What is Data Consolidation?

Data consolidation is a relatively new discipline that has emerged because our data is becoming more spread out and difficult to manage. To learn more about how data consolidation can help your organization benefit from all your data, rather than being overwhelmed by it, read on.

What is Data Consolidation?

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.

Why is it important for your project?

Data consolidation is maybe one of the most important processes within a data-driven organization. Various systems, multiple databases, plenty of departments, and dozens of 3rd party vendors contain data about your business and customers.

While this data provides value to the respective parties, only when you combine all of the information and analyze it, you can make the most out of it. Only then will you be able to have a better view of what’s going on and how everything is connected?

Data in different departments mean that besides their origin, their formats and purposes might be different. This diversity makes it difficult not only to combine the data but also to analyze it and eliminate any errors or duplicates.

When the amount of data being generated is increasing rapidly, the quality and accuracy of the information are at stake. Data consolidation aims to eliminate discrepancies before data is used for reporting or analysis, saving you time and increasing the value you’re getting out of your data.

Your business decisions are only as good as your data supporting them and without data consolidation, you lack the ability to see the full picture.

Benefits of Data Consolidation

Reduce Costs

Since organizations eliminate data redundancy with Data Consolidation, they eliminate the need for several databases that hold the same data for different business functions. As a result, this reduces the costs associated with data storage for insights generating.

Apart from optimizing storage cost, Data Consolidation ensures data integrity, which leads to consistent insights across the departments. Reliable data assist in generating uniform insights that help decision-makers avoid making ineffective decisions, which in turn, reduces operational costs.

Improve Security

Controlling the data flow is a strenuous task if you have to individually apply policies. The changing business requirements demand constant modification in the data access control.

Continuously making amendments in data management for different data sources can open up gaps in data security. However, with Data Consolidation, organizations can eliminate flaws in security policy as it requires data management from a single location. Data Consolidation simplifies data administrators’ work for compliance with the data protection laws of different countries.

Enhance Decision-Making

If data is organized properly, consolidated customer data may be instrumental in developing a Customer Satisfaction Index, which allows a business enterprise to track performance and create KPIs for each department. It not only enhances decision-making within their teams but also improves customer experience by monitoring audience activities and offering a personalized experience.

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

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

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 Challenges of Data Consolidation

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 where 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.

Conclusion

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.