In companies, more and more users are trying to access data, which unfortunately will contributea to a greater burden on the central IT department and data teams. Test carried out by Informatica w 2022 showed that 52% of data leaders indicated an increase in data integration workload of 10-20% annually. In the current economic environment, we are involved in more and more activities. This can lead to delays or halts in projects.
The competency gap is deepening along with the ever-changing technological landscape. In fact, according to research Informatica 64% of companies indicate the problem of losing qualified employees. Today's data engineers must possess a combination of business knowledge and technical skills to create effective data flows. Unfortunately, this can sometimes lead to a discrepancy between what the business needs and what the IT department delivers. Additionally, without proper automation, teams can spend most of their time on repetitive and monotonous integration tasks instead of developing strategies and optimizing connections.
According to a recent study by IDG, more than 20% of companies use 1000 or more data sources to power their analytics and business intelligence systems. This data comes from different applications and comes in different forms and formats (structured, unstructured). Although technically having all this data, finding and obtaining it in a uniform way is not easy. In order to be able to analyze it, it is necessary to define them, transform them through the ETL process (extract, transform, load) or ELT (extract, load, transform) and then transfer without violating compliance requirements. It is a complex and time-consuming process.
Transferring significant amounts of data between the cloud and on-premise systems is costly. However, the right tools for cloud data integration offer solutions that not only make better use of cloud infrastructure resources for data processing, but also bring benefits in terms of efficiency. Thanks to this, trade-offs between costs and performance can be avoided, which means financial savings for the organization while increasing the speed of data processing.
As the cloud grows in popularity, data and IT leaders may find it difficult to integrating applications, both within one and many cloud platforms, with the existing local infrastructure. Combining these unrelated systems can lead to self-integration, rescheduling, project overruns, and inconsistencies in data management and data quality – which is not beneficial in today's fast-paced business.
Security is an important consideration when it comes to moving data from one system to another. It is important to control the number of users and determine their level of access to ensure the protection and proper management of data. Unauthorized access and misuse of data can damage the company and its reputation, leading to financial losses.
Here's a quick overview of the different functionalities that a suitable cloud data integration solution should offer to avoid common challenges and drive your organization's growth.
ETL is a traditional way of data processing where data from various sources - including SaaS applications or on-premises systems - are transformed before being imported into the target application, such as an enterprise resource planning (ERP) system or Data Warehouse for analysis and business processes. Creating processes and dynamic mappings for the data flow can bring a high level of automation and help improve productivity. In the modern cloud, data lake and Lakehouse, data engineers prefer the process ELTwhere commands are executed directly on the target system. They use its computing power instead of moving data to and from the data warehouse for processing. This improves data flow efficiency and optimizes costs.
Faced with an increasing number of data sources, it is essential to choose a tool that is not only compatible with current data sources, but also able to adapt to new applications and technologies. You need a scalable platform that can work with different ecosystems, both in hybrid and multicloud environments.
With large data volumes and unpredictable data workloads, the right data integration tool should be able to process virtually any type of data, regardless of its size, without sacrificing performance. Data integration solutions that support massively parallel processing, Spark processing, or elastic processing can process multiple terabytes of data simultaneously. This allows us to save more time and money.
An important issue is transparency regarding the flow of data, from its source, through any changes it has undergone, to where it is delivered. Data tracking helps detect anomalies and optimize performance. In addition, full-route data tracking tools can help you maintain a competitive advantage.
Cloud data integration powered by artificial intelligence can reduce the time it takes for data flows to develop and operate. This translates into time savings for architects, business analysts and IT departments. Available self-service AI tools, such as multi-step wizards for data ingestion and integration tasks, are easy to use, even for non-technical people. What's more, they eliminate the need to write manual code, which can be time-consuming and expensive when creating and running dataflows. AI based recommendation engines on the principles of financial operations in the cloud (FinOps) can help optimize costs by suggesting processes, templates and configurations.
In order to facilitate infrastructure management, data experts are looking for serverless solutions for data integration that does not require any effort to provide and maintain a cloud instance. Thanks to this, analysts and data architects can focus on their main work, and not on administrative activities. Having a modern data integration tool cannot be missed in today's demanding environment. A solution that can help you lower costs, improve efficiency and compete effectively can be the difference between achieving success and maintaining the status quo.
In the past, many data integration tools were limited. And some current solutions are still only focused on a specific use case or solving a single problem. In today's digital age, the right solution must provide a full range of cloud data integration options in a secure, stable and integrated cloud environment that supports hybrid and multicloud configurations. This is where it comes in Informatica Cloud Data Integration, Intelligent Data Management Cloud (IDMC). Informatica Cloud Data Integration is a comprehensive cloud data integration solution. For data management and analysis, it is able to download, enrich and transform virtually any data from various sources, both in a multicloud environment and on local servers. The solution offers efficient ETL, ELT processing, importing, synchronization and replication in a multicloud and serverless environment. It covers a variety of patterns, use cases and users, providing well-designed and automated data flows that serve business growth.
Source: https://www.informatica.com/blogs/data-integration-challenges-and-problems.html
