CRM, ERP or CMS system - companies gather huge amounts of data in these systems, among others, over the years. One of the big challenges: Extracting the knowledge from the data and turning it into a competitive advantage. For this to succeed, companies need to look at data integration and bring data together in a strategic way.
Definition: What Is Data Integration?
Data integration refers to the process of consolidating data from different source systems into one data management system, for example a data warehouse.
In many cases, data integration is a prerequisite for producing meaningful analyses and forecasts. However, for data integration to deliver business benefits, implementing technical systems and workflows is not enough. Alignment with strategic business goals is just as important.
Why Is Data Integration Important?
Data is the gold of the digital era. To mine it, companies need to look at data from different source systems in aggregate. However, until now, cross-departmental analysis has often involved a lot of manual effort when data first has to be pulled from different systems. This costs time and requires technical know-how.
However, data-based decisions are increasingly adding to managers' subjective empirical values. Therefore, it is even more important that the data basis not only represents a partial section of reality but shows the whole picture.
Data integration enables precisely these cross-departmental analyses. Because BI tools can now access a comprehensive dataset. For users, this means that data-cleansed and quality-assured reports are available to them with just a few clicks. IT staff can spend their time on other value-creating activities. Managers receive more meaningful reports thanks to the consolidated database and can make strategic decisions more quickly.
According to Forbes' 2020 study, "The State of Enterprise Data Integration," 80 percent of executives view data integration as key to smooth operations. Sixty-seven percent of the companies interviewed were already using data integration as the basis for their BI and analytics platforms. 24 percent planned to implement it in the next 12 months. In the future, data integration will be a must for companies.
Data Integration – Example From Everyday Business
Examples of data integration use cases can be found in almost every department. Whenever a 360-degree view of customers is helpful or interface activities of departments are to be better synchronized, data integration simplifies the work process.
For example, sales records the development of opportunities (individual sales projects) and revenues per account, while Marketing records the costs per lead and website hits. If the development along the entire customer journey were analyzed - from the first contact to the purchase transaction and contact with support - it would be possible to find out whether later key accounts already have similarities at an early stage of the customer journey and to focus marketing on this target group. Until now, data from different applications had to be brought together manually for this purpose. With a highly automated integration of data from the various customer touchpoints, companies not only accelerate the analysis, but also bring more transparency into the customer journey.
Which marketing measures are worthwhile and where is money being wasted? Facebook Ads, LinkedIn ads, email marketing software – many different paid tools are used in marketing. With the right data integration strategy, teams gain an overview of the effectiveness and profitability of their campaigns.
Without data integration software, users would have to pull reports from different applications one at a time, manually cleanse the data, and convert it to a consistent format before they could begin the actual analysis.
With data integration tools, users need only a few clicks to create a cross-departmental cleansed report for their question.
3 Recommendations for Data Integration Software
On the market, companies can find data integration tools for a wide variety of IT infrastructures and requirement profiles. Established and top-rated vendors include Informatica, SAP and Microsoft.
The industry leader's applications provide data integration solutions for all conceivable IT infrastructures: on-premise, cloud and hybrid models. Power Center is the high-performance, scalable data integration application that is metadata-based and forms the core of the integration. It can be extended in modules depending on business requirements.
SAP also offers software for data integration. The SAP Data Services Tool combines data integration with data quality management and data cleansing and is suitable for all companies that already use various SAP applications.
With its Azure Data Factory, Microsoft offers a cloud platform for data integration that is ideally suited for hybrid IT infrastructures. Both on-premise and cloud data from SaaS services can be easily captured and processed by the solution. An intuitive interface and smart automations, such as autonomous ETL, empower employees without programming experience to manage data integration.
What Are the Benefits of Cloud Data Integration?
Especially if companies use many SaaS applications, the use of a cloud application for data integration can be beneficial. After all, not every on-premises solution can be easily networked with cloud applications.
Incidentally, there are many reasons for using cloud data integration that apply to cloud-based applications in general.
A cloud platform for data integration protects internal IT resources. There is no need to invest in new server capacity, there is no additional maintenance time and the connection of cloud solutions for data integration is sometimes easier than the implementation and interface configuration of such applications on-premises.
Other advantages: The performance and flexibility of the cloud solution. Providers such as Microsoft or Amazon keep their platforms at the cutting edge of technology. They handle peak loads without any problems and companies can add capacity at short notice depending on their growth.
Frequently Asked Questions About Data Integration
When devices and systems within a department are networked to bring data together, this is called horizontal data integration. A common use case is industrial production. Data on all the machines on a production floor is bundled in software to ensure trouble-free operations. Vertical data integration, on the other hand, refers to the networking of data sources from different departments, for example production systems with logistics systems.
The more complex a company's existing infrastructure, the more challenging it is to select and implement suitable data integration software. Therefore, we offer strategic implementation support at Parsionate to shorten the decision-making process and to realize the implementation with minimal impact on the operational business.
ETL stands for Extract, Transform and Load. The abbreviation thus describes a possible data integration process. Especially in the cloud area, however, data integration can also take place in the ELT process, i.e. the transformation only takes place in the target system. In some cases, the term ETL process is also used synonymously with data integration, but this is not correct. If you compare ETL tools with current data integration software, modern applications are more broadly based. In some cases, they integrate functions for data quality management or master data management.
Data Integration Health Check