web analytics

3 Questions for Unstructured Data Analysis

Unstructured data is a generic term that describes all data that is not contained in a database or some other type of data structure. Behind this generic term, there are all the texts, emails, web pages, social networking statuses, multimedia files such as sounds, images and videos, etc. It is therefore primarily of irregular data, the information cannot simply be stored in boxes in a systematic way, and unstructured term does not refer to the lack of structure, but the fact that this data have very complex and non-standard structures, where information cannot be obtained with simple queries to which we are accustomed.

It is estimated that the percentage of corporate structured data will become smaller, and the big portion of created data from our daily business activities are mainly semi- or unstructured. This trend is due to the current time when the emails, smartphones and PDF files are an essential part of the “information cloud”. The unstructured data naturally deserve as much attention as structured data, as we must also be able to use this information.

Companies may have various reasons for unstructured data analysis. For example:

  • To discover – What information and relationships are hidden there? What trends can we discover?
  • To understand – Why do people behave according to certain models?
  • To anticipate – What can we expect from certain people or groups of people on the basis of existing data?
  • To summarize – What is the essence of Text Mountain?

Why analyze unstructured data?

Unstructured data has always been present in the business environment, but they have long been in the shadow of structured data, which are easier to analyze and treat. However, recent trend has been a renewed interest, and for several reasons:

  • On the one hand the data have multiplied through automatic data collection systems and / or through user generated content.
  • Moreover, many technological and algorithmic developments suggest the information systems that can treat effectively, and in a reasonable time.

In the absence of administration, the sheer volume of unstructured data generated annually within a business can be costly in terms of storage. The information contained unstructured data are not always easy to locate. This localization in fact implies that all data contained in documents, both electronic and physical, are scanned, to allow a search application to extract analysis of concepts according to the terms used in specific contexts. This process is called semantic search.

We all use search engines enormously. We introduce a specific keyword and then hope to find information related to it. But what if you do not know what you are looking for? How to find information that you do not know yet? More and more companies want to find unknown correlations from unstructured data. For example, they want to use unstructured data to identify new patterns, relationships and concepts.

How to start the adventure?

Unstructured data offer many opportunities for diverse businesses. But it must naturally be able to analyze. CIO Insights has summarized the following 12 steps for analyzing unstructured data:

  1. Know your disparate data sources
  2. Choose method of analytics and set goals
  3. Evaluate your technology stack
  4. Real-time access Is crucial
  5. Data Lakes before Data Warehouses
  6. Prepare data for storage
  7. Ontology evaluation
  8. Retrieve useful information
  9. Statistical modelling and execution
  10. Disposition of customers
  11. Analyze most relevant customer topics
  12. Visualize your analysis

What make unstructured data exploitation possible?

Different solutions exist to isolate structured information that is present, such as names, dates or places. But you can also take a more comprehensive approach with semantic analysis models that identify the most relevant documents during a search. For example, grouped by themes, automatically associated keywords, or identified topics.

The non-text data is not left out, and it is possible now to identify images in the presence of people or certain objects, or distinguish the recording of a speech by the cry of a whale, with varying success depending on the nature and complexity of the corpus considered, and the objectives that fixed. Other examples include the cameras have learned over time to recognize human faces and systems automatic speech recognition has become increasingly reliable.

This brief overview of the opportunities offered through the management and analysis of unstructured data is far from exhaustive, but it aims to raise awareness that much knowledge is there, but is useless without the appropriate tools. Now it becomes essential to grasp, and to put in place the infrastructure and processes that draw critical information needed for the development of any organization. The technology is better controlled, and becoming friendlier to use, nothing precludes its widespread adoption.

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