Garbage management

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Data garbage management basically deals with data that is no longer required or redundant , but still stored, which has become (economically) useless for private individuals, companies and institutions. In this context, it is questionable whether their permanent deletion represents the ultima ratio .

For many companies and institutions, junk data and the amount of superfluous information is a problem.

overview

The database is increasing worldwide:

End of 2010: ≈ 970 exabytes

End of 2011: ≈ 1.8 zettabytes (IDC)

End of 2020: estimated around 35 to 40 zettabytes

In addition, around 80 percent of the data in companies worldwide is in unstructured form . For companies, garbage data is not only a question of costs, but also a problem of efficient organization.

Garbage data leads to unnecessary workload for employees in companies and institutions. Resources are being wasted. The following examples should make this clear:

When managing their own e-mails alone , an employee needs an average of two days a year to delete spam e -mails. The percentage of spam e-mails (advertising, etc.) in worldwide mail traffic on the Internet is around 70 percent. In 2008, the estimated number of spam emails worldwide was over 100 billion per day.

In the project documentation of a company, around 70 to 90 percent of the files are no longer required at the end of the project, as they are only interim statuses. In addition, most project files are stored multiple times, e.g. B. within Sharepoint and on various data carriers that are located in the users' end devices.

With regard to the retention of data and documents by a company, statutory retention requirements must be observed: the German Commercial Code (HGB) for all merchants and the Tax Code (AO) for all those who are required to keep accounts regulate the respective retention requirements. So are z. B. To store business documents in accordance with Section 257, Paragraph 1, Clause 1 of the German Commercial Code and in accordance with Section 147, Paragraph 1, Clause 1 of the AO so that they can provide an informed third party with an overview of the business transactions and the company's situation within a reasonable period of time.

In addition, storage costs have fallen dramatically over the past 35 years. In 1988 1 GByte cost around 12,000 dollars, today only a few cents. Nevertheless, the expenditure for storage capacities in the IT budgets of companies has risen continuously over the past few years.

A typical starting point in a company

The following characteristics indicate garbage data in a company or institution:

1. High data growth per year
2. Increasing annual storage purchases and increasing IT budgets for storing data.
3. The proportion of unstructured data in the company is (too) high.
4. Data is kept multiple, redundant and inconsistent with one another in the company.
5. Poor finding of the correct files (information). The current versions are sometimes no longer found or only after a long search time.
6. Employees in companies spend too much time handling garbage data, e.g. B. Deletion of Spam Emails.
7. Data objects and emails have no expiration date, no data lifecycle management .
8. Too high costs for the storage of data garbage (data backup, power consumption, etc.).
9. The waste data management process in the company is missing; also professional data management.
10. The role of a data waste manager (“data waste officer”) is not established.
11. Storage media are not disposed of professionally (with the consequence of espionage risks, compliance violations ).

Objectives of a data garbage management process in the company

transparency

• Transparency about the garbage data in the different processes should be created.
• There should be an overview of the sub-processes and data in which significant amounts of data garbage are already present and will continue to arise in the future.
• Transparency is to be achieved especially about the volume of data trash induced by spam email: per employee and in the company as a whole.
• The aim is to provide an overview of the garbage data that results from the introduction of new technologies (such as images, language, films, social platforms, etc.).
• The data garbage volume saved resulting from ongoing improvement measures should be recorded.
• The corporate data management and its data objects are to be expanded to include properties such as the expiry date of data objects as well as the type of disposal and dependency on other data objects that represent data garbage.

Calculation of costs and cost transparency

• The costs for processing spam e-mails and also unproductive e-mails (e.g. "cc-mails" or "note-taking e-mails" from your own company) per employee and in the company as a whole should be determined.
• The costs of maintaining a garbage management process should be shown.
• The costs for the new measures to be carried out annually to reduce data garbage should be presented.
• The total annual data garbage volume in relation to the total data volume should be recorded.
• The total annual data garbage volume and approximately the costs for the storage (including backup) of the data garbage volume should be determined.
• The potential data garbage savings volume in the current financial year and at least for the following financial year should be shown.

Controlled garbage disposal

Indispensable objective for companies and institutions: Achieving sustainable data garbage disposal. Ensuring (also to meet possible compliance requirements) that the newly generated data garbage is identified as automatically as possible in the company processes and disposed of safely and sustainably.

Benefit potential

With the introduction of a professional data garbage management process, the following potential benefits for companies and institutions can be achieved:

• Lower cost increases due to sustained fewer storage purchases per year.
• Contribution to “Green IT” through sustainably lower power consumption for the operation of storage systems.
• Find the right information faster and make fewer mistakes using incorrect file versions.
• Clear location of files by avoiding unnecessary multiple storage of files (data backup is OK).
• Greater security when using and exploiting the rights of files (e.g. photos, films, scientific texts).
• Improved use of labor resources by improving effectiveness (no release).
• Complexity reduction and better controllability of the entire data backup process.
• Sustainable release of hidden reserves in the existing storage systems (for improved storage utilization) and more efficient use of existing IT resources.
• Medium to long-term: data garbage as »raw material«, a sale of reusable data to other IT communities is conceivable.

Consequences from the flood of data

The consequences are now known in large parts of society (private individuals, companies, public authorities). The technology for processing data, information and knowledge is becoming more and more complex, expensive, slower and more error-prone. This is also reflected in the strategy concepts of the storage systems of the manufacturers, who are often also providers of backup and archive solutions. Brilliant business can be generated from this development. The costs of storing garbage data will continuously increase and are also sustainable. Software methods for data compression and targeted deduplication are becoming more and more important - an indication that data growth is advancing faster than the technological advancement of storage systems.

Possible solutions

Ultimately, the development to date shows that software and technology solutions for storing data are constantly being developed and offered, but that a comprehensive company process to curb data growth has so far been lacking. A project model for the introduction of the garbage management process is the model by Martin G. Bernhard. A project model for the introduction of the garbage data management process is presented, thus a concrete project model for the introduction of a garbage data management process. It ranges from the identification of the garbage data areas, the garbage data and the places where it occurs, to the monitoring of the garbage data areas. This project model can be used in practice for the introduction of a data garbage management process in every company and every institution. It must be tailored to the particular situation of the company or institution.

The following topics are to be dealt with in the context of this project model:

• the garbage management process with its main and sub-processes,
• the role of a garbage collector,
• Templates for collecting data garbage,
• a catalog of measures with concrete proposals for containment and short to medium-term reduction of data garbage and key figures for determining and controlling the data garbage.

See also

• Deduplication
• Green IT

literature

• MG Bernhard, D. Hirschberger: Data garbage - the new parliamentary group - No other parliamentary group is growing so quickly. In: G. Hösel, B. Bilitewski, W. Schenkel, H. Schnurer (eds.): Garbage Handbook - The Encyclopedia of Waste Management. 7 volumes. Berlin 2003. (loose-leaf collection)
• MG Bernhard, I. Trenn: The new garbage fraction of the future: data garbage. In: Garbage and Garbage. 2/2003, pp. 94-97. (dgaw.de)
• MG Bernhard, I. Trenn: The future has already begun: with garbage data. Technologie-Stiftung Hessen GmbH, Software News, March 2002.
• WL Brunner, MG Bernhard, J. Weber (eds.): Companies sink into data garbage - approaches and procedures for more efficient data management. Symposion Publishing, Düsseldorf 2011, ISBN 978-3-939707-63-9 .
• WL Brunner, J. Weber, MG Bernhard: Spam mails: data garbage in banking. In: The Bank. 11/2004, pp. 58-63.
• WL Brunner, J. Weber, MG Bernhard: Weapons against e-waste. In: Financial Institutions. 3/2008, pp. 68-71.
• R. van Gisteren, WL Brunner: Human Resources Management from the perspective of operational risks. In: Erich R. Utz (Ed.): Operational Risks. Stuttgart 2011, pp. 210-239.
• O. Kohlbrück: The network of the disappointed. In: horizon. No. 37/2011, p. 17.
• C. Kurz, F. Rieger: Die Datenfresser ,. Frankfurt am Main 2011, ISBN 978-3-596-19033-1 . (datenfresser.info)
• IDC White Paper - sponsored by EMC - The Diverse and Expploding Digital Universe - A Forecast of Worldwide Information Growth Through 2011. March 2008. (PDF; 452 kB) ( Memento from April 4, 2013 in the Internet Archive )
• M. Rosenbach, H. Schmundt: The perfect crime. In: Der Spiegel. No. 27/2011, pp. 28-38.
• H. Scheffler: New methods, new ethics. In: absatzwirtschaft. No. 3/2010, pp. 44-46.
• J. Schrey: Obligation to Retain Business Records. In: WL Brunner, MG Bernhard, J. Weber (eds.): Companies sink into data garbage. Düsseldorf 2011, ISBN 978-3-939707-63-9 , pp. 141-175.
• A.-L. Thomat, MG Bernhard: data garbage - the garbage of the future - that already exists today. In: IT Management. Dec. 2001, p. 14 to p. 20.
• B. Westphal: Use of data mining instruments in "Operational Marketing" at Landesbank Berlin. In: WL Brunner (Ed.): Success factors in bank marketing. Wiesbaden 2004, pp. 273-283.

Individual evidence

1. ↑ "in Organizations, unstructured data accounts for more than 80% of all information", quoted from: IDC White Paper, p. 12
2. ↑ ^{a b} brand one. 09/2009.
3. ↑ brand eins, 06/2008
4. ↑ WL Brunner, MG Bernhard, J. Weber (eds.): Companies sink into data garbage: Approaches and procedures for more efficient data management.