Everything You Should Know About Data Integration

By Ashutosh Jha → Last Updated on Sunday, July 26, 2020
Enterprises’ revenues depend on the data quality that is crucial for business analytics, sales, and marketing efforts. In order to extract insights from a multitude of databases and achieve complete, consistent, and up-to-date data, businesses implement data integration solutions. So, what is data integration and why is it so crucial for a business foundation?

Data Integration
Data integration is a technological process of combining data from various systems into a single and cohesive viewpoint. It helps to remove duplicates, clean data, and transform it into the required homogeneous format. Since the amount of data is growing exponentially, the establishment of robust data integration solutions has become more crucial than ever.

Why Does Your Business Need Data Integration?

With the velocity of digital transformation and data, salespeople need to find more efficient solutions that will help them to sell more and faster. You might wonder: most companies use CRM systems; don’t they provide the entire vision of relations with customers?

They do provide a lot, but it is not sufficient for them to deliver maximum results. There is a lot of information in different systems (for example, the ERP system) that are not present in the CRM system, and consequently, could bring much more value when combined with customer relationship management systems.

For instance, billing or accounting data, information about logistics procedures, or previous orders can immensely enhance the results of sales reps making them more efficient and faster.

With ERP-CRM integration your business can be much closer to your customers through streamlined and automated workflows. Here’s what you can get:

  • 360-degree view of customers
  • Effective marketing campaigns through accurate forecasts and predictions
  • Elimination of manual data entries thanks to synchronized data that are reflected in all integrated systems
  • Instant access to real-time information
  • High data quality

That’s why your business needs a robust cloud-data integration solution to boost revenue and provide team members with effective solutions to enhance business performance and customer experience.

Steps to Take When Planning Your Data Integration Project

To ensure consistent data across systems, you need to be strategic in your intentions coming up with a plan with detailed data integration process flows:

  • Define your business processes and determine how your data should synchronize.
  • Choose the right data integration software.
  • Map your connectors and fields.
  • Connect your systems.

Types of Data Integration Methods

The selection of data integration forms depends on your company preferences, goals, and business model. You have to know how and why your organization uses applications. In any case, the key success factor for any solution is high data quality. Generally, it is worth getting in touch with data integration professionals who will provide you with appropriate recommendations.

Most common methods of data integration. Infographic

Data consolidation (Uniform Data Access)

During the data consolidation process, data is physically fetched from numerous separate systems and presented in a unified form. Consequently, the number of data storage locations is reduced. There are two approaches to data consolidation:

ETL (extract, transform, and load). ETL extracts data transforms it into an appropriate format, and then data is transferred in the data warehouse. Because of the data staging, processing time can be high, however, this approach is better suited to large amounts of data.

ELT (extract, load, transform). In this approach, once extracted from the source system, data is loaded into the target destinations, and only afterward, the transformation logic is applied within the database. Because transformation occurs directly in the data lake destination, the latency is reduced.

Common Data Storage (aka Warehousing)

Being the most frequently used, this technique helps to collect data from different systems and combine it into a single place, enabling you to handle and store data securely. In contrast to data consolidation, using this approach, data is copied to the integrated system for a unified view and data can be combined from very different sources.

Common User Interface

This method is also called manual data integration since users have to homogenize and integrate data by themselves since data from relevant data systems are presented separately. There is no consolidated view of data. This approach can be used only for a small number of sources.

Integration by Application

Application-based integration solutions are suitable for limited amounts of data and systems with a small number of components. This approach covers software applications that retrieve, clean, and integrate data from disparate sources. Regardless of the fact that this solution allows automated and seamless data transfer, there are also drawbacks such as difficult data management, complicated setup, as well as limitations in case of large data volumes.

Middleware Data Integration

Middleware data integration serves as a mediator between two separate systems that enables different services and products to be connected and helps bring data to the master data pool. Being a common layer of software middleware connects different internal and external applications, software platforms, and devices. The goal of this technology is to foster the efficiency and sustainability of business processes through rationalizing legacy IT systems.

Final Thought

Data quality is a crucial aspect of data integration and data management. Companies have to manage a multitude of databases effectively in order to extract high-quality insights for enhancement of their business performance. Without a proper data integration solution, executives take the risk of getting inconsistent and incomplete data that can be harmful for a business foundation.

Data integration solutions are aimed for efficient and fast sales enabling business processes to be more streamlined and automated. There are many aspects worth considering before implementing a data integration project, such as a detailed plan of the data integration process, types of data integration methods, scalability opportunities of the integrated systems, etc.

Generally, to build a robust enterprise architecture your solution needs to be future-proof.

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Ashutosh Jha

Ashutosh Jha is a professional blogger, Blog and IT Consultant. He writes about Blogging, SEO, Making Money, Internet Marketing and Web Design.
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