Any organization considering acquiring an ERP software module from a CRM to Finance, HR, SCM, or Sales & Marketing will ask how to integrate their existing data. Whether it is a Cloud or On-Premise ERP, the methods for importing, exporting, and continuously synchronizing data within your organization’s existing systems are the same. The main difference to the Cloud is that it is usually required to duplicate some existing organizational data. In the following sections, I’ll share my typical explanation of those notions with new customers.

First of all, a quick glossary:

  • An Enterprise resource planning (ERP) software is a business management software that an organization can use to collect, store, manage, and interpret data from their activities. (Financial Accounting, Management Accounting, Human resources, Manufacturing, Order processing, Supply chain management, Project management, Customer relationship management, etc.)
  • Customer relationship management(CRM) allows a company to manage and analyze its interactions with its past, current, and potential customers.
  •  Cloud computing software is a software model in which customers’ services are available over the internet on a subscription/per-use basis. 
  • A Middleware is a communication software to enable communication and data management between distributed applications.
  • An ETL is a Middleware short for Extract, Transform, and Load. Three functions combined into one tool to pull data out of systems, clean and transform those data, and load them into another software.

Data integration in an ERP Cloud

Data integration is about combining data from different internal and external sources into a single, centralized repository. For example, a business can store customer data in a local database, manage inventory data with a third-party platform, and want to centralize all those data into a data warehouse or an ERP module like a CRM.

Such situations are not uncommon. As a business grows and changes, so do their software and data needs, and a strategy that once made sense needs to be revised.

The ETL process and other modern data streaming approaches are at the heart of data integration. Data integration begins with extracting data from multiple sources and moving them into a single data warehouse. (For businesses and organizations that do not use a data warehouse, the process is similar, although the data will be integrated directly from the source.) To facilitate the integration process, a cloud ERP offers a range of interface points, including REST, SOAP, and a BULK API.

During the transformation step, data is cleaned, validated, organized, and standardized. At this point, all of the different datasets are now in conversation with each other. Finally, the converted data is loaded at its final destination.

Data migration or Data integration?

The terms describe distinct and separate processes. They do, however, share some of the same implementation techniques.

Data integration is combining data from multiple sources, internal and external, into a target system. Data integration describes a unified set of smaller processes. Each process allows the extraction, transformation, and loading of a different data model. (customers, addresses, orders, etc.)

Data migration, involves moving data from one system to another. When a company decides to change its existing CRM system, or when it decides to downgrade from an older version to a more recent one, it must migrate all data from the current software to the new one.

Common integration methods

So far, we’ve provided an overview of the data integration process and how it combines data from multiple origins into one view and source. Some of the different data integration methods include

Manual data consolidation

This part of the process typically requires a conventional ETL, although some companies may use built-in custom tools or a simple excel extraction. Manual consolidation can work well for smaller, more specific datasets that don’t require a deep clean, but it can be too time-consuming and error-prone for more massive datasets. Besides, the lack of realtime data limits its usefulness.

Propagation of data from source applications

The goal here is to propagate the data from the individual applications to the ERP, and the integration logic to achieve this expands in the client applications. Rather than a standard tool or approach to moving data into the warehouse, each application takes responsibility for moving their data to the central store. This method is generally adopted because there can be heavy data cleaning and manipulation, and the application is in the best position to understand and perform these operations.

This approach is challenging to maintain because applications are subject to change, which often means that the integration logic needs to be rebuilt or adjusted.

Propagation of data using a Middleware

This method ignores the logic of application integration and shifts the responsibility to the Middleware. For example, a subscription mechanism configured between the Cloud ERP and the data warehouse ensures that whenever there is an update, an event is triggered to automatically publish the data to the warehouse, keeping it up to date.

Even when applications change, the Middleware maintains his function as a bridge transferring data to the ERP.

For this method to work, there must be an implementation layer that manipulates and transforms the data into a format that the consumer understands.

Data virtualization

In virtualization, data is not extracted and stored in a common repository but provides a mechanism to access data remotely from multiple sources.

The technique has the advantage of not having to create and manage a Middleware and offers up-to-date data in real-time without any data replication. It is perfect for highly secure applications that do not allow data to be stored elsewhere. However, this limits the scope of how ERP can use this data. The ERP is also constantly polling these data sources, adding performance loads to those databases.
This technique is not available on all Cloud ERPs.

The challenges of data integration

54% of Salesforce business customers identified integrating apps and data sources as their top challenge. Let’s take a look at a few factors where data integration remains a challenge:

Find the right experts

Integrating a cloud ERP with a data warehouse requires experts in different fields such as Cloud technologies, ERP modules, data warehouses, and Middleware technologies. Building such a team and ensuring that they communicate effectively can be a challenge.

Complexity of systems

Bringing together data from many systems using different technologies and locations can be a complicated task. The scale, volume, and complexity of this process require substantial planning and coordination.

Data mapping

Because data fields tend to be stored with different names and types in data sources, it isn’t easy to map each lot to the destination system. Some of the data sources could also be existing systems with significant data gaps. Solving these issues requires collaboration between business and technical stakeholders, who profoundly understand the data.

Ensure continuous data integration

Data integration is not a one-time task. The initial effort to import data is significant. Nevertheless, you need constant efforts to update the ERP and data warehouse when changes occur automatically.

Despite these challenges, data integration remains an essential part of an organization’s strategy to achieve a unified data view. Having a clear integration strategy and using a data integration tool overcomes these barriers.

Uniform data integration strategy

Consolidating the mix of Cloud and on-premises sources can mean different approaches to integrating their data. However, divergent paths can lead to inconsistent data processing, which in turn can compromise data quality. Creating a uniform strategy that ensures data integrity and synchronization despite systems’ individuality can be difficult.

How to define your integration strategy?

Identify your stakeholders

These can include executive sponsor, cloud ERP experts, data engineers, customers, and other specialists with a comprehensive organizational data view.

Ask the right questions

What are the budget limits, time, and availability of stakeholders?
Does your data need to be available in real-time, or can it be pulled on-demand or in batches?
What works best for your business: manual consolidation, propagating data to a warehouse using applications, reproducing data to a warehouse using a Middleware, or keeping your data bounded using virtualization?

Match the ERP data fields to yours.

Will you be using APIs, direct database access, Queuing, Streaming to manage the integration?

There is no conventional approach to integrating data into an ERP. Some organization sticks to manual integration while others use application logic, a Middleware, or a hybrid approach.

The final solution an organization achieves depends on many factors: the propensity to create a data warehouse, the availability of resources such as time and money, the size of the data sets, needing the data synchronized in real-time.

Reduce recurring human intervention

A data integration tool helps simplify the integration process’s complexity by providing an automated mechanism that consolidates data from multiple sources on-premises and in the Cloud. Such a tool not only enables faster ETL operations but also ensures continuous and real-time updates of the centralized data store. Doing so minimizes human intervention, reduces errors, saves time, and thus increases productivity and data quality.

Additionally, the tool makes it easier to scale as more data sources are added. Rather than having a fragmented approach with a different integration method for each source, the tool offers a consistent solution.

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