data culture, including data orientation, data availability, data responsibility, data vision, and data recognition, must be implemented at the same time. Implementing only a subset of those components does not fully resolve the fragmented data culture.
Data culture integration must be performed organization-wide. Little good comes from implementing the cohesive data culture to only part of the organization. Other parts of the organization simply continue creating disparate data. Since the data are usually orthogonal to the organization structure, the disparate data cannot be resolved as long as some of the business units have not been included in data culture integration.
Data culture integration is simpler than data resource integration, yet is more difficult to implement. It’s simpler in the sense that it is not as detailed as data resource integration, fewer steps are involved, the steps are relatively straight forward, the variability is lower than the data resource, the understanding process is easier, and the documentation is easier.
It’s more difficult in the sense that changing the culture is more difficult than changing the architecture. Having the culture change the architecture is difficult enough, but having the culture change the culture can be quite difficult. People show some resistance to change when integrating the data resource. However, people show great resistance to change when integrating the data culture.
Implementing a cohesive data culture must be persistent. If the data culture transition is not persistent, people will drift back to their old ways of managing data and data disparity will increase. A considerable effort must be made to understand the resistance that people have and address that resistance. A considerable effort must be made to show people how a cohesive data culture develops a comparate data resource and how reversion to the fragmented data culture creates disparate data.
Implementing a cohesive data culture is not data governance. Data governance seldom has specific concepts, principles, and techniques, and seldom addresses the issue of a common data culture. It does not address a preferred data culture or the development of a cohesive data culture. It’s not as robust as formal data culture integration.
Data culture integration must be done both within and without the organization. The internal aspect is with the employees that must buy into data culture integration and commit to making data resource management work if a comparate data resource is to be developed and maintained. The external aspect is with consultants, vendors, trainers, and so on, that the organization utilizes to support their data resource management effort. The external people have their own thoughts, ideas, and agendas, which are often not in synch with the organization and should not be allowed to alter the cohesive data culture of the organization.
Data Culture States
The fragmented data culture state is the situation where every organizational unit, and possibly every person, is managing data in their own way, with their own orientation, vision, processes, and software tools. The data culture is highly variable and exhibits all of the characteristics of a fragmented data culture. The management is informal and seldom documented, and the fragmentation is not known. It is the least desirable state and is the initial state for data culture integration.
The formal data culture state is a necessary state where the data culture is readily understood within the context of a common data culture. The variability of the fragmented data culture is understood and documented, the preferred data culture is designated, and the data culture integration is prescribed. No changes to the data culture have yet been made pending review and approval by the organization.
The cohesive data culture state is the desired state, where the fragmented data culture has been transformed substantially and permanently to a cohesive data culture. It’s a persistent transformation according to the preferred data culture prescription. A single set of processes has been established across the organization. It’s the ideal, mature state for management of the organization’s data resource.
The fragmented data culture state is retrospective, descriptive, and probabilistic due to its fragmented and uncoordinated development. The formal data culture state understands that fragmented data culture and prepares a prospective, prescriptive, and deterministic preferred data culture. The cohesive data culture state implements the preferred data culture to provide an integrated data culture that works in concert with an integrated data resource to formally manage an organization’s data resource.
DATA CULTURE COMPONENTS
The problems, criteria, and principles for the five components of the Data Culture Segment of the Data Resource Management Framework are summarized below. Data Resource Simplexity should be consulted for a more detailed description.
Data Orientation
Data orientation is the first component of the Data Culture Segment. Orientation means the act or process of orienting or being oriented; the state of being oriented; the general or lasting direction of thought, inclination, or interest; the change of position in response to external stimulus. Data orientation is the orientation of data resource management in response to business information needs which allows the business to operate effectively and efficiently in the business world.
Unreasonable Data Orientation
Unreasonable means not acting according to reason, not conforming to reason, or exceeding the bounds of reason or moderation. An unreasonable data orientation is an unreasonable attitude about developing the data resource that is physically oriented, short term, and narrowly focused. Most public and private sector organizations have an unreasonable data orientation that results in high quantities of disparate data.
The problems with an unreasonable data orientation are summarized below.
A physical orientation is a profound orientation toward physical design, data storage, and hardware performance. Little or no consideration is given to developing data that completely support the current and future business information demand.
A short term orientation is focused on short term objectives to the detriment of long term needs. The major concern is current performance of hardware and software applications. A short term orientation usually results from a physical orientation.
A multiple fact orientation places multiple business facts or multiple values of the same business facts in a single data attribute. The orientation is toward short term data needs of current applications, not the long term needs of future applications.
A process orientation is a focus on the process performed on the data rather than on the data as a resource. The orientation is toward physically processing the data rather than on supporting the business information demand.
An operational data orientation is a focus on discarding operational data when their operational usefulness is over. No consideration is given for retaining historical data for analytical processing.
An independent database orientation focuses on developing individual databases without concern or interest for developing a data resource within a consistent organization-wide data architecture.
An inappropriate business orientation is any orientation that is not appropriate for business professional involvement. It can range from the exclusion of business professionals to business professionals developing their own databases.
Reasonable Data Orientation
Reasonable means agreeable to reason, not extreme or excessive, having the faculty of reason, and possessing sound judgment. A reasonable data orientation is an orientation toward the business and support of the current and future business information demand. It depends on the architectural concepts, principles, and techniques, but more importantly, it depends on the culture of the organization.
The reasonable data orientation criteria are summarized below.
Development of a comparate data resource must include both architectural principles and cultural principles.
Development of a comparate data resource must be business oriented.
Development of a comparate data resource must include business professionals and their knowledge of the business.
Development of a comparate data resource must include data management professionals, and their knowledge and skills with design and development.
Development of a comparate data resource must be done within a common data architecture.
Development of a comparate data resource must follow the Five-Tier Five-Schema concept.
Development of a comparate data resource must follow a proper sequence where appropriate people are involved at the appropriate time.
Development of a comparate data resource must include teamwork and synergy.
The reasonable data orientation principles are summarized below.
The business orientation principle states that the data resource must be oriented toward business objects and events that are of interest to the organization and are either tracked or managed by the organization. Those business objects and events become data subjects in a subject-oriented comparate data resource.
The business inclusion principle states that business professionals must be directly involved in the development of a comparate data resource. The understanding and knowledge that business professionals have about the business must be included to ensure development of a comparate data resource that supports the current and future business information demand.
The teamwork synergy principle states that the appropriate business and data management professionals must be involved at the appropriate time, in any project, to ensure that development or enhancement of a comparate data resource supports the business information demand.
The proper sequence principle states that proper design proceeds from development of logical data structures that represent the business and how the data support the business, to the development of physical data structures for implementing databases.
The single data architecture principle states that the entire data resource of an organization must be developed and managed within a single, organization-wide, common data architecture.
The Five-Tier Five-Schema orientation principle states that development of a comparate data resource within a common data architecture must be done according to the Five-Tier Five-Schema Concept.
Data Availability
Data availability is the process of ensuring that the data are available to meet the business information demand, while properly protecting and securing those data. The data must be readily available to support business activities, but they must be protected to ensure proper access and recoverability in the event of a natural or human caused disaster.
Unacceptable Data Availability
Unacceptable means not acceptable, not pleasing, or unwelcome. Unacceptable data availability is the situation where the data are not readily available to meet the business information demand or are not properly protected or secured.
The problems with unacceptable data availability are summarized below.
Data are not readily accessible when anyone needing data to support their business cannot readily access those data. The data can be totally unavailable or very difficult to access, or a person could not be authorized to access those data.
Data are inadequately protected when anyone who does not have business authority can gain unauthorized access to the data to alter or destroy it, or to make unauthorized use of the data.
Data are inadequately recoverable when the frequency of backups is not sufficient to match the fast pace of changes in a dynamic business environment, or when data are not backed up.
A person’s or organization’s right to privacy and confidentiality is unprotected when data are released that should not be released. In some situations, data that should be released are withheld when those data are not privileged by law.
Data are used inappropriately for purposes other than legitimate business activities.
Acceptable Data Availability
Acceptable means capable or worthy of being accepted. Acceptable data availability is the situation where data are readily available to meet the business information demand while those data are properly protected and secured. The data must be readily available so they can be shared across business activities. The data must also be adequately protected from unauthorized access, alteration, and deletion.
The acceptable data availability criteria are summarized below.
The data resource must be readily available to business and data management professionals needing data to perform their business activities.
The data resource must be adequately protected from unauthorized access, alteration, or destruction.
The data resource must have an appropriate balance between ready accessibility to meet business needs and adequate protection.
The data must be protected against reasonable failures in accordance with the critical nature of the data
The data resource must be recoverable from failure in accordance with the critical nature of the data.
The privacy and confidentiality of people and organizations must be protected.
The data must be used for ethical purposes.
The acceptable data availability principles are summarized below.
The adequate data accessibility principle states that access to the data resource must be sufficient to allow people to perform their business activities, and for citizens and customers to obtain the data they need regarding services and products.
The adequate data protection principle states that the data resource must be protected from unauthorized access, alteration, or destruction.
The proper balance principle states that a proper balance needs to be maintained between allowing enough access for people to perform their business activities, and limiting access to protect the data from unauthorized alteration or deletion.
The adequate data recovery principle states that the data resource must have reasonable protection against reasonable failures, and must be recoverable as quickly as possible with the data are altered or destroyed by human or natural disasters.
The privacy and confidentiality principle states that the data resource must be protected from any disclosure that violates a person’s or organization’s right to privacy and confidentiality.
The appropriate data use principle states that an organization must constantly review the use of data to ensure the use is appropriate and ethical.
Data Responsibility
Responsibility is the quality or state of being responsible; moral, legal, or mental accountability; reliability and trustworthiness; something for which one is responsible. Data responsibility is the assignment of appropriate responsibility for development and maintenance of the data resource to specific individuals.
Inadequate Data Responsibility
Inadequate means insufficient, or not adequate to fulfill a need or meet a requirement. Inadequate data responsibility is the situation where the responsibility, as defined, does not fulfill the need for properly managing a comparate data resource. The responsibility is casual, lax, inconsistent, uncoordinated, and not suitable for the current environment of a shared data resource.
The problems with inadequate data responsibility are summarized below.
No centralized control is the situation where any formal, centralized control over the data resource does not exist. People are thinking locally and acting locally.
No management procedures is the situation where few procedures exist for managing the data resource. Some degree of control may exist, or appear to exist, but minimal procedures are in place for people to follow.
No data stewardship is the situation where no formal responsibility for developing and managing the data resource for the good of the organization has been assigned.
Adequate Data Responsibility
Adequate means sufficient for a specific requirement; sufficient or satisfactory; lawfully and legally sufficient. Adequate data responsibility is the situation where the responsibility, as defined, meets the need for properly managing a comparate data resource. The responsibility is formal, consistent, coordinated, and suitable for a shared data environment.
The adequate data responsibility criteria are summarized below.
Formal responsibilities must be defined for data stewardship at all levels of the organization.
Reasonable management procedures that can be easily and readily followed must be established.
Centralized control of a common data resource must be imbedded in the organization.
The Data Resource Guide must provide support for data stewardship responsibility.
The adequate data responsibility principles are summarized below.
The data stewardship principle states that data stewards will be assigned at all levels of an organization, with appropriate responsibilities for developing and maintaining a comparate data resource.
Steward came from the old English term sty ward; a person who was the ward of the sty. They watched over the stock and were responsible for the welfare of the stock, particularly at night when the risks to the welfare of the stock was high.
A data steward is a person who watches over the data and is responsible for the welfare of the data resource and its support of the business information demand, particularly when the risks are high.
A strategic data steward is any person who has legal and financial responsibility for a major segment of the data resource. That person has decision-making authority for setting directions, establishing policy, and committing resources for that segment of the data resource.
A detail data steward is a person who is knowledgeable about the data by reason of having been intimately involved with the data. That person is usually a knowledge worker who has been directly involved with the data for a considerable length of time.
A tactical data steward is a person who acts as liaison between the strategic data steward and the detail data stewards to ensure that all business and data concerns are addressed.
The reasonable management procedures principle states that reasonable procedures for development and maintenance of a comparate data resource must be established.
The centralized control principle states that centralized control of a comparate data resource within a common data architecture evolves from the assignment of data stewards and the development of reasonable data management procedures.
Data Vision
A vision is the act or power of imagination, a mode of seeing or conceiving, discernment or foresight. A data vision is the power of imagining, seeing, or conceiving the development and maintenance of a comparate data resource that meets the current and future business information demand.
Restricted Data Vision
Restricted means to confine within bounds; subjected to some restriction; not general; available to particular groups and excluding others; not intended for general circulation or use. A restricted data vision is the situation where the scope of the data resource is limited, the development direction is unreasonable, or the planning horizon is unrealistic.
The problems with a restricted data vision are summarized below.
Scope pertains to the range of a person’s perceptions, the breadth or opportunity to function, or the area covered by a given activity. The data resource scope is the total data resource available to an organization. The actual data resource scope is the portion of the data resource that is actually formally managed. The perceived data resource scope is the portion of the data resource that is perceived to be formally managed. In most organizations, the perceived data resource scope is far larger than the actual data resource scope.
The data resource direction is the course of data resource development toward a particular goal or objective. In many organizations, the data resource direction is incompatible with the business direction, or is incompatible with the database technology direction.
A horizon is the distance into the future which a person is interested in for planning. The data resource horizon is the distance into the future that an organization is interested in planning for its data resource development. An unrealistic planning horizon is the situation where the data resource horizon is too nearsighted, too farsighted, or overly optimistic.
A nearsighted planning horizon is the situation where an organization’s data resource horizon is very short term. Data resource development is focused on short term objectives to the detriment of long term goals.
A farsighted planning horizon is the situation where an organization’s data resource horizon is very long term. The vision is too far over the horizon to be of interest to most people.
An overly optimistic horizon is the situation where the data resource horizon is the best, and can be easily and quickly achieved. The vision may be valid and realistic, but the horizon is too optimistic. In spite of the good intentions, the vision cannot be achieved within the designated horizon.
Expanded Data Vision
Expanded means to increase the extent, number, volume, or scope of something; to enlarge; to express fully or in detail; to write out in full; to increase the extent, number volume, or scope. An expanded data vision is an intelligent foresight about the data resource that includes the scope of the data resource, the development direction, and the planning horizon. It’s the situation where the scope of the data resource includes the entire data resource, the development direction is aligned with the business and technology, and the planning horizon is realistic.
The expanded data vision criteria are summarized below.
Increase the scope of data resource management to include the entire data resource at the organization’s disposal.
Set a reasonable direction for development of a comparate data resource that is aligned with the business direction and the technology direction.
Establish reasonable planning horizons that encourage people to become involved.
Develop a cooperative environment where all stakeholders work together as a team to achieve a comparate data resource.
The expanded data vision principles are summarized below.
The wider scope principle states that data resource management must ultimately include all data at the organization’s disposal.
The reasonable development direction principle states that the direction of data resource development must focus primarily on the business direction and secondarily on the database technology direction.
The realistic planning horizons principle states that realistic planning horizons must be challenging, yet achievable, and must be developed to cover all audiences in the organization. The horizons must stretch the imagination slightly, but not unrealistically. It must be understandable and achievable, but not too close or too distant.
The cooperative development principle states that the stakeholders of the data resource must be involved in developing the vision for a comparate data resource. An expanded data vision must be developed collectively by all the stakeholders of the data resource, through the strategic, tactical, and detail data stewards. It must be acceptable by the stakeholders after it is established.
Data Recognition
Recognition means the action of recognizing; the state of being recognized; acknowledgement; special notice and attention. Data recognition is the situation where management of the data resource is recognized as professional and directly supporting the business activities of the organization.
Inappropriate Data Recognition
Appropriate means especially suitable or compatible; fitting. Inappropriate means not appropriate. Inappropriate data recognition is the situation where the organization at large does not recognize data as a critical resource of the organization, the fact that the data resource is disparate, or the need to develop a comparate data resource.
The problems with inappropriate data recognition are summarized below.
The wrong target audience is where an initiative is targeting the wrong audience or trying to convince the wrong people that a serious situation exists with disparate data. The targeted audience may be too high in the organization or too low in the organization to be effective.
Requiring unnecessary justification is the mistaken perception that an initiative to understand the disparate data and develop a comparate data resource requires extensive justification.
Searching for a silver bullet is an attempt to achieve some gain without any pain. The result is minimal gain with considerable pain. The resulting situation may be worse than the initial situation.
An attempt to automate understanding is a mistaken perception that the understanding of disparate data and development of a comparate data resource can be automated.
A reliance on data standards is a mistaken perception that standards can resolve existing disparate data or prevent future disparate data. Data standards cannot resolve existing disparate data, and many data standards themselves are disparate.
A reliance on generic data models or universal data architectures is a mistaken perception that they can resolve the existing disparate data or prevent future disparate data. Those data models and architecture cannot resolve disparate data. They often create additional disparate data by forcing an organization to warp their perception of the business world into a generic data model or universal data architecture.
Appropriate Data Recognition
Appropriate data recognition is the situation where the organization recognizes that data are a critical resource of the organization, the data resource is disparate, and an initiative to develop a comparate data resource is needed. The recognition is organization wide and the data resource is managed with the same intensity as the financial resource, human resource, and real property.
The appropriate data recognition criteria are summarized below.
Start an initiative that targets vested interests.
Seek the direct involvement of business professionals.
Tap the hidden knowledge base in the organization that understands the data resource.
Start an initiative within the current budget.
Incrementally improve that initiative based on benefits gained.
Provide a proof positive perspective for improving data resource quality.
Be opportunistic and take every opportunity to sell a comparate data resource.
Build on any lessons learned with each successive phase of the initiative.
Adopt a no blame – no whitewash attitude for resolving the disparate data situation.
Avoid requiring any unnecessary justification for beginning an initiative to manage data as a critical resource of the organization.
The appropriate data recognition principles are summarized below.
The vested interest principle states that the audience with a vested interest in managing data as a critical resource of the organization should be targeted for supporting any quality improvement initiative.
The knowledge base principle states that the existing, often hidden, base of knowledge about the data resource must be tapped to ensure a complete and thorough understanding of the data. Any initiative to improve data resource quality must include people who have an intimate knowledge of the data resource by reason of having worked with the data for a long period of time.
The current budget principle states that any first initiative to improve data resource quality should begin within current budget. Most initiatives that start lower in the organization and within the current budget get very early recognition.
The incrementally cost effective principle states that any data management initiative to resolve disparate data and create a comparate data resource should begin small, produce meaningful results, and continue to grow to a fully recognized initiative.
The proof positive principle states that when you go to executives for approval with proof of positive results, you are more likely to gain their support than if you ask for support based on a promise to deliver.
The opportunistic principle states that every opportunity should be taken to promote the initiative in the organization, regardless of the size of the opportunity.
The lessons learned principle states that every initiative has some failures and some successes, and the lessons learned can be included in the next initiative.
The no blame – no whitewash principle states that the disparate data situation exists, that laying blame for that situation only polarizes and alienates people, and whitewashing the situation only allows it to continue.
The unnecessary justification principle states that an extensive justification is not needed to begin an initiative for developing a comparate data resource. An extensive justification is not needed to improve data resource quality.
DATA CULTURE INTEGRATION
Data culture integration includes description of data culture variability, data culture integration approach, data culture survey, the preferred data culture designation, and data culture transformation. Each of these topics is described below.
Data Culture Variability
The fragmented data culture has a high degree of variability, just like a disparate data resource. The data culture variability applies to all five components of data culture, just like data resource variability applies to all five components of data architecture. The data culture variability must be understood if a fragmented data culture is to be integrated into a cohesive data culture.
Data culture variability is a state where all aspects of data management are inconsistent, characterized by variations, and are not true to the concepts and principles for managing data as a critical resource. The management procedures are highly variable, and that variability is pervasive throughout the organization.
The larger the organization and the more geographically or functionally diverse an organization is, the greater the data culture variability. The longer an organization has been in business, and the more mergers and acquisitions the organization has endured, the greater the data culture variability. Greater data culture variability makes the task of developing a cohesive data culture more difficult. Greater variability causes greater uncertainty and greater resistance to creation of a cohesive data culture.
Explicit data culture variability is the variability that can be readily visible, or identified in documented procedures and data management actions pertaining to data orientation, data availability, data responsibility, data vision, and data recognition. Implicit data culture variability is the variability that is not readily visible, or identified in documented procedures and data management actions.
The presumed data culture variability principle states that an existing fragmented data culture is highly variable and should be considered as the norm in most public and private sector organizations. Seldom is any organization free from some degree of data culture variability.
Acceptable data culture variability is the acceptable level of variability in management of the data resource. Unacceptable data culture variability is any unacceptable level of variability in management of the data resource. Any data culture variability that is unacceptable and impacts management of the data as a critical resource must be resolved.
The data culture variability principle states that every organization has a level of variability that must be accepted and clarified, and that any variability above that acceptable level must be resolved. Data culture integration seeks to resolve the unacceptable variability and clarify the acceptable variability. The expect anything principle applies to data culture the same as it does to the data resource. One should expect any data management procedure, even if it seems irrational.
Data Culture Integration Approach
The data culture integration approach includes the common data culture, the scope of data culture integration, the sequence of data culture integration, the involvement in data culture integration, sources of insight about the current data culture, and adjustments to a common data culture. Each of these topics is described below.
Common Data Culture
The Common Data Culture consists of the five Data Culture Components of the Data Resource Framework: data orientation, data availability, data responsibility, data vision, and data recognition. It’s the common context for understanding and resolving fragmented data management practices in an organization. The existing fragmented data management practices are documented within these five components, the preferred designations are made within these five components, and the transformation is planned within these five components.
The data management practices may be grouped within these five components based on each individual organization. Public and private sector organizations vary considerably in their organization and business practices, making designation of a specific grouping of data management practices within each of the five components difficult. Therefore, each core team leading the data culture integration can develop any structure for grouping the data management practices that is appropriate for the organization.
Scope
The scope of data culture integration is the entire organization. Since the organizational structure is usually orthogonal to the data architecture, the entire fragmented data culture of the organization must be understood and integrated into a cohesive data culture. When only part of the organization is involved in data culture integration, the other part of the organization retains a fragmented data culture and continues creating disparate data. Therefore, the entire organization must be involved in integrating the data culture.
Sequence
The overall sequence of data culture integration is generally prioritized based on the organizational units that are most involved in managing or using the data resource. Organization units that are responsible for developing and maintaining the data resource should be involved first, followed by those organizational units that use the data resource to support their business activities. Finally, organizational units that are minimally involved in using the data resource can be included in data culture integration
The specific sequence for data culture integration is to start by surveying and documenting the existing fragmented data culture, much like inventorying and documenting the disparate data resource. When the existing data culture has been surveyed and documented, a preferred data culture can be designated, based on the business goals and objectives of the organization. When the preferred data culture has been designated, the data culture transformation can be planned and then implemented.
Data culture integration must be performed across the entire organization, and must be integrated with data resource integration. In the ideal situation, data culture integration should precede data resource integration so that the people are prepared to manage data resource integration properly. However, that ideal is not easily achieved and data culture integration often proceeds in concert with data resource integration.
Involvement
Anyone in the organization who develops, manages, or uses the data resource, or manages people who develop, manage, or use the data resource, must be involved in data culture integration. In most public and private sector organizations, that involvement includes virtually everyone in the organization, from executives to knowledge workers, and from business professionals to data management professionals.
A core team is usually designated to manage data culture integration. That core team typically has different members from the core team that manages data resource integration, although the two teams need to work together. The core team needs to establish the priorities for conducting the data culture survey and the people that need to be contacted. Then the core team moves through the process of surveying, documenting, making preferred designations, determining the transformation, and finally implementing the data culture transformation.
Data culture integration must be based on a no blame – no whitewash principle. People cannot be blamed for past practices and the existent of a fragmented data culture. Blame only polarizes people, alienates them from the data culture integration process, and hampers effective development of a cohesive data culture. Similarly, whitewashing the existing fragmented data culture prevents any effort to develop a cohesive data culture. Therefore, a no blame – no whitewash attitude must be adopted to encourage people to become involved in developing a cohesive data culture.
Sources of Insight
Insight into the current data culture in an organization comes from two primary sources. The first is any formal or informal documentation about the data management practices that should be followed. Formal documentation is generally approved by some person or group within the organization and is readily available to everyone in the organization. Informal documentation may not be approved by some person or group in the organization and may not be readily available to everyone in the organization. Informal documentation usually exists within a specific group or for a specific person.
The second source of insight is what each person in the organization is actually doing when developing, managing, and using the data resource. People may be following formal or informal documentation, or they may be doing their own thing, independent of any formal or informal documentation. The worst-case scenario in many public and private sector organizations is that people are doing their own thing when developing, managing, or using the data resource.
Adjustments and Enhancements
Adjustments and enhancements often need to be made during data culture integration. As the data culture integration process moves through the fragmented, formal, and cohesive data culture states, additional insights may be gained. Those insights need to be documented, and adjustments or enhancements made to the survey results, the preferred data culture, or the data culture transformation process. Like data resource integration, data culture is a discovery process and insights are continually gained.
Data Culture Survey
A survey is the act or instance of surveying; something that is surveyed; the examination of a condition, situation, or value; appraise, inspect, scrutinize. A data culture survey is the act of surveying the current data management practices in an organization and documenting the results of that survey. It identifies and documents the current data management practices within each of the five data culture components, including the explicit and implicit data management practices. It documents the fragmented data culture state and begins the process of formally understanding the existing data culture in an organization.
The term survey is used rather than inventory because specific items to be inventoried do not exist as they do in the data resource. The existing data culture is a set of explicit and implicit practices that must be identified and documented.
The data culture survey concept is that the existing fragmented data culture in an organization is leading to the creation of increasing quantities of a disparate data resource that are impacting business activities. That fragmented data culture must be identified and documented as the first step to understanding that fragmented data culture and transforming it to a cohesive data culture that leads to creation and maintenance of a comparate data resource that supports business activities.
The data culture survey objective is to survey and document all of the fragmented data management practices that are explicitly and implicitly being performed by people within and without the organization. Those fragmented data management practices will be used to designate preferred data management practices that will then be implemented to properly develop and maintain a comparate data resource.
Data Culture Survey Process
The data culture survey process is to identify the existing fragmented data culture and the problems created by that fragmented data culture within the five data culture components in the Data Culture Segment of the Data Resource Management Framework. The basic problems with a fragmented data culture were described above for each of the five data culture components. Those basic problems are a guide to gaining input from business professionals and data management professionals about the explicit and implicit data management practices currently being performed.
The data culture survey is a fact-finding mission to identify the specific problems with an organization’s fragmented data culture and what needs to be done to create a cohesive data culture. Existing formal documentation is collected during the data culture survey. Informal documentation is identified and formally documented.
People are interviewed about the organization’s management practices and the problems those practices are creating. Anyone in the organization involved in data resource management is interviewed to gain insight about the existing data culture. The basic problems start the interview process. When people start with the basic problems, they begin to see specific problems in the organization and begin to identify those problems.
The data culture survey does not include the architectural problems and principles. However, it can address problems with not having formal data architecture principles and techniques, or not following established data architecture principles and techniques. In other words, not having formal data architecture principles and techniques is a data architecture problem, but not establishing or following the formal data architecture principles and techniques is a data culture problem
Data Culture Survey Documentation
The existing documentation is brought together in one place as data culture insights. Data culture insights are any insights necessary for thoroughly understanding the organization’s existing fragmented data culture and developing a cohesive data culture for properly managing data as a critical resource of the organization.
Data culture insights gained during the data culture survey are documented according to the five components of data culture. Documentation of the data culture survey is relatively simple compared to documentation of the data resource. The documentation consists of textual statements about the data culture practices actually being performed and the conditions under which they are performed. The data culture practices are documented within each data culture component, and specific problems are documented within those data culture practices. The data culture insights are stored in one readily accessible place so that anyone in the organization can review and comment on those insights.
Data Culture Survey Components
Brief examples of data culture insights are shown below for the five data culture components. The specific insights obtained during the data culture survey vary with each organization and are far more extensive than those shown below. The reader can likely add more insights to the list.
Data Orientation:
Orientation of the data resource is purely physical without any logical design based on how the organization perceives the business world.
The business is unclear about the orientation of the business, making orientation of the data resource very difficult. Frequent changes are needed because the business lacks a long term orientation.
New employees need to receive a thorough orientation about the data resource before they are allowed to use or update the data.
Data warehouse and data analytical processes are severely hampered because operational data are not saved. Historical data cannot be found for longitudinal analysis.
Data files are developed based on business processes, which leads to redundant data and many bridges to maintain those redundant data.
Data management practices are in place, but very few people follow those practices, either because they choose not to or because they are not aware that formal practices exist.
Business professionals have absolutely no say in how the databases are designed or managed. The data often do not meet business needs.
Data Availability:
I know the data are there, but I can’t access those data. I end up creating my own data.
I assure my clients that their private data will be protected, only to find out that other employees have released those data. My clients are quite unhappy.
We have no idea what data are privileged and what data can be released. We need guidelines for privileged data.
Our system goes down frequently. I try to do business manually, but find it difficult to get those manual data back into the system.
I know other employees are using our data inappropriately for their own gain, but don’t want to create problems by naming those people.
It seems that anyone can change the data any time, which causes problems with the integrity of the data. Specific procedures need to be implemented for who can change data and when the data can be changed.
Data Responsibility:
I have no idea who is responsible for the data. It seems that anybody can do anything with the data.
Management allows a free-for-all with the data. I create my own data to protect its integrity.
I don’t know who to go to for information about the data. Everyone is referring me to someone else.
We need people who have a formally designated responsibility for the data.
Data Vision:
The people in my unit have no idea what the direction for data resource development might be. I’d like to follow that direction, but find it difficult.
All of the plans I see are so far in the future that it’s impossible for me to adapt to those plans. I need some near- term plans that I can follow.
Apparently management has plans, but I can’t find them. If I can’t find them, I certainly can’t follow them.
The plans I see are great, but it’s impossible to meet their deadlines. It takes time to implement the plans and I still need to perform my regular business tasks.
When people make plans, they need to consult the work force to see if those plans are reasonable and can be implemented. Dictated plans just don’t work.
Data Recognition:
We seem to be continually acquiring new software packages that will make our work better, but it always makes things worse.
Management keeps asking for justification to improve data quality. I could spend that time actually improving data quality rather than justifying the need.
We keep telling executives the problem, but they never listen. We explain a situation and then ask them later about that situation, but they don’t seem to remember.
I’m continually presented with purchased models that don’t fit the data I need to support my tasks. Why can’t we get a model that matches the data I need?
I keep being asked to be involved in writing data standards. That takes my time, but seldom seems to product any results.
Just when I get my data organized and understood, a new package comes along that treats my data differently. I have to adjust to the package, but it doesn’t fit my business.
Preferred Data Culture Designation
A preferred data culture is a subset of a common data culture that contains the preferred practices for managing data as a critical resource. It’s the desired data culture that provides the pattern for building a cohesive data culture and transforming the fragmented data culture to that cohesive data culture. It’s how the organization chooses to manage their data as a critical resource.
The preferred data culture concept is that the variability of the existing fragmented data culture will be resolved through the designation of a preferred data culture and the transformation of the fragmented data culture to a cohesive data culture. The variability may not be eliminated, but it will be reduced to a known and manageable level. Formal documentation of the preferred data culture allows people to readily understand what’s needed to formally manage data as a critical resource.
The preferred data culture objective is to designate the preferred practices for managing data as a critical resource, so that those practices are readily understood and consistently performed throughout the organization. Those preferred practices are then used for data culture transformation.
Preferred Data Culture Process
The preferred data culture process is to consolidate the data survey insights and to designate the preferred data culture based on the consolidated insights. The specific criteria and principles for making the preferred data culture designations were described above for each of the five data culture components. Those criteria and principles must be met when designating a preferred data culture for the organization.
The preferred data culture process defines the formal data culture state based on the data culture survey, the specific data culture criteria, and the data culture principles. It reduces the unacceptable level of data culture variability to a known and manageable level of variability. It sets the stage for transformation of the data culture from the fragmented state to the cohesive state.
The variability in the data culture identified during the data culture survey is relatively easy to identify. The insights obtained during the data culture survey often provide a wide range of differences in the existing data culture. That variability needs to be resolved to develop a cohesive data culture.
Note that data culture integration has no cross-referencing equivalent to the data cross-referencing in data resource integration. A common data culture does not exist like the common data architecture. Therefore, a preferred data culture variation is not designated from a set of data culture variations. The process is to state the preferred data culture based on the insights from the data culture survey, and the criteria and principles stated above.
The first step in the preferred data culture designation process is to consolidate similar insights into basic insights about the data culture. The data survey usually produces many insights that are similar, but are worded slightly different. These similar insights are combined into a set of basic insights that are used to prepare the preferred data culture designations.
The second step in the preferred data culture designation process is to review the basic data culture insights and prepare the preferred data culture practices within each of the five data culture components. If the basic insights don’t cover all of the criteria and principles, then preferred data culture practices are created to cover those criteria and principles. The result is the designation of a complete set of data culture practices for properly managing the data resource.
Preferred Data Culture Documentation
Documenting the preferred data culture is done in the same way as documenting the results of the data culture survey. The preferred data culture practices are stated within each of the five data culture components. Those preferred data culture designations are stored in one readily accessible place so that anyone in the organization can review the data culture practices.
Preferred Data Culture Components
The preferred data culture practices must meet the criteria and the principles provided above, and must meet or resolve the data culture survey items. Additional data culture criteria, data culture principles, and data culture practices may be added as necessary, to provide a complete set of data culture practices for the organization.
A complete list of all possible preferred data culture practices for all types of organizations would fill a book. Therefore, a few key considerations for developing the preferred data culture practices for a specific organization are listed below for each of the data culture components.
Orientation:
The data culture orientation practices must provide one formal orientation for managing the data resource, or several formal orientations for very large organizations with a wide range of business activities.
The data culture practices must focus on the business and how the business perceives the business world in which the organization operates.
The data culture orientation practices must focus on business goals and business objectives as laid out in the business intelligence value chain.
The data culture practices must provide support for the business plan and strategies.
The data culture orientation practices must support the entire data resource of the organization, including data within and without the organization.
The data culture orientation practices must support a critical area approach, where data are maintained for core business data and critical business areas.
The data culture orientation practices must be business professional focused, where the data resource is oriented to the business professional’s way of thinking, draws the business professional into the process, and taps the business professional’s knowledge and skills.
The data culture orientation practices must encourage business professional involvement, particularly in the logical design of the data resource, which leads to commitment, acceptance, and success.
The data culture orientation practices must focus on the people performing the business tasks, not on the processes, including business experts, domain experts, data experts, and any other stakeholders in the data resource.
The data culture orientation practices must focus on getting people to interact and share their problems, techniques, vision, options, and solution, as well as their data.
The data culture orientation practices must use acceptable terms that are appropriate for the business, are readily understood by the business, and draw business professionals into the process of managing the data resource.
The data culture orientation practices must not be tool driven or allow the data perceived by the business to be warped into a software product.
Availability:
The data culture availability practices must provide one formal view of availability for the data resource related to access, security, privacy and confidentiality, backup and recovery, and ethical use of the data.
The data culture availability practices must meet all rules and regulations for releasing data and withholding data
Responsibility:
The data culture responsibility practices must define formal responsibilities for managing the data resource, such as strategic data stewards for legal / financial responsibility, tactical data stewards for liaison in large organizations, and detail data stewards for developing the data architecture.
Vision:
The data culture vision practices must define a formal long term strategic vision for the organization’s data resource, and one or more short term tactical visions for major segments of the data resource.
The data culture vision must ensure that the vision is a very vivid picture of the future data resource, and includes steps to achieve that vision.
The data culture vision must describe an architecture approach to managing the data resource, and integrate that data architecture with the information technology infrastructure.
The data culture vision must describe the tangible and intangible benefits of managing data as a critical resource of the organization.
Recognition:
The data culture recognition practices must describe how the data can be recognized as a critical resource of the organization, including how management should be made aware of the critical nature of the data resource and how data management professionals should become more professional.
The data culture recognition practices must emphasize a sharable data resource that is developed within a single common data architecture.
The data culture recognition practices must emphasize that data and processes are orthogonal to each other, and that the data resource is not developed based on processes.
The data culture recognition practices must emphasize that problems exist with a disparate data resource, that blame cannot be laid for those problems, and that the problems cannot be covered up.
Data culture recognition practices must emphasize the advantages of a comparate data resource and the advantages of formal data resource management.
Data Culture Transformation
Data culture transformation is the formal process of transforming a fragmented data culture to a cohesive data culture, within the context of a common data culture, according to the preferred data culture. It’s a subset of overall data culture transition that includes transforming the data orientation, data availability, data responsibility, data vision, and data recognition. It’s a very detailed process that requires careful planning, but it is absolutely necessary to achieving a cohesive data culture.
The data culture transformation concept is that all data culture transformation will be done within the context of a common data culture using the preferred data culture. The best existing data culture practices are combined with new data culture practices to provide a cohesive data culture.
The data culture transformation objective is to transform the existing fragmented data culture to a cohesive data culture to support management of data as a critical resource of the organization. The objective is more than just documenting the existing fragmented data culture. It’s a precise, detailed process that creates a cohesive data culture.
Data Culture Transformation Process
The data culture transformation process is to move the organization from a fragmented data culture state to a cohesive data culture state based on the preferred data culture practices. It resolves the existing data culture fragmentation by implementing a cohesive data culture. It’s a forward transformation process that does not have a reverse data culture transformation equivalent to reverse data transformation.
The data culture transformation process is based on a formal plan, like the formal plan for any project. It includes data orientation transformation, data availability transformation, data responsibility transformation, data vision transformation, and data recognition transformation. It must be done either before data transformation or in concert with data transformation. The key considerations for developing a data culture transformation plan are listed below.
The data architecture concepts, principles, and techniques are important for creating a comparate data resource. However, the data culture concepts, principles, and techniques are needed to ensure that a cohesive data culture is in place so the organization can properly manage data as a critical resource.
Most people want the data resource to support the business, but past practices have prevented that from happening. Transformation to a cohesive data culture will ensure that the data resource meets the current and future business information demand.
Data culture transformation is based on success motivation, which requires a people orientation. People created the past data disparity and only people can resolve that disparity through implementation of a cohesive data culture.
People are uncertain about change and tend to resist it. Data culture transformation must recognize the resistance to change, reduce the resistance with a vivid vision, and manage the expectations of business professionals and data management professionals.
Most people are willing to change, but don’t know how to go about it and are concerned about impacts on the business during change. Data culture transformation must minimize these concerns by reducing impacts on the business.
People don’t mind changing, but they do mind being changed. They mind it very much. Therefore, business professionals and data management professionals must be actively involved in preparing a data culture transformation plan and implementing that plan.
Success is contagious. The data culture transformation plan must ensure that successes occur regularly and are visible. Those successes encourage people to continue with the transformation.
The data culture transformation plan should encourage involvement and change, not mandate that change. Mandates imply compliance, enforcement, and punishment, and should be avoided in any data culture transformation.
Data culture transformation should be the easiest route to follow toward a cohesive data culture and a comparate data resource. The easiest route to follow creates a win-win situation for the business and the employees.
The data culture transformation plan is a people issue, not a technical issue. Just writing the plan doesn’t achieve the end result. People must be involved in developing and achieving the plan.
Implementing the data culture transformation plan takes a very personal and emotional commitment. It takes trust in a clear, compelling, and credible vision of a cohesive data culture.
Many people concentrate on data architecture integration and ignore data culture integration. An organization cannot achieve complete data resource integration without having both data architecture integration and data culture integration.
Not creating both a comparate data resource and a cohesive data culture at the same time allows the data resource to drift back to disparity and the resulting impacts on the business. Many of the best-intentioned data architecture integrations fail because the data culture was not integrated. Both are needed for complete data resource integration.
SUMMARY
Data culture is the second segment of the Data Resource Management Framework, consisting of data orientation, data availability, data responsibility, data vision, and data recognition. The current data culture in most public and private sector organizations is fragmented, just as the data resource is disparate. That fragmented data culture needs to be integrated to form a cohesive data culture, just like the disparate data resource needs to be integrated to form a comparate data resource.
Data culture integration must be done either before or at the same time as data resource integration. All components of the data culture must be integrated at the same time to create a cohesive data culture. Data culture integration must be done organization wide since data and processes are orthogonal to each other.
Data culture integration is simpler than data architecture integration because there are fewer components to integrate. However, it is more difficult because the existing organization data culture must change that culture. A culture changing the architecture is relatively easy compared to a culture changing the culture.
Data culture integration is done within the context of a common data culture. The scope is the entire organization and the sequence is to survey the existing data culture, designate the preferred data culture, and transform the data culture. Anyone involved in developing, maintain, or using the data resource is involved in data culture integration. Insights into the existing fragmented data culture is obtained from formal and informal documentation, and from anyone involved with the data resource.
Data culture integration goes through three states: the existing fragmented state, the formal state, and the cohesive state. The fragmented state is the existing stat. The formal state is an understanding of the fragmented state through a data culture survey and the designation of a preferred data culture. The cohesive state results from a transformation of the fragmented state based on the preferred data culture designations. The formal criteria and principles for each of the five data culture components guide data culture integration.
Creating a cohesive data culture is a choice, just like creating a comparate data resource. Organizations can choose whether to create a cohesive data culture and a comparate data resource that supports the business information demand, or they can choose to all the disparate data resource and fragmented data culture to impact the organization’s business. The responsible choice that’s in the best interest of the public sector citizens and private sector customers is a cohesive data culture and a comparate data resource.
QUESTIONS
The following questions are provided as a review of data culture integration, and to stimulate thought about the need for data cultural integration.
- Why is data culture integration needed in addition to data architecture integration?
- What is the purpose of the three data culture states?
- What are the five components of data culture?
- How is a data culture survey performed?
- How is the data culture survey documented?
- How are the results of the data culture survey used to designate the preferred data culture?
- How does the Common Data Culture differ from a common data culture?
- How is the fragmented data culture transformed to a cohesive data culture?
- Why is there no reverse data culture transformation like there is with data transformation?
- Why must data culture transformation be persistent?