Big Data represents the enormous volumes of data, both structured and unstructured. The processing of this data provides companies with valuable information. While Big Data is independent of the cloud, the power and flexibility of this ecosystem are indispensable in a data-driven world.
Huge volumes of data in all formats are constantly being produced and exchanged on the Internet. Companies are constantly collecting and storing this data, and analyzing it to improve their results and performance.
That's the official line and the objective announced by companies. But what is the reality? In reality, we're talking about "Dark data"! Coined by analysts Gartner, this term defines information collected and archived with little or no use by the companies that collect it: geolocation data, IoT (Internet of Things) diagnostics, analysis reports, surveys or even HR data.
Like Big Data, Dark Data continues to grow. According to an IBM study, it accounts for some 80% of the commercial information held today.
This enormous untapped volume confirms that data management is a difficult, time- and resource-intensive task. It requires a powerful IT infrastructure, as well as specialized profiles, to guarantee successful processing and analysis.
Hence the interest in relying on the performance of the cloud, as organizations' information systems (IS) are showing their limits. However, the cloud ecosystem is not yet fully integrated by companies.
Big Data: INSEE survey
Sources Eurostat; Insee, TIC-entreprises 2018 survey.
In 2018, 77% of companies with 10 or more employees based in France and paying for cloud computing services resorted to paid cloud file storage. As this chart published by INSEE shows, the majority are content to store data, exchange emails and manage databases.
The study also stated that in countries where cloud is "a practice widely adopted by large companies (250 people or more), big data analytics is also more widespread within these large companies. In 2018, in the EU, 33% of companies with 250 or more employees carried out massive data analysis or had it carried out. In France, the figure was 37%, and in Belgium and the Netherlands, more than half".
Since that survey, the situation has evolved positively: the adoption of analytical technologies and efficient data collection processes is starting to be increasingly integrated into corporate strategy.
Exploiting Big Data
But a study by IDC tempers this shift. Business managers are all interested in big data, but they deplore the lack of an overall strategy for exploiting data.
Because before relying on the cloud, it is essential to optimize this dormant information heritage. This means preparing the data before processing it. Given the sheer volume of data involved, this phase is often extremely time-consuming, and software has emerged to automate the process.
In particular, this software facilitates integration (structuring data in a semantically coherent way), distribution (presenting data to the user, managing access rights) and, lastly, restitution (providing the most visually clear information possible).
Integration tools are essential. They enable companies to consolidate all their data in one place (or on a few specific sites), so as to benefit from an overall view.
There are many integration tools and services available. But data ingestion remains an essential part of integration, especially when it comes to ingesting large quantities of data into data warehouses (better known as "data lakes").
A data ingestion solution must therefore be able to handle all sources and types of data without too much manual oversight. Out-of-the-box connectors for data lakes such as Amazon Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery and Snowflake should be available. They should also offer data synchronization and replication capabilities.
Among the various options available, an iPaaS solution can solve the challenges of cloud integration by providing both the platform and the tools needed to host and manage this integration.
Finally, the cloud enables us to improve database management performance and security levels thanks to Machine Learning capabilities.
