In our latest CIO Influence Primer Series, we discuss the latest developments in cloud data integration and how IT leaders embrace the trends to maintain a competitive edge over others.
According to IDC’s “Worldwide Data Integration and Intelligence Software Market Shares, 2022: Controlling Data Amid Uncertainty Report,” the global market for data integration and intelligence (DII) software grew to $8.22 billion in 2022. In recent years, the growth of Cloud-based Business Intelligence and Business Analytics tools and services has resulted in the origin of “data integration.” Data integration is a complex technique that involves understanding different types of information collected from various sources and combining these data sets into a singular unified data set to streamline and accelerate decision-making powered by the BI/BA tools. Big data from the Internet of Things, Cloud-based analytics tools, Software-as-a-Service, and now, Generative AI-based solutions have created new opportunities for DII software vendors and customers. A competent data integration strategy is essential to accelerate their digital transformation capabilities fueled by multi-cloud integrations powered by exceptional AI and machine learning operations that deliver unmatched performance at a fraction of the cost.
BMC Simplifies Application and Data Workflow Orchestration with New Cloud and Data Integrations
In this primer on data integration, we have analyzed hundreds of online resources on the data integration market to bring you the most updated information from the industry. In this article, we will define what data integration is, and what are the key techniques, and tools that are used for data integration. In addition, we would also provide information based on the latest industry-centric examples, and top vendors that provide data integration software tools and solutions.
Table of Contents
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What is Data Integration?
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Types of data integration
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The importance of data integration
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Applications of data integration
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Data integration challenges
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Top data integration tools
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Must-have features in a data integration tool
What is Data Integration?
Data integration is a scientific discipline in the business intelligence and information technology industry. It can be defined as the complex master data management (MDM) framework consisting of various techniques, practices, tools, and standardizations for combining and unifying different types of data from disparate sources into a single unified and coherent business information that can be accessible through a synchronous network of BI/BA tools.
According to Gartner, data integration is done to achieve “the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.”
Matt McLarty, CTO & VP of Product at Boomi attributed the recent spurt of innovations in the data integration software market to the growing complexity of enterprise IT environments. Today, enterprises require complete data integration architectures to account for data democratization, composability, visibility, and control.
According to Informatica, data integration connects the dots between structured and unstructured data sources. It is the process of transferring and syncing different types and formats of data from different systems. Data could be extracted from social media platforms, app information, payment tools, CRM, ERP reports, and others. Users plug the real-time unified data into their existing analytics solutions to generate actionable business intelligence with zero latency.
Qlik mentions the use of data replication, ingestion, and transformation to combine different types of data into standardized formats during data integration processes. The unified data can be stored in a data lakehouse, data warehouse, or data lake for future applications.
Types of Data Integration
Data engineers and analysts work with massive troves of data to harness insights from unstructured and structured data. Data integration processes can be categorized into different classes This classification is done based on the final objectives of data integration and where the information would be stored and extracted from. The common types of data integration are:
- Manual data integration
- Middleware data integration
- Application-based integration
- Uniform access integration
- Common storage integration
It is worth pointing out that, by 2024, manual data integration will be reduced by 50% of its current market share, replaced by augmented data integration practices in a multi-cloud data fabric architecture. Top DII software vendors are already embracing AI and machine learning capabilities to accelerate real-time data integration, delivering instantaneous results to end-users.
Talend, a Qlik company, provides a clear distinction between each type of data integration process. You can refer to this table to pick a process that best suits your business needs.
What is Real-time Data Integration?
As the name suggests, real-time data integration refers to the continuous processing of data every time it is available, with the sole objective of reducing the problem of latency to zero. Real-time integration tools accumulate data from the new Internet of Things (IoT) devices and other sources to improve the overall customer experience and operational efficiency. Though costlier and far more complex than the existing batch processing techniques, real-time integration empowers the BI teams to quickly adapt to the changing market conditions and respond to the situations with agility.
Bharti Patel, Senior Vice President, Head of Engineering, Hitachi Vantara said, “Today’s enterprises often have multi-cloud or hybrid strategies that lead to complex integration processes and data silos that hinder productivity. A modernized cloud environment demands a robust data integration platform that collects, curates, and transforms data from different applications, formats, and locations to derive real-time insights for a 360-degree view of the business to make informed decisions.”
Hitachi Vantara’s Pentaho data integration tool reduces the time and complexity of building and maintaining analytic data pipelines.
Data Integration Practices
To execute data integration, data engineers could use five common practices. These are:
- Extract, Transform and Load (ETL)
- Extract, Load and Transform (ELT)
- Event Stream Processing (also referred to as Data Streaming or simply streaming)
- Application Integration (API)
- Data Virtualization
Due to the rising demand for real-time analytics and insights, DII software platforms are switching to a holistic Integration Platform as a Service (IPaas) delivery model. The modern data integration platforms with IPaaS capabilities can handle larger datasets, complex data fabrics, and data mesh architectures to accommodate constantly evolving AIOps and SecOps demands, moving their practices to ELT, Event Streaming, and API pipelines.
The Importance of Data Integration
Business owners are putting cloud-first strategy at the center of their technology investments. In 2023, cloud migration is not only viewed as a means to enhance organizational productivity but also as a channel to drive digital transformation values. This can be achieved by streamlining the entire big data management architectures and practices and pivoting on compliant and secured integration, scaling, and data warehousing capabilities. Data integration, therefore, is a 360-degree transformation of data management practices that needs to continuously evolve with business requirements, technology capabilities, innovations, and regulatory changes.
Business data integration directly impacts the business intelligence ecosystem by supporting the operations for enterprise reporting, analytics, and risk assessments. To remain competitive, organizations rely on data integration solutions and tools to integrate data from different systems based on interoperability features.
Organizations derive the following benefits by improving their data management with world-class data integration practices:
- Access to high-quality data streams for real-time business decision-making.
- Data-centric analytics pipelines are directly accessed by BI teams.
- Empowerment of data science teams that can easily access self-service analytics and reports.
- Reduction in data silos across data warehouses and data lakes.
- Improved IT efficiencies using multi-cloud tools and platforms with better visibility and control.
- Data-driven IT outcomes that support advanced technologies such as generative AI, Deep Learning data lake development, IoT analytics, real-time analytics, and cryptography.
Barry Shurkey, CIO, NTT DATA said, “During a data integration process, it is critical to keep sensitive data like personally identifiable information (PII), protected health information (PHI), payment card information (PCI), and information about intellectual property out of the hands of hackers and competitors. Therefore, it is recommended to incorporate cybersecurity into data platforms and services from the beginning.”
Applications of Data Integration
Getting it right with data integration means that your IT and data ops teams have a clear view of the economics and the long-term value of investing in a high-quality IPaaS vendor. It could also mean that you are ready to use Cloud and analytics as a catalyst to build new future-ready applications and take advantage of the AI offerings. We have gathered the top examples of data integration applications from the business world.
Marketing data integration
Marketing data sources are exploding across platforms that could have billions of users and subscribers. To target these users, the marketing teams could be running hundreds of campaigns across their website, social media channels, e-commerce, mobile applications, emails, and chat messengers.
Collecting marketing data from multiple platforms becomes a complex and long-drawn task. Top-rated data integration platforms unlock customer data intelligence in real time. Marketing data integration enables the Martech teams to create a unified view to build an insights-driven personalization customer experience platform. Martech vendors and users rely on advanced data integration tools to streamline the data from multiple sources or databases, pushing them into a single unified repository to meet immediate and long-term organizational goals.
Healthcare data integration
In the quest to embrace 100% digitalized wellness before 2030, healthcare data volume is skyrocketing in 2023. It currently accounts for 30% of the world’s total data. It is growing faster than the data produced by organizations in the financial services, media and entertainment, and manufacturing sectors. Wearables, healthcare and personal wellness apps, games, and IoT devices produce data at an unprecedented level. As patients and healthcare service providers demand personalized omnichannel customer experiences and real-time information, the issues related to data quality can only be tackled by adopting data integration best practices. Therefore, leading healthcare cloud and data management companies are leveraging world-class data integration platforms to control costs, improve resilience, and deliver superior healthcare results.
Financial data integration
Big data analytics (BDA) in finance is a game-changer. Data powers a majority of fintech innovations that we see today. The finance industry produces petabytes of structured and unstructured data every year which influence the quality of results derived from the supply chain. This big data is used to make contextual decisions for a variety of tasks. Commonly, big data analytics is used for stock market analysis, inventory management, payment processing, risk identification, revenue maximization, fraud detection, and customer relationship management. Finance organizations rely on data integration solutions to meet regulatory requirements, improve customer satisfaction, break down data silos, and improve the overall cybersecurity stance.
Manufacturing data integration
Multi-cloud environments have replaced traditional EDIs in the manufacturing sector. Manufacturing companies use multiple cloud services for their business intelligence needs. Enterprise data integration for manufacturing companies connects data sources from different IT or OT systems to a unified platform. These support multi-cloud analytical and anomaly detection operations during critical business operations, such as furnace ignition, temperature regulation, equipment maintenance, automation, and parts replacement. Using real-time data integration, manufacturing companies are able to make accurate forecasts with a fuller picture of their plant operations.
Data Integration Challenges
To extract maximum value from data integration platforms, you should be aware of the common challenges associated with this discipline. Here are the top data integration challenges that BI teams face.
- Lack of strategic planning for data management
- Over-reliance on manual data integration
- Excessive data volume and variety
- Poor quality data
- Duplicated data
- Poor data labeling
- Data latency
We will explain these challenges in our upcoming articles.
Top Data Integration Tools
Choosing the best data integration software and solutions provider can be a bit of a challenge. There are hundreds of data integration tools in the market. These tools can be classified into four families:
- Cloud-based data integration tools
- On-premise data integration tools
- Open-source data integration tools (Apache Kafka, Pentaho Kettle, Talend Open Studio, Scriptella)
- Proprietary-based data integration tools
Here are the top data integration tools that feature prominently on our CIO Influence RADAR program in 2023.
- Pentaho Intelligent DataOps Platform by Hitachi Vantara
- Oracle Cloud Infrastructure
- Boomi
- AWS Glue
- IBM DataStage
- Qlik Replicate and Talend Data Fabric
- TIBCO Messaging
- Informatica Intelligent Data Management Cloud
- Matillion
- DataPARC
- Rivery
- n8n
- Integrate.io
- CloverDX
- SAS Data Management
- Precisely
- CData
- SnapLogic
- Fivetran
- Jitterbit
Must-have features in a data integration tool
Data integrity
Different types of errors may affect the quality of data integration. A successful data integration demands complete data integrity. It consists of logical integrity as well as physical integrity of data during the transformation. Data integration is defined as the completeness and consistency of data in adherence to the existing data compliance and governance. It means that the data integration process has been carried out in accordance with the accepted data management standards throughout the lifecycle.
Big Database Support
The top data integration platform should support big databases such as Tableau, Teradata, Oracle, Snowflake, redshift, BigQuery, Azure Synapse, Hive, MongoDB, and others. Supported data sources accommodate your ever-growing business requirements without restricting your agility and compliance governance.
Automated Scalability
Your data ops team should be in control of every mission-critical integration process using automation. It takes care of your daily scale-up-down initiatives according to your data goals.
Tarun Chopra, Vice President of IBM Product Management, Data and AI said, “I believe that CIOs should focus on tools and solutions leveraging data locality of workload execution to ensure that transformation, enrichment, and data quality remediation takes place where data resides. Data Integration workloads can vary based on the use case, so it’s important to select the right technology patterns replication, change data capture, batch, virtualization, or event-driven/streaming instead of forcing fitting all use cases into a single form factor.”
Generative integration
Generative AI tools are disrupting data integration best practices. SnapLogic introduced a “generative integration” approach to tackle the growing complexities in the data ops marketplace. It leverages Generative AI and Large Language Models (LLMs) to generate code for data integration tasks.
Brad Drysdale, Principal Solutions Engineer, SnapLogic said, “Generative integration is by far the biggest revolution in our field, with some of our customers reporting time savings upwards of 80 percent. Having launched the first generative integration platform this year, we have seen customers develop their own new solutions, like using SnapGPT to document new and existing pipelines and create new products. Generative artificial intelligence (AI) and machine learning (ML) capabilities are now at a point where they can significantly improve integrator productivity and deliver measurable time to value and ROI. AI-augmented data integration platforms speed up the ability to identify, access, connect, and move data.”
Real-time API Management
A powerful data integration platform should provide seamless API management with a master data management hub. It should facilitate full-cycle data orchestration and democratization for numerous applications beyond data integration. This could mean establishing a total system integration at an enterprise level for IT and Operations data, including MES, ERP, etc.
Final Thoughts
Data integration and related intelligence are pivoted on transparent metadata management architecture. It ensures that the data ops pipelines strive for greater accountability, scalability, and compliance. Business units led by CIOs should accomplish their AI-powered data integration plans in 2023 by adopting scalable cloud-based software solutions. Data Ops should align with the overall digital transformation goals consisting of customer experience management, master data management, information security and compliance, and business intelligence. By bringing top resources to data ops practices, CIOs can ensure cloud and application integrations can sustain the growing challenges in an uncertain world.
