Data Quality, Integration, and the Foundation for AI: What It All Means

The smartest businesses understand that a strong foundation is necessary for growth. Especially in the AI era, secure groundwork allows businesses to properly scale, implement modern technologies, and enable future success.

Today, achieving a solid organizational foundation often comes down to data quality. Because every modern organization relies on business data to make decisions, providing quality data is vital to create proactive success, make better decisions, and enable modern technologies like AI to flourish.

So how can you achieve high-quality data? The best place to start is by getting ahead on data integration.

Why is Data Integration So Important?

To achieve fundamental data quality, data integration is crucial for growing organizations. Integration processes provide huge organizational boosts, connecting disparate systems and ensuring sources of data are reliable and accurate. Doing so prevents redundant information and data silo backups, keeping tools and applications like AI from being hampered by poor data.

Despite this, many businesses find data integration difficult. According to a recent Business Wire article, about 89% of companies struggle with data and system integration [1]. This number aligns with Big Data Wire’s 2024 article, stating that “57% of [surveyed] respondents rated data quality as one of the three most challenging aspects of the data preparation process.”

Nearly 40% of respondents also found that integrating data from multiple sources was a top three challenge [2].

Without data integration, connecting organization-wide systems effectively can be extremely challenging. Often, poor-quality data comes from disconnected systems. When disconnected, these systems can create data silos latent with redundant, error-prone, and unclean data.

As such, effective data integration is vital for modern companies. For most companies, the goal of data integration should be to achieve a single, accessible source of accurate, quality data.

So, what exactly are the benefits of having quality data?

The Benefits of Quality Data

The more solid an organization’s data foundation is, the safer it can scale.

Quality data provides stability across the board, providing accurate information for a company to utilize. Having high-quality data provides important benefits that improve a business’s overall infrastructure.

Some of the biggest benefits include:

Accuracy

Accurate data improves decision-making and minimizes expensive mistakes. Accurate business data improves an organization’s decisions by giving correct information upon which to form decisions. When data is correct, your business decisions will be correct too.

Lowered Costs

Having reliable access to quality data can save organizations copious amounts of money. It means that an organization can minimize costly mistakes, hire far less manual labor, and turn employees toward proactive goals.

Improved Efficiency

Data efficiency works in tandem with lowered costs. Because integration can make data faster and more accessible, it improves the efficiency of business operations, thus increasing savings.

Higher Business Sustainability

With all these factors combined, organizations that prioritize high-quality data are looking out for the future of their business. Providing high-quality data ultimately ensures long-term benefits for a company, enabling a successful future.

These benefits of quality data are a clear-cut way of practicing successful business, both in savings and decision-making.

As well, data quality can vastly improve the functionality of tools like AI, which rely on business data to function.

The Effect of Data Quality on AI

Quality data is the foremost important aspect of generative AI tools. When an AI model is built on poor data, the AI tool simply cannot function at a quality level.

As the saying goes, garbage in, garbage out.

An example of when bad data causes undesirable results is exemplified in AI hallucinations. This phenomenon occurs when an AI model generates incorrect, nonsensical, or misleading information [3].

In some cases, popular language-learning models like Chat GPT3 have had some notable hallucinations. One example, found in Douglas Hofstadter’s 2022 economist article, is when GPT3 claimed that “Egypt was transported across the Golden Gate Bridge for the second time on October 13th, 2017” [4]. Of course, this is completely nonsensical.

While responses like this can be amusing, they’re also indicative of serious foundational data quality issues. And, for organizations seeking to utilize AI models to run business procedures, these errors are simply a liability.

This exemplifies the importance of data integration for business functionality, especially progressing into the AI era. For this reason, many organizations turn toward data integration tools to prevent poor, biased, contradictory, and incomplete data.

Types of Integration Tools

Data integration tools are excellent at improving the data integration processes by ensuring that data is clean and accessible.

Designed to improve reporting, scalability, functionality, and speed in back-end systems, integration tools excel at unifying business data and creating a holistic store of data. These tools also automate manual data integration processes, which often “drives up costs, takes copious amounts of time, and [damages] the accuracy and reliability of data” [5].

When considering data integration tools, using tools that effectively clean data is often the best choice. A tool like Kore Integrate is especially useful in this regard, acting as a filter against poor or redundant data. These tools should be flexible, with multiple methods to achieve quality integration.

Another type of integration tool is middleware. Middleware integration tools unify data by acting as a third-party bridge between disconnected sources. A good example of this is IBM WebSphere, which excels at large-scale, complex integrations through the platform’s many availability, scalability, and security features.

Finally, P2P (point-to-point) tools offer simple integrations that directly link applications, rather than communicating via a third-party application. Because the connection is so simple, P2P tools are generally better for less complex ERPs and could cause issues with scalability. For smaller operations, however, a tool like Oracle’s Fusion Cloud Procurement is a solid P2P option.

Ultimately, by choosing the right data integration tool, you can improve your business’s data quality.

Conclusion

With strong data integration tools, you can improve the foundation of your business data. This is important, especially as technology like AI becomes a prevalent part of modern businesses. Thus, the foundation of your business can be greatly strengthened with the right tools to achieve data quality.

References:

[1] Marchese, Lucia. “Market Study Reveals 89% of Companies Struggle With Data and System Integration; Driving Ipaas Adoption.” Business Wire, November 2, 2021. https://www.businesswire.com/news/home/20211102005932/en/Market-Study-Reveals-89-of-Companies-Struggle-with-Data-and-System-Integration-Driving-iPaaS-Adoption.

[2] Woodie, Alex. “Data Quality Getting Worse, Report Says.” Big Data Wire, April 5, 2024. https://www.datanami.com/2024/04/05/data-quality-getting-worse-report-says/.

[3] Ibm. “What Are AI Hallucinations?” IBM, September 3, 2024. https://www.ibm.com/topics/ai-hallucinations

[4] Hofstadter, Douglas. “Artificial Neural Networks Today Are Not Conscious, According to Douglas Hofstadter.” The Economist, September 2, 2022. https://www.economist.com/by-invitation/2022/09/02/artificial-neural-networks-today-are-not-conscious-according-to-douglas-hofstadter.

[5] Dallinga, Maxwell. “Top 3 Reasons Manufacturers and Distributors Should Use Data Integration Tools.” Kore Technologies, August 20, 2024. https://www.koretech.com/top-3-reasons-manufacturers-and-distributors-should-use-data-integration-tools/