Unstructured data

Source: Wikipedia, the free encyclopedia.

Unstructured data (or unstructured information) is information that either does not have a pre-defined

ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged
) in documents.

In 1998,

Merrill Lynch said "unstructured data comprises the vast majority of data found in an organization, some estimates run as high as 80%."[1] It's unclear what the source of this number is, but nonetheless it is accepted by some.[2] Other sources have reported similar or higher percentages of unstructured data.[3][4][5]

As of 2012[update],

zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010.[6] More recently, IDC and Seagate predict that the global datasphere will grow to 163 zettabytes by 2025 [7] and majority of that will be unstructured. The Computer World magazine states that unstructured information might account for more than 70–80% of all data in organizations.[1]

Background

The earliest research into

Big Data in the late 2000s led to a heightened interest in the applications of unstructured data analytics in contemporary fields such as predictive analytics and root cause analysis.[11]

Issues with terminology

The term is imprecise for several reasons:

  1. Structure, while not formally defined, can still be implied.
  2. Data with some form of structure may still be characterized as unstructured if its structure is not helpful for the processing task at hand.
  3. Unstructured information might have some structure (semi-structured) or even be highly structured but in ways that are unanticipated or unannounced.

Dealing with unstructured data

Techniques such as

text analytics provide different methods to find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual tagging with metadata or part-of-speech tagging for further text mining-based structuring. The Unstructured Information Management Architecture
(UIMA) standard provided a common framework for processing this information to extract meaning and create structured data about the information.

Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication.

word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, ...) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data".[13] For example, an HTML web page is tagged, but HTML mark-up typically serves solely for rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page. XHTML
tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms.

Since unstructured data commonly occurs in

document management system which can categorize entire documents is often preferred over data transfer and manipulation from within the documents. Document management thus provides the means to convey structure onto document collections
.

Search engines
have become popular tools for indexing and searching through such data, especially text.

Approaches in natural language processing

Specific computational workflows have been developed to impose structure upon the unstructured data contained within text documents. These workflows are generally designed to handle sets of thousands or even millions of documents, or far more than manual approaches to annotation may permit. Several of these approaches are based upon the concept of online analytical processing, or OLAP, and may be supported by data models such as text cubes.[14] Once document metadata is available through a data model, generating summaries of subsets of documents (i.e., cells within a text cube) may be performed with phrase-based approaches.[15]

Approaches in medicine and biomedical research

Biomedical research generates one major source of unstructured data as researchers often publish their findings in scholarly journals. Though the language in these documents is challenging to derive structural elements from (e.g., due to the complicated technical vocabulary contained within and the domain knowledge required to fully contextualize observations), the results of these activities may yield links between technical and medical studies[16] and clues regarding new disease therapies.[17] Recent efforts to enforce structure upon biomedical documents include self-organizing map approaches for identifying topics among documents,[18] general-purpose unsupervised algorithms,[19] and an application of the CaseOLAP workflow[15] to determine associations between protein names and cardiovascular disease topics in the literature.[20] CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.[20]

The use of "unstructured" in data privacy regulations

In Sweden (EU), pre 2018, some data privacy regulations did not apply if the data in question was confirmed as "unstructured".

GDPR
came into force in 2018. GDPR does neither mention nor define "unstructured data". It does use the word "structured" as follows (without defining it);

  • Parts of GDPR Recital 15, "The protection of natural persons should apply to the processing of personal data ... if ... contained in a filing system."
  • GDPR Article 4, "‘filing system’ means any structured set of personal data which are accessible according to specific criteria ..."

GDPR Case-law on what defines a "filing system"; "the specific criterion and the specific form in which the set of personal data collected by each of the members who engage in preaching is actually structured is irrelevant, so long as that set of data makes it possible for the data relating to a specific person who has been contacted to be easily retrieved, which is however for the referring court to ascertain in the light of all the circumstances of the case in the main proceedings.” (CJEU, Todistajat v. Tietosuojavaltuutettu, Jehovan, Paragraph 61).

If personal data is easily retrieved - then it is a filing system and - then it is in scope for GDPR regardless of being "structured" or "unstructured". Most electronic systems today,[as of?] subject to access and applied software, can allow for easy retrieval of data.

See also

Notes

  1. ^ Today's Challenge in Government: What to do with Unstructured Information and Why Doing Nothing Isn't An Option, Noel Yuhanna, Principal Analyst, Forrester Research, Nov 2010

References

  1. ^ Shilakes, Christopher C.; Tylman, Julie (16 Nov 1998). "Enterprise Information Portals" (PDF). Merrill Lynch. Archived from the original (PDF) on 24 July 2011.
  2. ^ Grimes, Seth (1 August 2008). "Unstructured Data and the 80 Percent Rule". Breakthrough Analysis - Bridgepoints. Clarabridge.
  3. ISSN 0268-4012
    .
  4. ^ "The biggest data challenges that you might not even know you have - Watson". Watson. 2016-05-25. Retrieved 2018-10-02.
  5. ^ "Structured vs. Unstructured Data". www.datamation.com. Retrieved 2018-10-02.
  6. ^ "EMC News Press Release: New Digital Universe Study Reveals Big Data Gap: Less Than 1% of World's Data is Analyzed; Less Than 20% is Protected". www.emc.com. EMC Corporation. December 2012.
  7. ^ "Trends | Seagate US". Seagate.com. Retrieved 2018-10-01.
  8. ^ a b Grimes, Seth. "A Brief History of Text Analytics". B Eye Network. Retrieved June 24, 2016.
  9. ^ Albright, Russ. "Taming Text with the SVD" (PDF). SAS. Archived from the original (PDF) on 2016-09-30. Retrieved June 24, 2016.
  10. ^ Desai, Manish (2009-08-09). "Applications of Text Analytics". My Business Analytics @ Blogspot. Retrieved June 24, 2016.
  11. ^ Chakraborty, Goutam. "Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining" (PDF). SAS. Retrieved June 24, 2016.
  12. ^ "Structure, Models and Meaning: Is "unstructured" data merely unmodeled?". InformationWeek. March 1, 2005.
  13. ^ Malone, Robert (April 5, 2007). "Structuring Unstructured Data". Forbes.
  14. S2CID 1522480
    .
  15. ^ a b Tao, Fangbo; Zhuang, Honglei; Yu, Chi Wang; Wang, Qi; Cassidy, Taylor; Kaplan, Lance; Voss, Clare; Han, Jiawei (2016). "Multi-Dimensional, Phrase-Based Summarization in Text Cubes" (PDF).
  16. S2CID 31449783
    .
  17. PMID 26420781
    .
  18. PMID 23554924
    .
  19. PMID 25411329
    .
  20. ^
    PMID 29775406
    .
  21. ^ "Swedish data privacy regulations discontinue separation of "unstructured" and "structured"".

External links

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