Data Consolidation- Definition, Techniques & More

Data Consolidation

When businesses do not consolidate their data types together by storing this information in one location, it becomes difficult to extract key information and analyze trends or patterns.

Data consolidation is the process by which companies can create a core record of data, in order to examine the insights that this historical information carries to direct future decision-making for business operations.

What is Data Consolidation?

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Data consolidation is the process by which various data points are gathered, combined, and stored in a single location, such as a data warehouse. Within this process, redundancies are removed and errors are cleared to ensure accuracy.

This essential process allows for management to streamline the sources of data, identify and examine patterns, and gain insight into essential business operations from a broad view, rather than sifting through multiple, disparate data points.

This is achieved by manipulating raw files into actionable insights to base future financial and operational decisions on. Data consolidation benefits companies by ensuring the quality and accuracy of information, allowing for a more effective process of accessing, manipulating, and examining the data when needed.

3 Key Data Consolidation Techniques

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The following techniques - ETL, Data Virtualization, and Data Warehousing, are the most common data consolidation methods.

1. Extract, Transform, Load (ETL)
ETL is the process of extracting data from a source system, transforming the information (by methods of sorting, cleansing, or aggregation) to then load into a target system. There are two main ways that this is done-

  • Real-Time ETL - Uses Change Data Capture (CDC) to transfer the data into the target system in real-time.
  • Batch Processing - For high-volume, repetitive datasets.

2. Data Virtualization

This technique integrates data sources without moving or replicating them. A virtual, consolidated view is what data operators see and work with. Though the data stays in its original location, it can all be retrieved virtually by front-end applications, portals, and dashboards.

3. Data Warehousing

This method involves integrating data from multiple locations and sources to store in a centralized location to effectively facilitate ad-hoc queries, reporting, and other business operations and insight.

It offers an integrated view of all data assets, categorizing relevant information together for more organized analysis. Having all data in one place allows more ease when identifying trends and creating strategies for enhancing business operations.

The Challenges of Data Consolidation

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With existing teams and internal management, taking on data consolidation internally may not be the most effective solution, as there are challenges that can arise when traditional on-site data consolidation is being done.

  • Time
Generally, internal IT teams will already have limited time with their roles in configuring, assessing, maintaining, and examining on-site equipment and hardware, along with their daily tasks. The team may not have additional time to then spend hours managing data consolidation.

  • Resources
Data consolidation requires many resources, from specialized knowledge from expert data scientists to the right kind of software. Some companies may not have the budget to source these experts for their internal consolidation efforts.

  • Locations
With many companies operating from multiple locations with remote offices, warehouses, and branches, there is no single place that the data is stored. Instead, it is managed across multiple locations.

It can take a great deal of time and resources to retrieve the data sources and bring them together. When too much time is spent on this task, that data can become redundant with new, more relevant data coming in before location compiling can be completed.


  • Security
There is always a risk of breaches and hacks when data is stored, and moving this information to another location can increase those risks. As businesses may also need to adhere to their industry regulations, it can be difficult to comply with security policies when there are scattered datasets.

Best Practices for Data Consolidation

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Data consolidation should be strategically planned to ensure that the most efficient process is being adopted for the type of data and the business model as a whole. The following points include some of the best practices for companies.

  • Check Compatibility - Are the types of data and the target of consolidation compatible? If they are not, this means that the data needs to be transformed to rectify the differences.
  • Keep Copies - The term data lineage describes how the source and history of data, it's transformation, and consolidation can be tracked and referenced. The information can be used for a company to prove their compliance with regulations, or to retrace steps to gain insight for future decisions in understanding these analytics.
  • Standardizing Character Set Conversions - When using applications that allow single-byte character storage, the application can convert the character types. However, the processing tools may not grasp that the data is in different formats, which may result in errors during the conversion. Standardizing character set conversions results in a more accurate and reliable outcome when consolidating data.

All companies that manage large volumes of data should have an effective strategy for data management. The consolidation process is one technique that enables organizations to have a clearer insight into their operational and sales trends to make informed decisions in the future.

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