When talking about data types, it is possible to do it from different approaches. They can be identified according to their origin, the importance they have for the business or the language that will be used to work with them, among others. But one of the key classifications when approaching its use is the one that distinguishes between structured and unstructured data.

Structured data

Structured data is what is found in most databases. They are text-type files with highly organized data in table-type formats, spreadsheets or in relational databases (RDBMS).

This data is managed by a type of structured programming language, known as SQL (Structured Query Language) designed to manage the information of the relational database management systems mentioned above.

A few decades ago, structured data revolutionized the paper-based systems that companies relied on for business intelligence. While structured data is still useful, it does not have even half of the information that is available in the form of unstructured data. In general, 80% of the information relevant to a business originates in unstructured form.

Unstructured data

Unstructured data is raw, unorganized data that can eventually be structured. However, it is an expensive and time-consuming process, something that challenges the agility that the market demands.

Currently, unstructured data is the most precious asset for companies. Its origin is diverse. Some of its sources are: web pages, videos, user comments, customer call transcripts, internet images, etc.

With the exponential growth of data availability, companies have the opportunity to make a quality leap in the knowledge of their businesses. The analysis of this information can lead an organization to improve different areas of the company: marketing, sales, operations, logistics, customer service, among others.

So, we are facing a scenario in which unstructured data is revolutionizing systems based on structured data, but it implies great challenges linked to its properties: it is disorganized, it comes from very diverse sources and its storage is not something simple.

Thanks to scientific and technical development, today there is technology that allows capturing, analyzing, sharing and safeguarding this information to make it productive and perform predictive analysis based on it and thus optimize decision-making.

What is a data lake?

Data lakes were born around 2000s as a less expensive and efficient option for storing unstructured data. Although this type of data could already be stored in other previously existing formats, the purification and preparation processes were long and costly. This is how Data Lakes became the quintessential raw data storage option, without hierarchy or organization.

The central objective of Data Lakes is to generate a repository that allows large amounts of raw data to be collected in their native format so that they are available for use at the time they are needed.

Unlike Data Warehouses, which store your information in files or folders in perfectly structured and hierarchical systems, Data Lakes do not have a pre-established order. Instead, it assigns a unique identifier along with a set of metadata tags. Later, when business questions are raised, the “tagged” data can be retrieved for analysis and response.

What are the advantages of a Data Lake?

The main advantage of a Data Lake is the centralization of disparate content sources. Once assembled, these sources can be combined and processed to provide answers to questions that might not otherwise be answered.

The data is infinitely more flexible than in a structured database and is prepared according to the question you want to answer at that moment, which reduces initial processing costs and is easily scalable.

Plus, there’s no need to discard data and it’s accessible to all users who need it, regardless of their location. This property increases content reusability and helps any organization make faster, smarter decisions.

First steps in data management

Today, more than 80% of companies still carry out a large part of their data processes manually, or even do not have a comprehensive data control and management policy. Taking the first steps in that direction implies starting by automating the processes that consume more time, and beginning to centralize the data sources in one platform.

Conciliac EDM connects different data sources (extraction and transformation of files, databases, APIs, FTPs, among others) and specializes in the integration and reconciliation of information from various sources so that companies can automate their data management processes optimizing decision making with accurate and validated data.

To find out more, ask for a demo.