Data is often referred to simply as “data”—an aggregation of information, as if each unit of data was identical in structure and purpose. But the reality is far different. For most organizations, data is not like having a shopping cart full of apples. Instead, that cart is typically full but with much or most of its data in different formats (apples, bananas, oranges, etc.).

Because the average data-driven organization relies on many types of data from numerous data sources, forward-thinking companies are now using data consolidation tools to more efficiently deal with their data warehouses full of information.

Although it begins its journey as raw data, businesses can apply data analytics to that information and derive business intelligence insights. At this point, it’s up to the organization to effectively implement that data analysis into its business decisions, but at least the company will have more complete and immediate data access that can better inform its decision-making.

Data consolidation (often referred to as data integration) offers several key advantages:

An expanding number of methods are used to support data consolidation projects.

The most important data consolidation technique is known as ETL (extract, transform and load). ETL processes begin with ETL tools extracting information from data sources. Then that data is transformed into a standard informational format. Lastly, the data is loaded into a selected destination.

An emerging counterpart to ETL strategy is called ELT (extract, load and transform). The re-arrangement of ELT steps is crucial. In ELT, data is extracted, then loaded to a type of staging area. Data remains here as various entities within the organization study it from different angles, ultimately transforming the data.

Keeping all data in one centralized repository is a practical approach. A higher degree of data security can be achieved with the use of a data warehouse, which accepts the data sets from various source systems. ETL tools can then be used to automate data and consolidate it into the warehouse.

Data warehousing is used in part to clean or process data. A data lake, on the other hand, is simply a data repository that offers none of the data-processing capabilities. A data lake is essentially a place to park data while it’s still in its rawest form. Typically, this is where a company might deposit obscure data.

It’s all a matter of scale. A data warehouse is geared to accept and store all data. A data mart is simply a smaller data warehouse with a much narrower focus. So, while a company uses a data warehouse, a department or group within that company might have a data mart specific to its particular needs.

In an age of automation, hand-coding seems old fashioned. However, there are plenty of circumstances which call for a simple data consolidation job. Such work is accomplished through hand-coding, as performed by a data engineer. The code that engineer writes helps “corral” data into one location.

Yet another data consolidation solution for businesses to consider is data virtualization, wherein data stays in its existing silos and is viewed through a virtualization layer that’s added to each data source. Unfortunately, there are limitations related to this method, including reduced scalability.

The tremendous growth of big data continues to rock the tech world, and should for some time. For the period of 2022 through 2030, Acumen Research and Consulting is predicting that the big data market will continue to expand (link reside outside ibm.com) at a rate of approximately 12.7% annually. According to its predictions, that market will skyrocket from a 2021 value of USD 163.5 billion to a projected 2030 market worth USD 473.6 billion. As the big data market expands, so does the need for more data consolidation.

The automation of manual processes related to data consolidation is another area that has seen intense development in recent years. This is occurring at a time when there’s a relative scarcity of data science talent. It’s been estimated that more than 60% of data science hours (link resides outside ibm.com) are spent cleaning and processing data during consolidation processes. Those processes can and should be automated (and will be, in increasing amounts).

Data security also remains in the center stage, reflecting the continuing and growing threat of cyberattacks or ransomware attacks. In response, organizations are choosing options like data pipelines that offer greater security as pipelines move, store and analyze data.

Similarly, another recent development speaks to the growing interest in protecting the privacy of consumers, especially after a rash of high-profile cyberattacks that resulted in the mass dissemination of consumer data. So-called data clean rooms are now increasingly being implemented as a privacy-friendly way to interact with consumers. In data clean rooms, interactions are structured in a way that limits the amount of consumer information that’s typically being collected by the organization.