Consistent Access Meets Reliable Delivery: Data Integration
The growing use of cloud tools makes a robust data integration system more important than ever. Data integration encompasses the practices, architectural techniques and tools needed to consistently access and deliver data across your organization and meet data use requirements for all your apps and processes. We focus on data integration tools and cloud data integration.
When it comes to data integration, there is no blanket solution. The right setup depends on what the company needs. But there are typical use cases for data integration projects:
Efficient Use of Big Data
Data lakes can be highly complex and vast. These huge volumes of data are known as big data. The more big data pools a company has, the more there is to analyze. This makes highly sophisticated data integration solutions more and more important for many organizations.
Creating Data Warehouses
Large enterprises in particular often launch data integration initiatives to create data warehouses that combine multiple data sources into one relational database. With a data warehouse, you can run queries, create reports and analyses, and retrieve data in a standardized format.
ETL (ETL = extract, transform, load) is a sub-stage of integration where data is extracted from the source system and transferred to the warehouse. This continuous process sees data from a range of sources transformed into usable, standardized information before it’s applied for business intelligence and analysis purposes.
Optimizing Business Intelligence (BI) Processes
Data integration simplifies BI processes by creating a standardized view of data from disparate sources, making it much easier for organizations to view available data sets and extract meaningful information. Data integration allows analysts to leverage larger volumes of information for more accurate evaluation, all without being slowed down by the massive number of data sets.
Cloud and Hybrid Integration
Enterprises are increasingly using applications both in the cloud and in their own data centers. Data from these applications must be integrated and connected beyond the cloud.
Consistent, accurate, and unambiguous master data are the cornerstones of digitization. Data quality is an essential intangible asset. No matter your data issue, we support you throughout the process. To this end, we use initiatives such as Master Data Management (MDM), Data Fabrics and Data Lakes. Improving data quality means ensuring that business processes can be executed efficiently and accurately.
Customer experience is the driving force behind revenue and customer loyalty. In this context, data creates a directly measurable added value. We consider how data can be effectively processed, maintained, and presented to the customer. We use initiatives such as Product Information Management (PIM), Customer Data Platform (CDP), Digital Asset Management (DAM) and Publishing.
AI & Analytics
Making good decisions requires new analytic techniques and AI/ML. We help our customers make data-driven decisions by processing data with artificial intelligence (AI) and making them accessible in advanced analytics tools. Based on leading AI and cloud frameworks.