The Dilemma of structured and unstructured data for data Protection and Data Privacy Compliance

The Dilemma of structured and unstructured data for data Protection and Data Privacy Compliance

The mass data categorisation is central to data and IT Security GDPR Compliance. Structured data and unstructured data affect the speed of assimilation as well as information recall. During data onboarding (collection) combine the unfamiliar, and disparate personal and privacy data for GDPR compliance

Structured data refers to information with a high degree of organisation,  i.e. such that inclusion in an interactive database that is seamless and readily searchable by simple, straightforward search engine algorithms. Unstructured data is fundamentally the exact opposite.

• The lack of structure makes the assembling data a time and energy-consuming task.
• Structured data is more expensive; however unstructured data adds costs to the organisation

The Problem with Unstructured Data

It can be possible or achievable to transform unstructured data to structured data and then creating intelligence from unstructured data with data mining techniques and through the use of metadata

Unstructured data is (roughly speaking) is usually for people, who do not easily interact with information in a strict, structures, excel oriented database format.

Email is an example of unstructured data; the ever busy inbox of any corporate manager might be arranged by date, time or size by outlook; if it was configured properly it is fully structured, and arranged by exact subject and content. That is why any unstructured outlook is impractical.

Unstructured data has its own non conformist internal structure

Spreadsheets, are structured data, that can be scanned for information because it is appropriately arranged in a relational database system. The problem in that case id that unstructured data presents is one of volume, requiring a considerable investment of resources to examine through and extract the necessary elements, as in a web-based search engine.

Data harvesting technology employs multiple threads to mass-harvest scalable quantities of unstructured data through customisable filters and gets qualified results in a searchable database based on customizable facets (URL, filetype, source category, people mentioned, places mentioned, companies mentioned, custom keywords, etc.)

Start the data structure journey

Structured and unstructured data are both used extensively in big data analysis. Traditionally, because of limited processing capability, inadequate memory, and high data-storage costs, utilising structured data was the only means to manage data effectively.

Cheap availability of storage and the sheer number of multiple data sources with two data formats to identify the different structured and unstructured data. Unstructured data analytics sources have skyrocketed in use due to the increased use and demand for big data.

 Structured Data is deciplined and not easy

Structured data is highly organise information that is uploaded neatly into a relational oriented rational databases (row structures in a database), with fixed fields, and detectable via search operations or algorithms.

Structured data can therefore leave out large amounts of data/material that simply does not fit into a firm’s organisation of information. With the improvement of programs and software for systems processing the costs for data storage are lowered cost and then the spread of new formats and categories of data, the age of unstructured data began.

For nontechnical business users and data analysts, he fundamental challenge of unstructured data sources are that they are difficult to unbox, understand, and prepare for analytic use that goes beyond the issues of structure, and the sheer volume of this unruly type of data. Because of this situation, data mining techniques often leave out valuable information and make analysing unstructured data both lengthy and expensive.

Start your structured data management solution journey by applying big data to your unique business goals with these simple resolutions;

1. Index everything
2. API-enable data
3. Release the data from the storage platform(s)
4. Modernise legacy applications