Big Data

3 Complex Data Integration Problems Derailing Your Analytics and How to Solve Them

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In a recent Leadership Perspective Survey from Evanta, nearly 900 CIOs indicated “data and analytics” as a top priority to ensure their enterprise goals are met.[1] To address this priority, collecting customer data is a start. But the true value comes in applying that data to make smarter business decisions.

Your customer data is likely spread across multiple systems. This means you first need to undergo the complex process of cleaning, moving and standardizing it. Data integration can help with these tasks, but attempting to do it manually or with suboptimal integration tools can be slow, costly and error prone.

Here are three main challenges organizations frequently face when addressing data complexity and tips on how to overcome them:

  1. The data connection barrier

The better your connections across data systems, the stronger your customer insights will be. Just as it’s difficult for a doctor to make a diagnosis without knowing all of a patient’s symptoms, businesses cannot fully understand their customer without connecting data from diverse sources.

You also need to incorporate relevant data from other areas — such as sales, billing or service — to understand the customer journey. But picking an ineffective data integration tool can be as suboptimal as hand-coding. These options can end up reinforcing silos or adding to complexity and cost.

How to break down the data connection barrier

A smart data integration tool can connect many data sources to a central cloud repository. Start with a proven data integration solution that makes it easy for data professionals to create pipelines. That way, they can connect virtually all their data sources to a central enterprise data warehouse.

The Informatica Data Loader offers a simple and free way to create reliable pipelines between most data sources and all major cloud data warehouses. Data analysts can easily load billions of rows of data in virtually any format in minutes, without the need for technical expertise.

  1. The data quality barrier

Once all your data source systems are connected, data practitioners can reach a barrier because data comes in different formats with redundancies, gaps and other inconsistencies. This data is often unusable without undergoing data transformation, or heavy-duty prepping.

Using inaccurate data for analytics can lower confidence in the resulting insights. And because all data has a shelf life, you don’t have time to waste between collection and analysis. But speed should not come at the cost of data security and data governance.

How to remove the data quality barrier

Because customer data streams nonstop from multiple sources, ad hoc data cleansing is inefficient. Enterprise data practitioners need an automated, ongoing data cleansing and transformation process that prioritizes factors such as data consistency, accuracy, security and governance.

Informatica Cloud Data Integration-Free is a true end-to-end ETL solution that delivers high-performance, high-speed and high-scale data transformation. This helps ensure virtually any data being fed into BI or analytics engines is cleansed, normalized and ready for use.

  1. The data usage barrier

Advanced data integration systems are often too complicated for departmental teams to use independently. Basic systems might be more user-friendly but cannot deliver the performance the enterprise needs.

Analysts are often dependent on central IT for advanced data transformations or data analysis use cases. This can delay data analytics and derail time-sensitive go-to-market strategies.

How to eliminate the data usage barrier

An advanced data integration tool can make it easier for data practitioners to find, understand and use data. The ideal solution should be:

  • Easy to deploy: Your data professionals should not need to code or create patchwork fixes. Instead, look for a no-code system that enables your data analysts to load and transform data without technical expertise.
  • Easy and efficient to use: Most modern enterprise IT teams get a high volume of data requests. Departmental data practitioners cannot address these requests with ad hoc reports. Instead, use a tool with codeless point-and-click mapping interfaces, simple drop-down menus and pre-built data transformation templates to automate recurring tasks.
  • Easy to scale: As your data maturity grows, your systems need to handle higher processing volumes and advanced use cases. Your data integration tool should be able to increase processing volumes and introduce additional features without disruption.
  • Affordable: The high cost of data management operations often forces data teams to select suboptimal solutions. Look for an affordable advanced data integration solution that data practitioners across the organization can use.

Departmental teams may be overwhelmed by the data deluge, but with the right tool, it’s easy to overcome typical barriers derailing data analytics outcomes. Learn more about how to easily load, transform and integrate your data at



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