Overcoming Data Integration Challenges

Six common data integration problems and how to solve them

Last Published: Jun 29, 2023 |
Sudipta Datta
Sudipta Datta

Product Marketing Manager

Data Integration helps control costs, improve productivity and simplify complexity

Why Is Data Integration Important?

In today’s competitive market, advanced organizations must embrace their most strategic asset — data. Basing critical decisions on sound data will separate the disruptors from the laggards. Case in point: In a 2023 survey of CIOs, 38% of respondents ranked transforming existing business processes through tactics like automation and integration as their third most important business objective.

Being able to integrate data of virtually any type, from multiple sources, into a single destination, will help you better understand your business and deliver data-driven insights. These insights can help you become more agile and innovative. And it can help you deliver data-led decisions that improve the customer experience, enhance productivity and drive growth. These are all imperatives to compete in a demanding economy.

That said, data integration can have its own set of challenges. Let’s review the top six challenges of data integration.

What Are the Top 6 Data Integration Problems?

In today’s hyperconnected world, data integration challenges must be rectified as soon as possible. These common problems need to be addressed so you can experience effective data integration across your organization:

  1. Resource constraints
  2. More users across the enterprise are trying to access data. Unfortunately, this can create a heavier workload for the central IT and data teams. According to a 2022 survey, 52% of data leaders indicated that their data integration workload has increased 10–20% year over year. Given today’s current economic conditions, everyone is being pulled in many directions. This can result in delayed or derailed projects.

  3. Lack of qualified talent
  4. The skill gap is widening with the ever-changing technological landscape. In fact, 64% of CIOs say talent attrition is an issue. To stay relevant, today’s data engineers need a combination of business knowledge and technical skills to create an effective data pipeline. Unfortunately, this can sometimes cause a mismatch between what the business needs and what IT delivers. Moreover, without proper automation in place, teams can spend most of their time in repetitive, mundane integration tasks instead of strategizing or optimizing connectivity.

  5. Growing volumes in data, formats and sources
  6. More than 20% of companies recently surveyed by IDG are drawing from 1,000 or more data sources to feed their business intelligence and analytics systems. This data comes in different forms and formats (structured, unstructured) depending on its source application. So, while you technically have all the data, it’s not easy to locate and retrieve it in a uniform way. And to access this data for analytics, you need to set definitions, transform the data through extract, transform, load (ETL) or extract, load transform (ELT) and migrate it without breaching compliance requirements. This is a complex and time-consuming process.

  7. Rising costs
  8. Moving large amounts of data in and out of clouds is expensive. The right cloud data integration tools not only make more efficient use of cloud infrastructure for data processing, but they also deliver performance gains. This helps make traditional tradeoffs between cost and performance a thing of the past.

  9. Technical and operational complexity
  10. As cloud adoption accelerates, data and IT leaders can find connecting cloud and multi-cloud with on-premises environments challenging. Stitching together these disjointed systems can lead to constant do-it-yourself integration, changing roadmaps, project overruns and inconsistent data governance and data quality — not good in today’s fast-paced climate.

  11. Security and compliance issues
  12. Security is a major concern when it comes to moving data from one system to another. You need to be able to control the number of users and their level of access to ensure data is protected and governed. Unauthorized access and misuse of data can harm a company’s business and reputation, resulting in financial losses. In fact, according to a recent study, 29% of CIOs surveyed noted that their CEOs tasked them with upgrading IT and data security to reduce corporate risk.

So now you know the key challenges of data integration. But what you really want to know is: How can I avoid them? Read on to learn more.

How to Conquer Data Integration Challenges

Here’s a quick rundown of the different capabilities your cloud data integration solution should provide so you can avoid common challenges and advance your business.

ETL & ELT Automation

ETL is traditional data processing, where data from virtually any source — such as SaaS applications or on-premises systems — is transformed before being ingested into a target application such as enterprise resource planning (ERP) or a data warehouse for business processes and analytics. Creating workflows and dynamic mappings for your data pipeline can bring a high level of automation and help improve productivity. With the modern cloud data warehouse, data lake and lakehouse, data engineers prefer the ELT process where you push down the commands on the target itself and utilize compute capacity instead of moving the data in and out of the data warehouse for processing. This improves the efficiency of the data pipeline and optimizes costs.

Scalable and Versatile Data Platforms

With the growing number of data sources, it is important to pick a tool that is not only compatible with the data sources that you have now but also capable of accommodating new applications and technologies. You need a scalable platform that can work with many ecosystems, across both hybrid and multi-cloud environments.

Elastic Data Processing

For large data volumes and unpredictable data workloads, your data integration tool should be able to process virtually any data without compromising on performance. Data integration solutions that support massively parallel processing, Spark processing or elastic processing can process multiple terabytes of data simultaneously. This can save you both time and money.

Data Lineage

You need transparency on how data flows, where it starts, what changes it has gone through and where it is delivered. Data lineage provides visibility into pipelines, which helps build trust, detect anomalies and optimize performance. Plus, tools that empower you to track the full data journey can help keep you a step ahead of the competition.

Artificial Intelligence (AI)

An AI-powered cloud data integration solution can reduce development time and runtime of data pipelines. This can save time for architects, business analysts and IT operations. Self-service tools like AI-based multi-step wizards for data ingestion and integration tasks are easy to use — even for non-technical folks. Plus, it eliminates hand coding (which can be time-consuming and potentially costly) when creating and running a data pipeline. AI recommendation engines that are based on cloud financial operations (FinOps) principles can help optimize costs by suggesting processes, templates and configurations.

Serverless Data Architecture

To ease the process of managing the infrastructure, data practitioners are looking for serverless data integration, where zero effort is needed to provision and maintain the underlying cloud instances. This empowers data analysts and practitioners to focus on their core job versus being bogged down with administrative work.

Having a modern data integration tool in place cannot be an afterthought in today’s demanding landscape. A solution that can help reduce costs, improve performance and effectively compete can be the difference between excelling or maintaining the status quo. And Informatica can help.

How Informatica Can Help Resolve Your Data Integration Challenges

In the past, many data integration tools were limited. And some current solutions are still focused on just a particular use case or solving a single challenge. But given today’s digital economy, your cloud data integration solution must provide a full range of cloud data integration options in a secure, stable and unified cloud-native solution for multi-cloud and hybrid configurations. That’s where Informatica Cloud Data Integration, a service of Intelligent Data Management Cloud (IDMC), comes in.

Informatica Cloud Data Integration is a comprehensive, cloud-native data integration solution. It can ingest, enrich and transform virtually any data, anywhere, on multiple clouds or on-premises, for data management and analytics.

Built on a modern technology stack, Informatica Cloud Data Integration is designed to simplify data management, democratize data engineering for practically all users and support enterprise-level scaling. It ensures better control of your data integration cost with AI-powered recommendations and consumption-based pricing.

Informatica Cloud Data Integration offers high-performance ETL, ELT, ingestion, synchronization and replication for a multi-cloud and serverless world. It covers a diverse set of patterns, use cases and users, ensuring you have well-architected and seamlessly automated data pipelines that serve your business.

How to Maximize Your Investment in Data Integration

We get that not all organizations are ready for a comprehensive data integration solution like Informatica Cloud Data Integration. That’s why we recommend Cloud Data Integration-Free to get you started. It’s a free data integration solution to easily load, transform and integrate data. It’s fast, flexible and helps you turn data into insights in minutes. And it scales with your changing business needs.

From there, you can transition (in the same platform) to the pay-as-you-go option, Cloud Data Integration-PayGo.

These innovative tools minimize budget concerns. They also unburden your IT teams with a wizard-driven experience and help jumpstart your data integration journey.

And down the road, when your data integration needs become more complex — like monitoring and improving data quality, maintaining a data catalog or setting up a data marketplace — we have your back. You can use Informatica Cloud Data Integration services and experience the full range of capabilities and benefits.

Real-World Examples of Informatica Data Integration Capabilities

Here are a few examples of how leaders in key industries have overcome challenges with Informatica data integration capabilities.

  • Valley Health System in New Jersey used Informatica cloud data integration capabilities to feed patient data from many systems into Microsoft Dynamics. After implementation, the provider improved patient relationship management and increased their patient appointments by 300%.
  • Banco ABC Brasil, a commercial bank in Brazil and the Cayman Islands, leveraged Informatica data integration and data ingestion capabilities to enable comprehensive data insights across the business. Banco ABC Brasil enhanced its data ecosystem, built around a data mesh architecture, which allowed it to ingest financial data in a variety of formats 110% faster. This helped speed up customer credit applications and treasury P&L calculations, which are now 80% automated.
  • Sunrun, a provider of photovoltaic systems and battery energy storage products, used Informatica data integration capabilities to ingest data from dozens of source systems and build hundreds of pipelines into Google Cloud. This helped them reduce data warehouse design time by 50% and infrastructure building time by 75%.

Next Steps

Are you ready to improve productivity, reduce costs and minimize complexity with your data integration initiatives? Check out these valuable resources.

First Published: Jun 29, 2023