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iTTiGloss: Dark Data

Friday, 31 January 2014 iTTi, Innovation & Technology Trends Institute Posted in iTTi Gloss

Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value. 

 

Source: Gartner IT Glossary 

 

In fact, it might be more properly termed «dusty data»”. It’s that neglected data that accumulates in log files and archives that nobody knows what to do with. Although it never sees the light of day, no one feels comfortable destroying it because it might prove useful someday.

 

Source: “What Is Dark Data?”. Elizabeth Gaines. SAP-BUSINESS INNOVATION. 10/2012.

 

Dark data is usually defined as data that is kept “just in case” but hasn’t (so far) found a proper usage, or can be harvested and leveraged beyond its primary (intended) usage.

 

Source: IT Briefcase. 04/2013.

 

 

The issue of Dark Data (DD) -or perhaps better “Data in the Darkness”- is really important, is getting increasing attention (see as an example, “Dealing with Information Growth and Dark Data – Six Practical Steps”) and deserves further research. The definitions above, combined, might result in the following key ideas: 

  • DD are Big Data resulting from logs and archives nobody knows yet what to do with.

  • DD produce cost (storing and securing) and risks (IP piracy).

  • They are kept, in certain cases, for compliance purposes; usually well beyond the legal retention periods.

  • Destroying them might prove difficult -if not impossible- given the probably high number of copies (e.g.: for backups).

  • When analytics (BI, data mining) become more cost effective DD might prove useful.

  • In any case, organizations with processes in COBIT 5 PAM (based on ISO/IEC 15504-2) Level 4 (Predictable Process) or Level 5 (Optimizing Process) are currently in the need to exploit some DD with the appropriate analytic tools. 

 

Related perspective(-s)

1.- iTTi Gloss: Big Data

2.- iTTi Gloss: Social Business

3.- "Big Data": ¿un nuevo concepto?

 

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