Data Consolidation: How to improve the quality and efficiency of your data
Increasingly, business decisions in organizations depend on analyses and reporting of corporate data. In addition to the preparation of the available data for meaningful reporting, the preparation of the data is particularly relevant. Poorly prepared data carries the risk of large losses due to incorrect analyses and conclusions. Consolidating Data across the enterprise can help you minimize these risks.
Data Consolidation includes the merging of data from different source systems, the removal of duplicate content and the enrichment of incorrect information. As such a data quality function, Data Consolidation is also part of Data Migration.
Data Consolidation is the process of merging data from disparate source systems, formats, and business units into a single data source. Data Consolidation is part of data migration.
The goal of Data Consolidation is to achieve unified, cleansed, and consistent data sets, thereby improving the overall quality and usability of the data and resulting in consistent information.
When Is Data Consolidation necessary?
Do you use different source systems in which company data is available?
Data consolidation helps you to identify and merge related data records and overlaps in different systems. In this way, you will not lose any relevant information about your customers or the use of your products. All related information (e.g. about a customer) can thus be identified in the different systems. This allows you to use combined information for your business decisions.
Do you have Data Quality Issues?
When data is inaccurate, incomplete, or outdated, it needs to be cleansed and unified. Data consolidation identifies overlaps of data in different systems before merging them into a target system. This avoids inconsistent data in the target system (e.g. due to different units or formats). As a result, you get complete and combined golden records in your target system during the subsequent data migration.
During Data Consolidation, data can also be enriched with information from reference data and reference records and supplemented with additional information from external systems (data enrichment).
Are you struggling to use and access the data you need?
Data Consolidation can help when accessing data is too complicated or takes too long, or when it is unclear which information on a topic is correct. Data access and management can be greatly simplified through data consolidation and data cleansing.
Processes during a Data Consolidation
Data consolidation takes place in several steps, with certain steps running synchronously and continuously, especially when using intelligent MDM software:
Determine the goals for Data Consolidating: Before consolidating data, it is important to establish goals and objectives for future data use and management. This is especially important for the next step, identifying relevant data sources, so that critical information is not overlooked.
Goals may include: harmonizing data sets or data formats, improving data quality, reducing storage costs, creating a single, enterprise-wide data system, or optimizing data access.
Identify data sources: Define goals and benefits to determine which data sources are relevant to the business and from which data sources and business units data should be included.
Data Cleansing and Transformation: To obtain consistent information, define target formats (e.g., specify a product length in centimeters), cleanse data (data cleansing), remove duplicates, and, if necessary, enrich additional information from third-party systems with reference data (data enrichment).
Data Integration: Data from multiple sources is merged and stored in a central database or system.
Data Check, Maintenance and Monitoring: All merged and cleansed data is continuously checked, updated, and reconciled with new incoming data. New data or data sets are also checked for timeliness, consistency, and plausibility to ensure good data quality on an ongoing basis.
Data Consolidation Methods
Essentially, the various methods used in data consolidation can be summarized as follows:
Aggregation is the process of combining a data series into a single data point to save computing power.
Data Cleansing is the removal of duplicates and erroneous values to improve data quality.
Data Enrichment is the addition of incomplete information from other sources or the transfer of characteristics to group members to improve data quality.
Benefits of Data Consolidation
As already mentioned, there are several benefits that data consolidation can bring to an organization. The most important ones can be summarized as follows
Better and more informed decisions: Consistent, high-quality data from data consolidation enables informed decision-making and accurate analysis.
Increased efficiency: Reducing data redundancy and enriching the information you need lays the foundation for more efficient business processes.
Cost savings: Optimizing data infrastructure reduces the need for expensive hardware and storage.
Time savings: Creating a central data source eliminates time-consuming searches, research, and verification processes.
Reduce risks: Improved data quality and information consistency reduce the risk of incorrect assumptions and decisions in business analysis and reporting. Defined access rules also reduce the risk of data leakage.
How can MDM Software help?
Static programs are often inflexible and cannot meet the demands of an ever-changing data landscape. In addition, the time required to maintain, check and update data is high. Master Data Management (MDM) software, such as DATAROCKET Core, addresses these issues and minimizes the risk of errors. The benefits of MDM software include
Scalability: MDM software can handle dynamic data growth and ever-changing requirements.
Real-time data integration: MDM software can consolidate and retrieve data in real time.
Data quality management: With MDM software, data maintenance, auditing, cleansing, and updating are fully automated.
Rapid reporting capabilities: MDM software helps you maintain an overview and quickly evaluate existing data and create relevant analyses.
All in all, data consolidation is a critical step for organizations to optimize data assets and make better, data-driven decisions.