Data platform definition

Today, data markets are growing rapidly with AI, IoT, and machine learning, requiring solutions that enable data collection, storage, cleaning, transformation, and analysis, as well as better data governance. All of this data requires data centers of significant size. Data platforms can include both hardware and software components.
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What is a data platform?

A data platform or EDP (Enterprise Data Platform) is a technological solution that enables the collection, storage, cleaning, transformation, analysis, and governance of data. A modern data platform provides the tools organizations need to preserve data quality and increase its perceived value. More specifically, data platforms prevent the creation of information silos and can provide actionable insights, deliver self-service analytics, streamline automation, and optimize artificial intelligence (AI) applications. Data platforms have now become the driving force behind information systems.
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Key concepts and how a data platform works

The strength of a data platform lies in its integrated architecture, combining hardware and software to create a unified data management ecosystem. It acts as a hub, bringing together all of the company's data regardless of its source.

The data platform operates around five interconnected layers:

● Ingestion. Data is collected from multiple sources using connectors for each type of system

● Storage. The use of appropriate technologies depending on the nature of the data—for example, relational databases or data lakes—optimizes the cost-performance ratio.

● Processing. Smart orchestration of data flows via automated pipelines accelerates data transformation, enrichment, and validation.

● Analysis. Business intelligence, machine learning, and predictive analytics tools quickly reveal actionable insights.

● Governance. Applying security and compliance standards and managing the data lifecycle ensures data confidentiality and protection.

This enhances business scalability and enables companies to respond to specific changes and needs as data volumes increase.

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Data platform features: optimizing its architecture

To avoid data silos, the EDP architecture is designed to enable interoperability with other systems through intuitive interfaces and to adapt to increasing data volumes through the widespread use of AI and big data. It therefore supports data collection, storage, processing, and management within an organization. Implementing an effective data platform architecture requires collaboration between different departments: IT, security, and data analysts.

Data management

Data is essential to the functioning of a business in order to promote productivity, efficiency, and decision-making. Data management, which relates to the chain of architecture for collection, storage, security, and processing, enables the dissemination of data.

Data processing

To better stimulate its development, a business necessarily needs a data processing process. With data platforms interacting with business applications, a huge amount of data is processed, and this data must be ratified to become analyzable and usable information in connection with the company's strategic decisions. It is important to note that processing enables the cleaning, transformation, and qualitative validation of data.
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Key challenges for a modern data platform

Faced with the exponential growth of data generated by AI, big data, etc., extraction and exploitation present major challenges for most companies around the world. Many companies rely on the data they collect to improve their decision-making and bring it closer to their targets. In addition to this strategic challenge, the innovation and agility that data platforms can provide ensure optimal flexibility and adaptability for companies that need to constantly reinvent themselves and adapt to market uncertainties.

Business and technical challenges

Like any business, data platforms face major challenges such as competition and the ever-changing market in the face of an increasingly complex and demanding digital transformation. For consulting purchases—often a complex maze of stakeholders, scattered processes, and tight budgets—going digital can mean the difference between lagging behind and leading the pack. Implementing AI-powered tools, predictive analytics, and real-time reporting is not just about streamlining processes. It's about making smarter decisions, reducing costs, and transforming consulting from an expense into a strategic advantage.

Performances & objectives

Nowadays, many data platforms are cloud-based, with collection, processing, and storage capabilities that provide high-quality data in exponential volumes and in real time. Not only is it possible to collect data from IoT devices and machines on production lines, but also to acquire information from supply chain management, quality control, and even end-customer service satisfaction. Needless to say, everything happens in real time with the sole objective of overall efficiency, speed, and reliability in business decisions.
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Data center colocation vs Cloud: what are the advantages?

The growing digital transformation is putting pressure on many companies, forcing them to choose between a colocation platform in a data center or a Cloud computing solution. With the main objective being to improve decision-making performance and reduce costs, it is crucial for any company to better define its strategy in order to choose the right data platform model. It is difficult to talk about advantages, as these depend on the size and capacity of the company and its objectives. Colocation allows companies to retain ownership of their infrastructure with secure hosting, while cloud hosting may appear to be a more flexible and elastic solution for the company.
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Choosing the right data platform

The choice of data platform is still linked to the type of company, its capacity, and its short- and long-term objectives. But regardless of their size, almost all companies want to make better use of their data through a data-driven approach. There are often several technological choices available, and sometimes they are even complementary. It is necessary to seek expert advice to choose the platform that best meets your expectations. All other things being equal, the company's maturity in terms of data governance must be taken into account when choosing the future platform. Indeed, the level of skills required for proper administration can vary greatly from one platform to another.

Selection criteria

The criteria for selecting data platforms depend on the infrastructure and intrinsic maturity of the company. Technically, a variety of approaches to integration (ETL), modeling interoperability, functionality, etc. could be considered when making a choice. It is therefore recommended that companies and decision-makers seek guidance in order to choose the right platform.

Use case

Data privacy and governance (Source : CDP)

«Customer expectations regarding how a brand manages and stores their data have increased significantly since the COVID-19 pandemic. Customers want you to allow them to use their data, and they want to know how it will be used. Regulations such as the GDPR and CCPA ensure that their wishes are taken into account. The good news is that customers are happy to approve the use of their data, as long as they get something valuable in return. Since CDPs provide a single source of truth for customer data, they are the ideal solution for achieving and maintaining regulatory compliance.»

UltraEdge support

Make it easier to analyze your data to grow your business. At UltraEdge, we help you implement a process for choosing the right data platform with hosting in secure, nearby data centers.