In layman terms, unstructured data does not have any identifiable structure. So what does unstructured data include then? Yes. Bitmap images, objects, text, and other types of data that do not form a database. Most of the data generated by enterprises today are unstructured. For example, an email is an unstructured data. The email messages are properly organized in an exchanging server, the body of the email message involves free-flowing text without any particular structure.
Types of Unstructured Data
Raw and unorganized data stored by organizations is known as unstructured data. Ideally, every piece of information would be converted into definite structured data but the process will be time consuming and expensive. Moreover, you cannot convert every type of unstructured data easily. For instance, the email includes information like time sent, subject line, and sender; these are uniform fields however you cannot dissect the message content and categorize it separately. Hence, you will be faced with compatibility related to the database structure.
Following are the various forms of raw/unstructured data:
- Files processed by MS-Word
- PDF files
- Digital images
- Audio and video files
- Social Media Posts
Can you find a common element in the list above? The link between these file types is that you can store these files without bothering about their format. Storing these files is possible in an unstructured manner since the content is unorganized in these files.
Big data is continuously evolving and so is the problem of proper utilization of unstructured data by enterprises. Anyway, technologies are being developed to solve this problem. Excerpts from Darin Stewart’s blog for Gartner states, “The age of information overload is slowly drawing to a close. Enterprises are finally getting comfortable with managing massive amounts of data, content and information. The pace of information creation continues to accelerate, but the ability of infrastructure and information management to keep pace is coming within sight. Big Data is now considered a blessing rather than a curse.”
Unstructured Data vs. Semi-structured Data
Semi-structured and unstructured data are terms having different context and meaning. You cannot store unstructured data into rows and columns; typically, it is stored as a BLOB (binary large object), in most relational database management systems. The term ‘unstructured data’ may also refer to randomly repeating columns varying from one row to another within a document or a file.
Many types of data conform to a standard pertaining to metadata. What do you mean by metadata? It includes information of author, time of creation, etc., which is stored in the relational database. Therefore, metadata is more accurately the semi-structured data, however no consensus has been reached yet.
Enterprises can use the unstructured data to acquire knowledge about future trends. Forecasting aligns with business intelligence and analytics when enterprises measure their business performance. Collecting useful business insights provides them important data to arrive at a complete BI solution.
Problems With Semi-structured or Unstructured Data
Enterprises face several challenges to develop business intelligence using semi-structured data.
- Physical access - Data is stored in various formats.
- Terminology – No standardized terminology to describe unstructured data.
- Voluminous data – Majority of the data is semi-structured; there is a huge needs for semantic analysis of this data.
- Searchability – Search results return links having just a reference to the precise term. For example, if you search ‘felony’ the search engine will return links where the term is used as a reference point. But it is not enough; references to arson, crime, murder etc. are not returned.
Searchability and data assessment issues can be handled only if you want to know about the file content. Context is added to the content with the help of metadata.
Many systems capture metadata like filename, size, author, etc.), however, it is more useful to search for metadata related to the actual content. Examples include topics, summaries, people or companies.
Metadata means ‘data about data’ i.e. data provides information about one or more aspects of the complete data. It may tell us about how the data was created, its purpose, when was it created, the author, location from where it was created, and the typical standards followed to create that data.
Structural metadata refers to specifications and design of the data structures, also known as containers of data. Descriptive metadata refers to individual examples of applying data to the actual content.
Let us take the example of a digital image. It may include metadata describing the image size, color depth, resolution of the image, date of creation, and other information. The metadata of a text document will contain details about its length, size, author, date of creation, and a brief summary.
Metadata is stored and managed from a metadata repository or a metadata registry. Unless you add context and a reference point, it will be difficult to identify the metadata in the search engines.
For instance, you have a database including several 13-digit long numbers; they may be an output of lengthy calculations or an equation. You can perceive the numbers without any specific context. However if the context states that the database is related to a book collection, then the 13-digit long numbers will be the book’s ISBN number which gives relative information about the book and not the entire information about the book’s content.
Philip Bagley coined the term ‘metadata’ in 1968, where he used the term in its traditional sense i.e. structural metadata.
Extracting Information From Unstructured Data Using Natural Language Processing
Natural Language Processing (NLP derives structure from unstructured data. NLP identifies sentence, paragraph, and word boundaries of a text document. It also deals with ambiguity of languages. For example, a sentence reads: “I found my wallet near the bank.” Now, the NLP provides most likely interpretation giving sufficient context- i.e. clear distinction of whether the term ‘bank’ refers to the bank of a river or a financial institution.
Some of the common tasks performed by a Natural Language Processing system are as follows:
- Segmentation: Divides the sentence into segments to identify the end of one sentence and beginning of the other one. Punctuation marks are seen as sentence boundaries; however several exceptions are there as well. For example, ‘He said: “Hi! What’s up—Mr. President?” is deciphered as a single sentence.
- Tokenization: Identifies individual words, numbers, and single coherent constructs. Twitter hashtags comprise of alphanumeric and special characters which the NLP treats as a single coherent token.
- Stemming: Search engines strip the ‘ending’ of words to retrieve documents with greatest hits.
- Part-of-Speech (PoS) tagging: Each word of the sentence is assigned its respective part of speech like a noun, verb, or adjective.
- Parsing: The syntax structure of the sentences is derived with the help of a NLP. Parsing is used a prerequisite for other tasks like named entity recognition.
- Named entity recognition: The NLP system identifies entities like persons, locations, and times mentioned in the documents. After introducing the entity in a text, the language uses references like ‘he, she, it, them, etc. instead of using fully qualified entities. References attempt to identify multiple mentions of an entity within a sentence or document and mark them in the same instance.
Unified Insights on Structured and Unstructured Data
Your team of data scientists must focus on the following three key attributes:
- Speed and scalability: Software development involves huge volume of data sets including multiple query processing. Data science is an iterative process. The data requires cleansing; codes need to be adjusted for improving accuracy and other additional steps can help you develop an agile development process.
- Unified location for data processing: Analytics faces problems due to data silos’ assets and separate analytical tools for different types of data. When you receive the data, how do you analyze it? Executing analytics on a personal laptop is not sufficient. A unified location like a cloud data server or data lakes will support analysis of structured as well as unstructured data without silos.
- Support for analytics tools: Data lakes must offer able support to programming languages and analytical tools. e.g. ETL, SQL, , PL/Python, PL/R, PL/Java, Mahout, MADlib, Spring Data, Spring XD, Graphlab, Open MPI, MapReduce, Pig, Hive, etc. in this way, data scientists will be provided with an easy and cost-effective use of the existing code on new software platforms. In addition, data scientists will have access to the rights tools. Open-source technologies will enable free sharing of data science libraries which will boost business analytics.
Natural Language Processing as a Gateway to Understanding Unstructured Data
The natural language creates data and knowledge; it deals with daily use of Facebook, spreadsheet notes, or comments/reviews during online shopping; everything comprises of data extracted using natural language.
Once data starts resembling how humans write, talk or behave, the more important Natural Language Processing will become. The knowledge acquired will be translated into new terms that will be appropriate reference points for the data. The enhanced NLP systems have capabilities of creating rich web experiences with a wide variety of information from all types of unstructured data, irrespective of its source or format.