Point Solution Data Integration Tools Fail in the AI-Ready Data Era: Here’s Why
Last Published: Mar 31, 2025 |
Table Of Contents
Table Of Contents
Table Of Contents
Table Of Contents
Table Of Contents
Data is at the heart of decision-making in modern enterprises. It is the one business asset that has proven its value repeatedly, making outcomes more predictable and profitable. No wonder 45% of CIOs say that of all areas of technology investment, data analytics and data science have had the most significant impact on the organization in the last two years.1
As the volume, variety and velocity of incoming data grows exponentially, data integration has become central to making data “business ready.” But data integration in this scenario is so much more than just stitching various bits of data together. The data being connected also needs to be credible and accurate enough to deliver the expected outcomes for advanced analytics and emerging AI use cases.
Business data, like the oil we often compare it to, is useless unless refined and made fit-for-purpose. Data quality is fast becoming a must-have data integration capability. is fast becoming a must-have data integration capability.
In an AI-first world, the role of data quality is even more stark. If “business-ready” data is important for reliable analytics and insights, then "AI-ready" data is non-negotiable for generative AI (GenAI) success.
The Right Approach to Data Quality for Data Integration
Modern, data-led, and AI-ready enterprises no longer treat data quality as an afterthought that a separate tool or retroactive measures can enable. It is an intrinsic capability built-in to the end-to-end data integration process.
This simple but powerful shift in approach to data quality not only provides a robust foundation for reliable insights but also enhances overall data outcomes while lowering cost, complexity, and liability.
Yet, some teams still seem to think that point solutions with limited data quality features or adding additional tools downstream can effectively address enterprise data quality needs.
In truth, this approach only serves to exacerbate the challenge of delivering high-quality data. For instance, this recent survey found that a third of companies (33%)2 are using 5-10 solutions/platforms for data preparation alone. This not only adds to the complexity of data management by adding bottlenecks and obfuscating lineage but further dilutes data reliability and readiness.
Business stakeholders need a single source of truth, which is not only easy to access for data related tasks but also accurate, reliable and timely. In this context, point solution data integration tools fall short of delivering reliable, continuous, analytics- and AI-ready data for modern enterprise use cases.
The Problem with Stand-Alone Integration Tools is the Quality of “Data Quality”
Point solution data integration tools address specific data integration needs, such as extraction, transformation, loading, or synchronization for an organization’s analytics and AI use cases.
Business teams tend to choose these tools because they may seem less expensive in the short run or faster to deploy in a dynamic business landscape.
While these tools may address specific and immediate data integration needs, they often create more cost, complexity and down-stream issues than anticipated. Teams quickly realize the challenges with juggling multiple tools, each with its own learning curve, maintenance requirements, and compatibility issues.
The real problem with point solutions, however, is far bigger than the obvious issues of cost and operational complexity.
The real danger lies in the questionable reliability of data outcomes.
Point solution data integration tools cannot provide the deep, end-to-end data quality capabilities that modern enterprise-scale analytics and AI use cases demand.
A careful look under the hood will reveal that the data quality features many vendors claim are often not robust enough to handle your expanding range of use-cases. They may also be code heavy or not seamlessly built-in to every step of the data integration process, all of which will add to complexity, inefficiency and compliance gaps.
Using multiple point solutions further amplifies the challenge. Inconsistent data quality controls across point solutions can introduce errors that ripple through your analytics and AI initiatives, undermining their effectiveness.
In a quest for efficiency, businesses may inadvertently create a tangled web of tools that hinder their ability to scale and adapt in a dynamic data-driven world.
Equally significant, a lack of reliable results can lead to a slowdown or abandonment of ambitious analytics and AI projects. Worse, poor data governance or inappropriate use of data from AI apps can leave the company vulnerable to compliance and privacy liabilities.
As Informatica’s latest global CDO Insights survey shows data leaders are finding it hard to show the business value of their AI initiatives because of security and privacy concerns around data (46%), a doubt over the reliability of the results (43%) and the quality of the data itself (38%). Over 2 in 5 (41%) of respondents also said they slowed down, paused or stopped GenAI initiatives over the past 12 months.
As data maturity grows, complex integration projects are bound to be slowed down because of gaps in data quality that will have to be addressed sooner rather than later, defeating the very purpose of choosing a point solution as a quick fix.
AI is Shifting the Goalposts for Data Quality and Data Integration
The unprecedented adoption of AI in business has shifted the data quality goalposts altogether. With “AI-ready data quality,” what got you here won’t get you there, because the parameters of ‘‘high-quality data” for AI-use cases differ from what worked for data and analytics.
Perhaps this explains why 59% of CEOs are focusing on foundational data management, which includes data quality, data governance and master data management (MDM) as the area of data and insights capability they will actively invest in over the next two years.
Building data quality capabilities into the end-to-end data integration process ensures all data is always fit for purpose, ready for consumption, and compliant. Unlike point tools, comprehensive data integration solutions offer capabilities that lead to significant business benefits in terms of costs, efficiency and quality of decision-making.
- Built-in data quality capabilities ensure “always-on” data quality enhancement.
- Enterprise-wide data governance delivers consistent data quality standards.
- End-to-end data management capabilities make it easy to plug into upstream and downstream data processes, for seamless integration and scalability.
Future-Proof Data Integration with Informatica IDMC
A comprehensive data integration tool with built-in quality capabilities such as IDMC from Informatica not only meets compliance standards but actively enriches data and improves data integration outcomes.
A claim of best-in-class data quality capabilities must check all the boxes to deliver real value. Aside from a comprehensive set of features, they must be built into the data integration workflow, perform at any scale, and be vendor neutral.
With IDMC, enterprise scale data integration workloads are effortless, and ready for the most advanced analytics and AI use cases.
- Continuously enhanced data quality through better and ongoing integration, cleansing, and validation processes.
- Faster and better decision-making, enabled by consistent, accurate, fit-for-purpose and AI-ready data accessible across the organization.
- Future-proof, vendor-neutral data integration which can handle diverse data formats, sources, storage technologies, and applications which will inevitably continue to develop.
- Risk and liability mitigation with improved data compliance, data governance, MDM and catalogs in an evolving, unpredictable regulatory era.
Conclusion
“Data quality” has become an overused term with all point solutions claiming the feature. But not all data quality capabilities are of the same quality. Check if the features claimed merely check a box or offer a real strategic advantage for data-driven decision making.
If your goal is to compete using data-led analytics and AI, then it is important to consider the longer-term and organization-wide implications of using data integration point solutions with sub-optimal data quality capabilities.
A comprehensive data integration solution with built-in data quality will not only ensure you are AI-ready, but also future-ready, taking on any new data source and technologies without affecting outcomes.
Try out the difference with a comprehensive platform such as IDMC for success in the AI era.
1https://www2.deloitte.com/us/en/pages/chief-information-officer/articles/global-technology-leadership-survey.html?form=MG0AV3
2https://info.sqream.com/hubfs/data%20analytics%20leaders%20survey%202024.pdf?form=MG0AV3
3https://www2.deloitte.com/us/en/pages/chief-information-officer/articles/global-technology-leadership-survey.html?form=MG0AV3
