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Integration Technologies and Approaches

Data integration is a big deal in 2024, so what are the latest technologies and approaches for data integration?

Data integration technologies and tools have become a necessity for the modern organisations with complex data networks.


This article outlines why and when you should acquire a data integration tool and provides descriptions of some of the key tools on the market.



Why data integration?

We've covered complex data, integration and data management in previous articles, but in short, data integration is a necessity for the modern enterprise business. Data is the driving force for assessing business achievements, determining risk, and identifying future opportunities.


When data is housed in multiple and different locations - files, applications, tools and systems - it becomes difficult to understand and use. Distributed data is like scattered puzzle pieces. It can become challenging to know which data is more accurate and where it is outdated or corrupted. Data integration tools give you a complete and holistic understanding of the state of play.


Data integration processes and methods

The most common data integration process uses an extract, transform, load process. Known as ELT, this method of data integration extracts data from the systems where it is located, transformed, or updated so it can be used in conjunction with all of the other data and then loaded into its final, consolidated repository.


During the transformation process, a data integration tool will assess data quality and make it consistent.

Other data integration methods include:

  • ELT- through which data is transformed once it reaches its final repository rather than before it arrives
  • Data virtualisation - through which data isn't transformed or migrated, but rather a virtual data warehouse is created to process data records
  • Change data capture - through which data updates are detected, and records are updated automatically in the data repository 
  • Data replication - through which data from source applications is duplicated into a data warehouse or data lake


Data integration processes

A data integration tool will carry out some or all of these processes: 

  • Facilitating interaction between numerous applications
  • Move and combine data to create a cohesive data set
  • Data validation, mapping and transformation 


Data warehousing

There are different terms for the locations where the integrated data is loaded and accessed. These include:

  • Data warehouses - a repository for the structured data that has been through the integration process
  • Data marts - smaller-scale data warehouses that might have a specific purpose or be used by a smaller team
  • Data lakes - for extensive collections of unstructured data
  • Data lakehouses - containing both structured and unstructured data 


Data ingestion vs data integrations

Although these two processes are related and share a lot of commonalities, they are slightly different.

Data integration combines and collates data from various sources, producing a consolidated data record. It is a technological process that locates and integrates data from multiple systems. Data integration involves standardising and blending records so that differences in how they have been created and stored can be addressed.


Data ingestion describes the process of using a tool to collate data from multiple points and sources to analyse and process it.


Uses of data ingestion

Data ingestion software is used in businesses to collate data from multiple applications and programs in a centralised location, sometimes known as a data warehouse. Data ingestion tools can:

  • Collect and collate data
  • Run connectors to various data repositories
  • Schedule data collection 
  • Automate manual data collection tasks


How to integrate multiple systems

Some data integration tools are highly technical and must be customised to handle your existing data. Other tools are designed to be easier for the average business to implement without requiring highly technical internal knowledge.


If you intend to embark on a data integration project, it is essential to: 

  • Develop a clear data integration strategy to keep you on track, outline your strategic intent, and establish project timelines and budgets
  • Get consensus on your objectives from across the business. Different people and teams use and understand data differently
  • Create a clear data governance framework to help ensure a shared understanding of records, formatting, practices, and procedures
  • Assess the data- ensure you thoroughly understand all the different locations, applications and file types you will need your integration tool to handle


Benefits of integrated data

Businesses from all industries are prioritising data access and analysis. The benefits of integrated data include:

  • Enhanced data quality
  • Data sets that can be compared, contrasted, and reported on
  • Improved operational efficiency, with a reduction in manual tasks and duplication of effort
  • Better opportunity for collaboration, planning and workflow


Opportunities for data integration

Developments in capabilities and technology will only continue to evolve data integration tools. Opportunities we see on the horizon for data integration include:

  • AI in data integration - AI will increasingly be used during data transformation in the integration process. AI and machine learning offer the opportunity for data cleansing and correcting as it is transformed and also learns to assess and predict the relationship between diverse records.
  • Real-time data presentation - As technologies improve, integration processes can occur more quickly than ever. This means processing time is shortened, and access to integrated data can be provided promptly or even in real time. This, in turn, will lead to simplified and easy-to-use systems that the average business can deploy.
  • Cloud-based integration - giving access to integrated data from anywhere. Cloud-based data warehouses are increasingly popular, acting as easily accessible repositories for large integrated data sets. In many cases, the original data remains on-premises, while transformed data is housed in the cloud. This hybrid model suits many users and is flexible for data access requirements.


Pimcore's integration capabilities

Pimcore is not technically considered an integration or data ingestion tool. However, it can scan multiple data points and deliver a consolidated "golden" record to the user. This means that it can assess large data sets of various types and ensure that you have access to all the relevant data you require.


Pimcore can perform data integration activities in several ways. Pimcore's customer relationship management capabilities enable you to centralise and collate customer records from multiple sources, including sales and purchase records, customer contacts and social media activity. Pimcore's advanced CRM technology means you will have ready and easy access to all previous customer interactions.


Pimcore's Product Information Management (PIM) capabilities enable you to create impressive and easy-to-use product records. The PIM tool can contain and combine information related to product information, descriptions, customisation options, and even stock and ordering data. Because of its large and complex catalogues, Pimcore is a great choice for retailers and online traders.


Data integration is a complex activity, and it’s important that you get the process right. As Pimcore partners, the team at Stimulus can help you get your integration project up and running.


Stimulus is a Sydney based boutique agency that offers comprehensive enterprise digital solutions. Our team is made up of seasoned professionals with extensive experience in various areas of enterprise web development, Pimcore development and consulting and data management.


Reviews data integration tools

As described earlier, there are many data integration tools available. Your chosen one should be determined by your business requirements, industry, and intent to scale or grow your data. Three popular tools for integration to consider are:


Informaticais a popular data integration tool that is flexible enough to enable users to build their data workflows. Informatica can handle data from over 50,000 data sources, including AWS (Amazon Web Services), Salesforce, Google Cloud, Microsoft Azure, and more.


Dell Boomi is a popular choice for integration in complex organisations such as universities, hospitals, and government. Dell Boomi's clients include American Express and Expedia, and with such big-name customers, it makes sense to consider this tool. Users do not require developer or coding skills to be able to integrate data from what is estimated to be more than 2000M applications.


IBM DataStage is another option for combining complex data sets. It has been recognised for its good data cleaning capabilities. The tool can assess data quality and make improvements and amendments to improve overall data quality.



Related Questions

Is data integration too expensive?

Licencing costs for data integrations tools vary greatly. Once you have implemented your chosen solution, additional fees can be added. If your organisation continually adds new applications and data to your network, data integration can become expensive. The integration framework must be revised and updated each time a new system is deployed.


The amount you will pay for integration will vary, depending on your data size. Additional costs may be included if your data needs better quality, complete, or comprise both structured and unstructured data.


What is data manipulation?

Data manipulation, also called data preparation, is a process that prepares and transforms data for use in business intelligence and analytics applications. It creates data that can be more easily analysed in other systems. Data manipulation activities include filtering, purging, and merging data. Data technicians and engineers carry out data manipulation using data manipulation language (DML), a component of Structured Query Language (SQL).

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