If your business collects data from multiple sources, you will likely be interested to learn about data consolidation. Modern companies generate tons of data from mobile apps, web services, IoT devices, and more.
However, when it comes time to draw actionable insights from the wealth of data being collected, many companies struggle because their data is not unified.
The only way your business can get value from data from multiple sources is to consolidate the data in a data warehouse, data lake, or another unified system. Data consolidation is a modern practice that helps businesses manage and extract value from the enormous amounts of data they collect.
This post will explore data consolidation in greater detail, explain why it is essential for businesses, and cover some common challenges associated with data consolidation.
Data Consolidation Defined
Data consolidation is the process of collecting, combining, and storing data from multiple sources in a single location. Typically, the data is stored in a cloud data warehouse or data lake.
In many cases, the terms data consolidation and data integration are used interchangeably. Therefore, if you come across references to data integration, understand that it is the same concept as data consolidation.
Modern businesses collect data from various sources, such as CRM software, product databases, HR systems, IoT devices, etc. Yes, the data collected by these tools is valuable in its limited silo, but when combined with the rest of the data your company collects, it can provide more value than you can imagine.
When you consolidate data sources, gaining a comprehensive view of your business processes and operations is simpler.
The Data Consolidation Process
Data consolidation involves three key steps: extract, transform, and load (ETL). ETL is a data pipeline’s process to replicate data from the source to a data warehouse. There is a popular variant to the ETL data pipeline known as ELT. The letters in the acronym stand for the same words, except the steps are changed.
Instead of extract, transform, load, the process follows extract, load, transform (ELT). The ELT data pipeline process is popular with many data scientists because they believe it is easier to transform data once replicated in the target data warehouse.
There are two ways in which the ETL or ELT data consolidation process is accomplished:
- Hand coding
- Software tools
Hand-coding is a manual process that requires a data engineer to write a script to consolidate data from selected sources. Hand coding data consolidation is time-consuming and requires a specialist, but this data consolidation method could be the best choice for small consolidation tasks with limited sources.
However, if your data sources, destinations, or formats are not supported by available ETL or ELT software tools, you will have no choice but use the hand coding data consolidation techniques.
Software tools are the most popular method for consolidating data. These tools can be local or cloud-based, don’t require a data engineer to operate, and work fast.
For example, your business could choose ETL or ELT software tools, and once set up, these tools immediately get to work on data consolidation.
In addition, many ETL and ELT tools are sold as SaaS, so your business won’t have to worry about updating, testing, maintaining, or securing these tools because the SaaS provider handles those tasks.
Using software tools simplifies your data consolidation project, but it could limit your data consolidation options.
The Importance of Data Consolidation
Data consolidation is vital for several reasons, but three primary reasons rise above all the rest:
- Data quality
- Data analysis
- Business planning
If data is used in your business processes, you need to ensure that your company is using quality data sources. Poor data is worse than having no data.
Data consolidation helps ensure the quality of the data sources your company uses to make decisions.
Since data consolidation techniques involve transforming the data into a consistent format, your organization has a chance to improve data quality and integrity before the data is used in your operations.
Most companies have analytics and business intelligence tools that rely on the data resources collected to provide actionable insights and forecasts.
Data consolidation is crucial because it ensures that companies have a complete and accurate data set from every data source. HiTech data analytics, business intelligence, and Machine Learning tools are wonderful, but they are only as valuable as the provided data.
As a result, your company’s analytics and intelligence tools are ineffective without complete and accurate data.
How can you even begin to start creating a focused business plan without a complete overview of your business? Data consolidation provides a complete picture of your entire operation. Business leaders can use consolidated data to plan operations, improve business processes, and create disaster recovery plans. In addition, when you have all of your data in a unified data warehouse, it is simpler to plan for current and future data storage needs.
The Challenges Associated with Consolidating Data
Data consolidation is an important task for most businesses due to the sheer amount of data being collected from multiple sources. However, there are challenges that your business might face, including:
- Limited resources
- Data security
Most businesses don’t have unlimited time and money to complete data consolidation projects. Data consolidation projects can be lengthy and often require a dedicated team. While data consolidation is worth it in the end, your business might struggle to find the resources necessary to dedicate to a project of this magnitude in the short term. The more complex your data landscape, the longer data consolidation will take.
Data generated by different sources are often not formatted cohesively. As a result, a common challenge many organizations face is format incompatibility with ETL tools. Incompatibilities can be worked around with hand-coding, but this manual approach to data consolidation will take longer and cost more money.
Data is most valuable when it is timely. A common issue many businesses discover with data consolidation is data latency. If you are using a central source for data, the data you are viewing might not reflect the most recent actions, etc. This is caused by a latency between the initial data sources and the transfer to a centralized data warehouse. More frequent data transfers can help alleviate this issue, but if you require real-time data, consolidated data will likely be behind the initial source generating the data point.
Data security should be a primary concern for every business consolidating data in a single location. One centralized location for data is great for business operations, but it presents a greater security risk. Therefore, all businesses that use data consolidation must ensure that they take the necessary precautions and employ the latest security measures.
Data consolidation is a great approach for businesses that collect and rely on data. However, like any technology, data consolidation can also present difficulties. If you need help determining your company’s best approach to data consolidation, reach out to an experienced technical partner like Koombea for guidance.