Legacy data

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Legacy data is an information that is stored in old format or on obsolete information carrier that is difficult to access or process.

Problems with accessing legacy data can be associated with several causes:

  • no hardware to read data stored using obsolete information carrier (e.g. floppy disk, streamer),
  • no software to open file or convert it to readable format.

A lot of information collected by early information systems were kept on streamer tapes or floppy disks. Nowadays it is difficult to find old and working floppy or streamer drives. Therefore, accessing data is difficult. Many companies scraped their old computers, as they seemed to be unnecessary. However, sometimes it is essential to find old legacy data, e.g. from human resources systems or some government, insurance or health systems. There are service companies that help to recover such a legacy data.

In many households there are old video tapes (VHS or other). The best solution is to find working video tape player, connect it to computer and convert videos into files. Special video card usually is required. Many photo services will do it for you quite cheap.

It is easier to restore legacy data from files that are kept in old formats. Some of old applications are available on the internet as abandonware. However, some modern computers and system can be unable to run those applications. In that case emulators can help (e.g. DOSemu). They emulate old environment on modern computer. Other way is to find converter. For many once popular formats (e.g. ChiWriter) there were converters created. They convert files into readable formats[1].

Legacy data and organisations

Many organisations have invested in the special systems that manage data and allow access to this data because they use a large amount of data. These systems allow separation, merging and grouping legacy data. In this system, legancy data does not have to be moved elsewhere, because middleware system makes available an integrated outlook of legacy data sources[2]. Wrappers are components of software which converts data from LDM (legacy data model) to WDM (wrapper data model[3]. They are crucial elements of middleware system and data sources are escapulated by wrappers. Legacy data is modeled as subjects by wrappers. These wrappers ensure standard interfaces for query accomplishment and method invocation[4].

Legacy data systems

Legacy data systems may be based on different data models and this makes a problem in wrapping these systems. Wrapper must disguise the data model implemented by legacy system so that legacy system can deal with the differences between these models. Wrapper creates a canonical model that is more generic. The depiction of the database offered by wrapper must be a sematically richer than that gived by the DDL instruction. In addition, various data models have disparate languages that are used to fudge this data, so queries or commands are translated by wrapper from one language to another. Unfortunately, such translatation is not always realisable. Sometimes he must feign operations and conduct because the canonical model requires it. For COBOL files wrapper must feign the connected update and delete modes that inform how to supervise and propagate these operaions, if this canonical model contains interobject relationships or foreign keys. For COBOL files wrapper must feign some vanilla forms of alternative predicate if this model offers language which is similary to SQL language[5].

Wrapper that is destined for legacy data sources has three dimension.Ph. Thiran and J.-L. Hainaut mention about:

  • "the model-wrapper
  • the instance-wrapper
  • the upper-wrapper"[6]

The first and second dimension are created automatically, and the third dimension is created manually[7].

Goals of wrappers architecture

Roth M.T. and Schwarz P. argue that the architecture of building wrappers achieves some aims that allow the use of this architecture to integrate a different set of data sources. These aims are:

  • "The start-up cost to write a wrapper should be small
  • Wrappers should be able to evolve
  • The architecture should be flexible and allow for graceful growth
  • The architecture should readily lend itself to query optimization"[8]

Examples of Legacy data

  • Digital data stored on obsolete media such as 5.25" floppy disks and ZIP disks.
  • Legacy databases stored on mainframe computers using outdated formats and protocols, such as COBOL and VSAM.
  • End-of-life software applications with data stored in proprietary formats, such as legacy accounting systems.
  • Paper records stored in filing cabinets or microfiche stored in archives.
  • Audio recordings stored on cassette tapes or reel-to-reel tapes.
  • Video recordings stored on VHS tapes or Betamax tapes.
  • Image files stored in obsolete formats such as PCX and TIFF.

Advantages of Legacy data

Legacy data can be beneficial for organizations in certain ways. Here are some advantages of legacy data:

  • Legacy data can provide organizations with a view of the past. It can show how processes, systems, and services have changed over time, and provide insights into long-term trends.
  • Legacy data can also help organizations to better understand their customers and develop more effective marketing strategies. By analyzing customer behavior, organizations can adjust their products and services to meet customer needs and preferences.
  • Legacy data can also be used to track changes in the industry and assess the success of new products or services. By analyzing changes in market conditions, organizations can better manage risks and take advantage of opportunities.
  • Additionally, legacy data can be used to create better models and forecasts for the future. By understanding past trends and patterns, organizations can develop more accurate models for predicting future outcomes.

Limitations of Legacy data

Legacy data can pose many limitations for organizations in terms of its accessibility and utilization. Some of these limitations are:

  • Inability to access legacy data in its original format as the underlying technology is outdated or unavailable. This can make it difficult to interpret or process the data.
  • Legacy data can be stored in multiple formats, which can be difficult to harmonize and integrate with the current systems.
  • Legacy data often includes significant amounts of redundant or irrelevant information, making it difficult to extract meaningful insights.
  • Legacy data can be difficult to maintain and secure as the systems used to store the data may be outdated or no longer supported.
  • Legacy data can be inaccurate due to errors in data entry or data corruption over time. This can lead to incorrect conclusions or inaccurate results.

Other approaches related to Legacy data

Introducing other approaches related to Legacy data, the following should be taken into account:

  • Data Migration - transferring data from an outdated format to a modern approach. This process involves moving data from one environment to another, usually from an older system to a newer one, while preserving the data's integrity and preserving its content.
  • Data Archiving - storing data in an off-site location, with the ability to access it if needed. This allows businesses to keep older data around, while freeing up space and making sure the data is secure.
  • Data Conversion - converting data from one format to another. This is often done to make data more accessible or to make it easier to use.
  • Data Retention - keeping data for a certain period of time to comply with regulations or for other reasons. This ensures that important data is not discarded and can be used in the future.

In conclusion, data migration, archiving, conversion, and retention are all important approaches related to legacy data. These approaches help ensure that data is stored securely, remains accessible, and is usable for the future.


Legacy data — recommended articles
Technological environment — New technologies in management — Information gap — Knowledge tree — Marketing information system — Computer department — Classification of information — Changes in technology — Core process

References

Footnotes

  1. ↑ Rodriguez J.B., Gómez-Pérez A. (2006)
  2. ↑ Roth M.T., Schwarz P.M (1997)
  3. ↑ Thiran P., Hainaut J.L., (2001)
  4. ↑ Roth M.T., Schwarz P.M (1997)
  5. ↑ Thiran P., Hainaut J.L., (2001)
  6. ↑ Thiran P., Hainaut J.L., (2001)
  7. ↑ Thiran P., Hainaut J.L., (2001)
  8. ↑ Roth M.T., Schwarz P.M (1997)

Author: Joanna Zawiślan

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