Reference Data Management

This short paper describes the rationale for reference data and its management, sets out the six most common reference data cases, a generalized data model for managing reference data, descriptions of each of the six reference data cases, a strategy for reference data updating and maintenance, and finally, the recasting of existing reference data.

The topics is this paper include:

  • Rationale for Reference Data
  • Description of the Six Reference Data Cases
  • Generalized Data Model
  • Specifications of Six Reference Data Cases
  • Strategy for Reference Data Updating and Maintenance
  • Recasting Reference Data

Reference data represents data-based object collection classification schemes. Each reference data value should be orthogonal in terms of its semantics. Reference data values are employed to uniquely distinguish one object collection from another.

Collections of objects can be characterized in multiple ways, thus there can be multiple reference-data based characterizations. Simple reference data consists of discrete values representing single, easily distinguished meaning. Complex reference data contains multiple interrelated facts that are intrinsically related to each other, and in many situations have their values changed in lockstep.

An instance of reference data is almost always a row in a database table. As with all other rows, there are four kinds of columns for each table: uniqueness columns, metadata columns, content columns, and relationship columns. Uniqueness columns represent a set of values that cause the selection of just one row. Metadata columns represent the reference data’s adjudication information. That is, the information that affirms the authoritativeness of the values. This normally contains who, what, when, where, why, and how data. Content columns represent the “real” data, and for a single reference data content column that would be just the reference data value and the reference data value meaning. The final column set, relationship, represents the columns that, when valued, provide the uniqueness value for a different row in the same table (recursive relationship) or different table (child table for a parent-child relationship). In this latter situation, the uniqueness column set is often called the reference data table’s primary key.

Any database column that has a severely restricted value domain can be seen as reference data. Additionally, whole collections of data, that is, multiple columns within a reference data table, can also be seen as reference data. Reference data is often employed as sort fields, selection fields, and as “control breaks” for the purpose of statistical calculations. Sometimes reference data exists as single database columns, and other times reference data contains multiple columns that act as collections, in concert. On occasion, the reference data column values must “progress” from one value to the next in a strict sequence.

Reference Data CasesThe six common cases for managing reference data are presented in Table 1. Specific examples for each of these cases are provided in the tables (Tables 4 through 9) contained in the six subsections of this section. Table 2 of this section names and describes the database tables for the data model for reference data. Included in these subsections are descriptions of the data that needs to exist in the various data model tables from the generalized data model for reference data. Table 3 of this section describes the data that needs to be stored in the reference data tables to accomplish proper value domain governance.

These six reference data cases can be incorporated within the data model design set out in Figure 1. The database tables within the data model are set out in the Table 2.

For all of the six cases, the following tables need to be populated with an appropriate set of data that supports specific Code Types and Code Values. For the sequenced and mapped code values, this includes the policy basis for interrelationship among code values. For Code Types, this includes the relevant information within Code Type structures needed to support related Code Types. These tables are set out in Table 3.

Single Content Columns
An example of Case 1, Single Content Column reference data, is Gender. The involved Reference Data tables are identified and described in Table 4.

Multiple Content Columns
An example of Case 2, Multiple Content Column reference data, is postal addresses. In each address there are valid triples: City, State, and Postal Code. It is often incorrect to only update one of these columns without also updating its adjacent column’s value. The involved Reference Data tables are identified and described in Table 5.

Sequenced Values
An example of Case 3, Sequenced Values reference data are the various values for a document’s progression state. For example, Proposed, Draft, Final, and Revised. The involved Reference Data tables are identified and described in Table 6.

A variation on either the single content column or the multiple content column reference data type is sequenced reference data. In the case, the reference data values must either proceed in a forward or backward strict sequence, or can skip values forward or backward. Regardless, the general approach that is most commonly applicable is that any given value must be preceded by a previous value. For example, the values, Proposed, Draft, Final, and Revised are essentially indicators of state – based on well-defined assessments of a document’s progression.

In the example, the rules may be that the Draft state cannot exist except as a successor to a Proposed state. Or, that the Revised state cannot be set without the Final state having been achieved. Some states may exist in parallel and are only dependent on the prior state.

Representation of reference data sequence rules requires the employment of the Code Value Structure and Code Value Structure Type reference data tables. When these tables are properly valued, an application program can query an existing state and know what is the allowed successor state. If an inappropriate value change is attempted, the data from these reference data tables can be used to support a rejection. When these states are represented as data within the Code Value Structure data, they can be changed without 1) finding all the different application programs in which they have been encoded, 2) propose and accomplish software changes, 3) perform testing, and 4) modify user-guide documentation and the like.

With this strategy, single or multiple column reference data that is to be sequenced can be represented. The value of managing this sequence as part of the reference data is that it prevents any required encoding of a sequence in the application programs. Additionally, rules can be established and placed in the database that provides triggering information to the application program whenever any sequence “transgression” occurs.

Mapped Values
The ability to map reference data values one to another is needed for a number of different reasons. The five most common cases are:

  • Single or multiple content column value changes
  • Single value equivalences
  • Reduced quantity of reference data values
  • Expanded quantity of reference data values
  • External interfaces to other application systems and databases

An example of single value equivalences is again, “Gender.” For example, 0 = Female = F = Mujer. The involved Reference Data tables are identified and described in Table 7.

In the first case, single or multiple content column value changes, whenever a single or multiple content column value changes, both the previous and current values may need to be interrelated within the Code Value Structure table in order to represent history. Additionally, if the values are “changed in place” within the fact tables themselves, databases that contain millions of records and significant quantities of reference data columns would require large quantities of computer resources to effect the data value change. Not only would this be a waste of computer resources, all the backups of the database would have to be brought back online and have their reference data values changed as well.

Through the strategy of a mapped Code Value Structure table, an existing value would be retrieved, the effective end date would be discovered to be no longer valid, and the now current value would be retrieved.

For an actual example, if the value (e.g., Gender = “F”) is stored in the database and the value needs to be changed to “Female,” then, when the change is needed, all the records with “F” have to be retrieved and the gender column value changed to “Female.” This has two problems. First, the previous value of “F” is now lost if there hasn’t been an audit trail record created for each of the changed records; and second, there can be a significant quantity of computing resources expended for this change. If there were a million records and half were “F,” then there would have to be 500,000 records changed and also 500,000 audit trail records created.

The only practical alternative to this approach is to create the Code Value Structure records that interrelate the old value, “F,” to the now current value, “Female,” that has as its starting date and time the very same ending date and time for the “F” reference data record. The Code Value Structure record is the method that interrelates the “F” reference data record with the “Female” Code Value record. With this approach, none of the half-million records have to be changed. When the actual gender is needed for a person, the reference data value is retrieved. Because there is an “end date” value, the PassiveId value is employed to retrieve the current value for that reference data field. This technique works when there is the same quantity of valid values after a change as there was before.

In the second case, reduced quantity of reference data values, several values may be replaced by a single value. Suppose there were the values: Proposed, Draft, Final, and Revised. Suppose the new set was to combine the values Proposed and Draft into just one value, Initial. In this case the two discrete values would have to point to the same new value. This approach is straightforward if the mapping is always “forward.” But if it’s backwards, it would not be known what the prior value for “Initial” had been. That is, either “Proposed” or “Draft.” This too can be addressed by “walking back” from the Code Value row to the Code Fact Table Code Assignment row to the Fact Table’s row that contained the original value that remains. It remains because with this approach the original value is still in the database record.

In the third case, expanded quantity of reference data values, suppose the current values are: Proposed, Draft, Final, and Revised. Suppose the new values are to be: Proposed, Draft, Preliminary Acceptance, Accepted, Final, and Revised. In this case, business rules need to be created or changed that enable a re-casting of the reference data values. In this situation, not only must these values be added to the Code Value table, the interrelationships among these values and also the proper sequence must be added to the Code Value Sequence table. There also must be the identification of the business rules that would provide the unambiguous knowledge to know the conditions supporting the proper discernment of these new values. These business rules must be properly added and/or modified within existing business information systems. If the business information systems had been properly designed these changes should be minor.

In this third case, where there are interfaces to external systems either for data importing or exporting, there has to be Code Value mappings from the various reference data based external system data fields to the current set of reference data. These fields have to be discovered and their reference data values and meanings uncovered and stored in the reference data tables. This effort is directly supported by the reference data tables in Figure 1.

Figure 1: Generalized Data Model for Reference Data (mouse over image to enlarge)

In the fourth case, external interfaces to other application systems and databases, on importing, an external data record is read, its fields and values are known, and known as well are the transformed-to values. If all these values are stored in the Code Value tables, the data importing programs do not have to contain all these value transformation rules.

On exporting, this same general strategy applies for exporting data except in reverse. In either scenario, it is best to have the reference data tables contain all the value mappings and any necessary supporting rules for value mappings so that they are explicitly visible, are data not program based, and can be more easily changed than from within programs.

Discrete Values
An example of Case 5, Discrete Values, Zip Code, again serves as a good example. That’s because the Code Values are able to be well defined and are discrete. The involved Reference Data tables are identified and described in Table 8.

Discrete values reference data simply means that the values are enumerated. For example, the values 20716, 20717, 20718, 20720, and 20721. A common capability for representing discrete values is the ability to indicate that certain discrete values are not allowed. So, if all between 20716 and 20718 but not 20719, then the list would either contain mechanisms to express both.

Discrete value management can exist on either single content or multiple columns, and also on sequenced value and mapped value columns.

Range of Values
The immediately preceding example demonstrates the need to express ranges of values, such as 20716 through 20718, or 20718 through 20721. Negative ranges are commonly expressible as well. So, 20716 through 20718, but not 20719 would be expressible. Again, as in the last case this could involve both single and multiple column cases.

The involved Reference Data tables are identified and described in Table 9.

Reference Data Updating and MaintenanceIssues related to changes to reference data values parallels the Section 4 six reference data cases. Some of the update anomalies have already been addressed above and are cited. Of overall concern is what happens to a reference data value that was once valid and through a change has now become invalid? In this situation, there will have to be a thorough examination of how the formerly valid reference data values were stored in the database. How are they discovered? Can it be automatic? Or should it ever be automatic? Regardless, how will the already stored invalid values be changed? Will prior generated reports have to be re-executed and the difference in counts and other statistical and/or financial summaries have to represented along with explanation?

Single Content Column
The key issue with single content column value changes is whether history is to be retained or not. If not, there is no need to retain the previous value. In this case, there would be an automatic and complete recasting of one value and meaning to a different value and meaning. However, if history needs to be retained, this case immediately turns into a mapped value case where the old values are mapped to the new values.

Of special concern is whether a reference data value’s meaning has changed. If it has, and if the new meaning is to have the prior meanings retained, then again, this case immediately transforms to a mapped values case.

Multiple Content Columns
The updating concerns in this case are the same as for single content columns except that the value changes are possibly tracked as collection.

Sequenced values are inherently more complex. That’s because, for example, two additional states might have been introduced prior to a given state. All the business rules that enable an exact determination of all the states need to be very carefully examined to be assured that when the rules are executed the new set of sequenced states are able to materialized.

Mapped Values
Mapped values are also inherently more complex. That is because there will be prior mappings and new mappings. Each changed mapping set has to have the same end-date and start-dates so as to avoid unmapped intervals. If the mappings change, the various statistics, especially as they relate to importing and exporting from external systems may change and need to be explained. This will be especially so in situations where the quantity of source and target mapped values have changed.

Discrete Values
Discrete values are similar to single content column values except if an existing value becomes no longer valid. For example, if there were the discrete values, 20716, 20717, 20718, 20720, and 20721 and under a new scheme 20718 is now seen as invalid, the 20718 Value Case transforms to a mapped value case because the value 20718 might be mapped to either 20717 or 20720. Otherwise the reference data value becomes invalid and cannot remain in the database as such. If a new value is allowed, it may be necessary to revisit prior business event executions to determine if previously excluded data should now be included. Similarly, if a value is no longer allowed, prior data inclusions would have to be discovered and examined to determine what to do about what now becomes invalid data.

Range of Values
Ranges of values are similar to discrete values except if an existing begin or end value becomes no longer valid. For example, if there was the existing range, 20716 through 20717, and the new scheme was 20716 through 20721, an examination would have to be conducted to discover if previously invalided records with the value 20718 and now need to be included. Similarly, if the new range was 20716, through 20720, an examination of some existing records (e.g., 20721) that formerly passed a range test would have to be examined to determine appropriate actions.

Recasting DataThe most critical issue surrounding reference data is what to do when acceptable values change. Within the reference data community this is sometimes called “recasting.” Simply, recasting means that when a reference data value and/or meaning are changed, the understanding resulting from effectively the same queries and/or reports may also change. The three common cases are:

  • Single value change (with and without history).
  • Organizational relationship change (with and without history)
  • Meaning Change (with and without history)

For the first case, single value change (with and without history), some changes are simple and others are significantly more complex. Simple cases change, for example, the value of “F” to “Female.” In this situation, there is no recasting of the meaning of reference data value, only the value itself. Reports that include historical data will have to be able to not only obtain reference data values via their surrogate reference data values, but also have to discover and access any changed values that are, by reference data design represented by different reference data surrogate Id values.

For the second case, organizational relationship changes (with and without history), zip codes provide a ready but simple example. Zip codes change from time to time. Some entirely new ones are added, and some existing ones are split. For example, the “city”, Hershey, PA does not really exist. It’s just a U.S. Postal Service designation. If the USPS changes “Hershey, PA” to “Derry Township, PA” what happens to all the existing addresses for “Hershey, PA?” This would cause certain existing addresses that were formerly valid to now be invalid. Additionally, it might appear that Deny Township doubled in size if the report was based on its zip code. More importantly is the possibility of invalid addresses that were once valid and are now stored in a database. How are they discovered? How are they corrected?

A more critical example of organizational relationship changes would be the history of certain organizations within a database. Organizations can change through either acquisitions or losses. How is that history of changes reported over time? From one year to the next, a given organization can take on a completely different set of statistics. If longitudinal graphs are shown, there could be some real surprises.

For the third case, meaning Change (with and without history), reference data recasting can be very problematic. For example, suppose there are five values before, such as Proposed, Draft, Final, Terminated and Revised. Suppose the meaning of Terminated is changed from its meaning, Administrative Reasons, to also be understood as Administrative and Fraud Reasons. Grant Applications that were formerly denied for simply administrative reasons may now be seen as being denied for a very different set of reasons. While such a change can be accomplished through the reference data case, mapping, the real issue is reporting.

All these cases require very careful analysis and assessment to understand the complete implications of making reference data changes. All three reference data recasting cases apply to all six of the reference data types.

ConclusionsThe practical application of the points made in this paper include:

  • There is a critical need to reference data management as reference data are often the key characteristics employed for selection, counting, sorting, and grouping.
  • There exists a generalized data model that can be employed for reference data management.
  • The six common cases that must be addressed to effect reference data management are: single content column, multiple content columns, sequenced values, mapped values, discrete values, and range of values.
  • Strategies have to be created and set into motion to accomplish reference data management that is both efficient and effective, and that maintains to the maximum extent possible, the histories of values and meanings as the values were originally implemented.

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