Complex data integration

Companies often have a big problem. They do have all the necessary data available, but most of the time it is spread across many different data sources. There is the data in the CRM system, the data from web traffic and from the marketing software, data from sales systems or customer applications and many more. For analysis purposes, however, it makes sense to bring all this data together. That can be extensive and time-consuming work for data engineers and developers alike.

According to a study from 2020, almost two thirds of companies already use data integration. In doing so they improve their operational efficiency. Almost 60 per cent of companies also use it for faster analyses. For companies, unifying data sources is very important. It improves their workflow in many areas.

What actually is data integration?

Figure 1: In e-commerce in particular, companies are confronted with an enormous flood of data. Anyone who wants to remain competitive here needs help capturing and analysing that data. Pixabay © preis_king (CC0 Public Domain)

Data integration is not merely the bringing together of data stored in different systems into a single view. During the data integration process, the data is taken into a target system, cleaned and transformed to suit that target system. At the end, meaningful information is available.

Because of modern cloud and big data technologies, companies are confronted with an unmanageable flood of information that can only be analysed effectively with the help of professional data integration. In modern companies, competitiveness and decision-making are based on data. For them, data integration is unavoidable.

A uniform approach to data integration that applies to everyone does not yet exist. Some elements do recur again and again, however, such as the network with the various data sources and a master server with clients. During data integration, the client repeatedly sends requests to the master server. The server takes the data required for those requests from the various data sources and standardises it so that further processing is possible. As a rule, specialists for complex data integration take care of such data integration solutions.

Which technology problems does data integration solve?

Big data is a synonym for large and very complex data sets from very different sources. The sheer volume makes managing big data time-consuming and demanding. Special data integration platforms make it possible to simplify these processes. The information collected becomes easier to understand.

• The data silo problem

Figure 2: When everyone in a team, in a company, can always access the same data basis, work becomes far more efficient. Pixabay © ronaldcandonga (CC0 Public Domain)

Data silos always arise when a lot of information is stored in different places. That is typical when legacy systems are still in use or when disjointed software is in operation. Departments in a company that do not communicate can also produce data silos.

Such data silos can be prevented with appropriate data integration tools. The systems help to transfer the data from the older systems into new ones and make it available there. The data integration process makes the system cross-functional, because all teams and systems have to consolidate their data.

• The semantic integration problem

When several data sources exist, semantic problems and duplicates can arise in companies. For example, two files contain the same information. However, they are in different formats. A data integration application is capable of recognising variations and removing duplicates. In the end there is only one data source left.

• The accessibility problem

Data integration processes create central data sources in companies. This means that all stakeholders in a company can always access the same information. That shortens waiting times when someone retrieves data. At the same time, participants make fewer requests because they already have all the data or can find it themselves.

Added value through data integration

Data integration is also very useful when it comes to applications and business processes, keyword business intelligence (BI). With the help of BI, companies evaluate critical business data. That helps managers to develop strategies and gain insights into the entire operation. But before business intelligence applications can work, they need a uniform data basis, which can only be achieved through data integration. During the data integration process, all data is cleaned, prepared and standardised. This ensures that all reports really do contain precise, correct and reliable data.

Managers in companies have to make many decisions every day. Good decisions are based on correct and comprehensive information. Data integration can help with decision-making because it contributes to managers being comprehensively informed.

Data integration also provides companies with important customer information. They gain a far better insight into their customers’ buying behaviour. This allows companies not only to improve their customer service, but to align customer service and sales with their customers’ preferences.

In liquidity planning, integrations can ensure that information relevant to liquidity is integrated into the plan from upstream systems. In this respect COMMITLY provides direct integrations, a connection via Zapier and a direct open API.

First steps

Modern business processes always come with a large amount of data. This flood of data is putting organisations under more and more pressure. For a data integration solution to suit your own organisation or company, a few things need to be considered in advance, for example which requirements you want to apply in order to achieve your goals or which problems may arise in the course of the changeover.