Your organization may appear as a highly systematized structure to the external world. But internally, it is an assortment of data gathered from databases, files, and several other sources. This data can help your business evolve and improve, but only if you manage it efficiently. Data consolidation can help you do that!
This blog will present an overview of data consolidation and some standard data integration techniques used for consolidating data.
What is Data Consolidation?
Data consolidation is the process of combining data from multiple sources, cleaning and verifying it by removing errors, and storing it in a single location, such as a data warehouse or database. Data is produced from various sources and in multiple formats in every business. The data consolidation process makes it easier to unify that data.
Consolidating data enables companies to efficiently plan, implement, and execute business processes and disaster recovery solutions. This is done because all critical data in one place grants users a 360-degree view of all their business assets. It improves data quality, fast-tracks process execution, and simplifies information access. Thus, proving how necessary data consolidation is.
Data consolidation differs from data integration in that it specifically emphasizes the process of merging and organizing data from multiple sources into a single, coherent dataset. On the other hand, integrating data encompasses a broader set of activities to create a unified view of data. In short, data consolidation is a subset of data integration, focusing on creating a consolidated and organized dataset from diverse data sources.
Data Consolidation Techniques
The following are the three most common data consolidation techniques:
ETL (Extract, Transform, Load)
ETL is one of the most widely used data management techniques for consolidating data. It is a process in which data is extracted from a source system and loaded into a target system after transformation (including data cleansing, aggregation, sorting, etc.).
Automation integration tools can carry out ETL in two ways:
- Batch processing: is suitable for running repetitive, high-volume data jobs.
- Real-time ETL: uses CDC (Change Data Capture) to transfer updated data to the target system in real-time.
Data virtualization integrates data from heterogeneous data sources without replicating or moving it. It provides data operators with a consolidated, virtual view of information.
Unlike the ETL process, the data stays in its place but can be retrieved virtually by front-end solutions like applications, dashboards, and portals without knowing its specific storage site.
Data warehousing is the process of integrating data from disparate sources and storing it in a central repository. Hence, facilitating reporting, business intelligence, and other ad-hoc queries. It provides a broad, integrated view of all data assets, with relevant data clustered together.
Data gathered in a single place using a data consolidation tool makes it easier to determine trends and create business plans.
The data consolidation tasks offer businesses several benefits. When data is stored in one location, it requires a smaller setup for management. This allows companies to cut down their costs.
Moreover, by consolidating big data, you can enjoy better control as there are fewer processes involved in data retrieval, and you can access data directly from one place. This ensures significant time savings. Plus, planning, implementing, and executing disaster recovery solutions become comparatively more straightforward as all the critical data is in one location.
If you’re looking for an easy-to-use, user-friendly solution for consolidating data, give Astera Centerprise a try! It is more than just a data integration tool that gives users the option to consolidate data using ETL, data virtualization, or data warehousing. You can select the technique that best fits your requirements.