When quality data is used for business insights and data analytics, enterprises do better in revenues. And extracting these insights from high volumes of enterprise data requires robust and seamless data integration, either manually or with the help of robust automation tools. Businesses store their data in a multitude of databases, data lakes, repositories, and file systems–ranging from legacy to modern–that vary in formats. This bulk of data increases rapidly and daily, and not all is useful; most of it includes outdated, incomplete, compromised, inconsistent, or simply “bad” data, which 77% of businesses attribute to having a direct effect on their bottom line. Extracting analysis-worthy information from this deluge of big data is a critical, yet challenging task (due to the sheer volume and velocity of incoming data), which can be accomplished by a data integration software.
What is Data Integration?
The definition of data integration is the process of combining, cleaning, and presenting data in a unified form. This includes bringing together data from a wide variety of source systems with disparate formats, removing duplicates, cleaning data based on business rules, and transforming it into the required format. The data integration layer points to the change between raw integrated data.
However, data integration also covers various areas in big data management like data migration, application integration, and master data management. Code-free tools help business users access data from different sources in real-time and comb through business data lakes and repositories to derive business intelligence faster.
Consider the following database integration example: data from two sources (file and database) is merged and sent to a database destination. Data quality rules are applied to the phone column, and the fields with errors are logged separately.
Data integration example explained through a sample dataflow
A business using this dataflow or data integration software can ensure that all errors within the required fields are handled suitably and that the data flowing into the final database destination is actionable.
The need for data integration across different industries is broad and varies depending on the business’ needs and the volume and complexity of data. For instance,
- A health center may need integration software to consolidate and manage its multi-source real-time data related to patients and employees,
- An online vehicle buy-and-sell business may need it to update millions of records daily, and cut down customer onboarding time from months to hours by mapping the client data to the company database, and
- An office of investments may need it to map the institution’s endowment data from disparate source systems (including both internal systems and external money managers) into a tracking software program for risk analysis.
For each business data integration use case, a process can be constructed to automate manual tasks and streamline processes for accuracy. And while the specific needs may vary, at its core, the data integration system covers the processes of combining, cleaning and moving data from source(s) to destination, all of which can be done using different approaches.
Common Data Integration Approaches
Data integration techniques have evolved over the years from manual to automated solutions. Depending on the varying business needs, the process of integrating data from disparate sources can be implemented using any of these approaches.
This data integration technique involves a user manually collecting data from disparate source systems, applying quality rules to clean it, and uploading it to the target databases. It also involves hand-coding for every new use case to ease the mapping of datasets.
In a middleware solution, a virtual “pipeline” is created between multiple systems that allow bi-directional communication. This connectivity streamlines integration tasks.
- Data warehouse/physical data integration
This technique includes data moving from the source system to a data warehouse or other physical destination like a data lake. Businesses prefer this process due to the ease and flexibility in storing, viewing and managing all their data in a centralized location.
There are two approaches to this method: ETL (extract, transform, load) and ELT (extract, load, transform). Both techniques employ the three individual processes of extracting, transforming, and loading data onto a destination. However, the main difference is where the staging area resides for the data transformation process.
- ETL (Extract, Transform, Load)
In this ETL data integration approach, data is extracted, the transformation logic is applied, and the resulting data is loaded onto the target database or data lake destination. Due to the extensive availability of frameworks and tools that support ETL, this approach is great for businesses that need to integrate and process large volumes of data, though the processing time is higher for larger volumes.
- ELT (Extract, Load, Transform)
In this technique, the extracted data is first loaded onto the target destination, and the transformation logic is applied within the database or data warehouse. Because the ETL infrastructure is removed from the equation and the transformation occurs directly within the database, the total power consumed by the system and the data latency is significantly reduced.
- Data virtualization/Data federation
Data virtualization takes a completely different approach from physically moving data to and from databases. In this process, data is not moved across the systems—instead, an abstraction layer provides a unified view of the disparate systems, leaving the data exactly where it is physically. Data analysts can then request information through the virtual layer, which contains the metadata to access the sources. This process allows businesses to get real-time access to their data without exposing the technical details of the source systems, and quickly make enterprise-wide changes on the virtual layer instead of first consolidating the data in one place or implementing changes at each source separately. This integration approach does not support bulk data movement, although it can run alongside ETL or ELT processes.
Types of Data Integration Tools
List of common enterprise-level data integration tools used for consolidating data from multiple data sources to a data warehouse include:
- On-premise Data Integration
On-premise integration tools are launched locally, using an enterprise’s servers, and are typically used by businesses that process legacy and/or higher volumes of data.
Who uses on-premise data integration solutions?
Businesses that require full control over the tool and have big data architects to set up workflows as the need arises.
- Cloud-based Data Integration
Cloud-based integration tools are hosted on a third party’s servers and are usually iPaaS (integration platform as a service) solutions. In most cases, these solutions are web-based. However, it is also important to note that people often get confused between ETL vs iPaaS. iPaaS, a type of data integration technology, is considered as “the successor” of ETL.
Who uses cloud-based solutions?
Top cloud-based ETL tools help businesses with a simple use case, where their big data is routed to a workflow and the transformed data is loaded to the preferred destination(s).
How Data Integration Tools Help Businesses?
With the massive influx of information coming from multiple source systems, businesses need to proactively handle the five Vs of data—value, variety, velocity, veracity, and volume. With a robust integration tool, an enterprise can extract the most, standardize the variety of information, deal with the data velocity on time, improve the veracity, and easily process volumes of data. Here are some of the ways how tools that data integration companies use help grow their businesses.
- Faster time-to-value:
Businesses use approachable tools to create a single source of truth for their data and speed up their internal processes, reaching valuable insights faster. For instance, Randolph-Brooks Federal Credit Union wanted to migrate their legacy data, clean it, and convert it into various formats. What would have taken them a week, only took them half a day with an integration tool. Similarly, healthcare data integration can help doctors to efficiently make time-critical decisions.
- Smarter, better-informed business decisions
A smart data integration approach allows businesses to better manage, measure, monetize, and make targeted decisions based on quality data. With integration tools, business users can directly access data they need without having to constantly request it from IT, get a complete view of their customer behavior and use strategic insights from their clean data to gain an edge over the competition.
- Maintain quality data and improve revenues
Data quality correlates directly to the positive or negative impact on business decisions. When data is up-to-date, clean, and insightful, businesses can improve their revenues by up to 66%. With a high-quality database to extract insights from, business decisions are better sculpted to meet their goals without being hindered by bad-quality data. However, cloud-based data quality tools further offer secure and mobile access to data which can aid disaster recovery and collaboration.
Choosing the Right Enterprise Data Integration Tools
When evaluating enterprise data integration platforms, it is imperative to ensure that the solution offers a host of features that will make your data journey easier. Here are some features – based on common use cases – that you should look for in a data integration solution:
- Bi- and multi-directional data synchronization
In many use cases, data does not only need to be transformed in one destination, it also needs to be updated in systems to maintain consistency and ensure the authenticity of the data throughout the business network. An integration tool should be able to offer accurate and timely synchronization between the connected systems.
Sample of multi-directional data sync in Centerprise
- Workflow automation
Data integration is generally not a one-time job. The incoming data sets usually need to be cleaned, transformed, synced, and made available to the intended users multiple times. Trigger-based workflows allow data scientists to automate repetitive tasks and simplify the integration process. Users can easily schedule a workflow to run it at a specific time or trigger it once a specific event criterion is met.
Sample of workflow automation in Centerprise
- Quick data processing
Businesses can assign more time and resources on enterprise scaling and other revenue-based decisions once they decrease the usual time it takes for integration tasks and replace with faster solutions. A robust integration tool should be able to process volumes of data quickly and efficiently, without consuming too much time for any part of the process.
For industries where processing and analyzing volumes of data is critical and has a direct impact on their clients, such as in finance and healthcare, this feature can ease much of the integration tasks and ensure that the data latency is minimized to a manageable level.
- Support for multiple source systems and formats
Enterprises work with multiple formats and sources of data, including legacy and modern formats and structured, unstructured and semi-structured sources. An integration tool should offer support to all of these to provide a complete solution.
Sample of dataflow with multiple sources in Centerprise
- Data cleansing and profiling
Missing fields, duplicates, and invalid data are major data quality issues that hamper the effect of otherwise smart business strategies, and instead, result in negative customer experiences and missed opportunities. Data cleansing is a component of the integration process that identifies and weeds out the bad data and ensures that business analysts have the most updated information to derive insights from and base their strategies on.
Sample of data profiling in Centerprise
- Instant data previews
When creating complex data models and workflows, it is important to be able to preview the input or output data at any node in the flow before execution. Data previews allow for better flexibility and visibility into the mappings and enable users to check for issues at various instances and correct them before running the entire flow.
Once the data is clean and updated, business analysts need data profiling to extract valuable statistics, insights, and summaries from the database which they can utilize in informed business decisions. Both these features are must-haves in an integration tool.
Sample of data preview in Centerprise
Streamline Enterprise Data Integration with Centerprise
Astera Centerprise is an industry-grade, high-performance automated data integration solution that helps businesses make the most of their existing and incoming data with easy mappings, transformations, pre-built connectors, and more. With the ability to process volumes of data with its powerful parallel-processing ETL engine and supporting a wide range of source systems and formats, the tool eases the way to enterprise integrations.
Whether you want to translate complex schemas, use pushdown optimization to reduce your processing time, update and manage data in real-time or migrate your data to different database location(s), Astera Centerprise integration platform can help you set up and improve your data process without any manual coding thanks to its drag and drop designer. Download the free trial today and experience the benefits for yourself!