Right now, the data modernization arena is a maze of buzzwords and competing priorities. Everyone’s talking about legacy system migrations, cloud incorporation, data mesh, data fabric, and the latest data lake architectures. But with that most enterprises are caught in a complex web of decisions. And this is because there’s a very little clarity on what actually moves the needle.
Do we lift-and-shift to the cloud or re-architect?
Should we focus on data lakes or data warehouses?
How do we handle our mainframe applications?
What about our decades of accumulated business rules?
Where does real-time processing fit in?
The confusion is understandable. But what’s fuelling this is the fact that modern traditional data modernization strategy often focuses heavily on infrastructure changes (moving to the cloud, implementing new platforms, or building data lakes). But these efforts often miss the bigger picture i.e., how to make these modernized systems actually betterr business value. Because we usually see companies seeing data modernization efforts as a ‘start fresh’ button (which it ich it isn’t). The truth is most organizations aren’t starting from scratch. Because they are already dealing with – mission-critical mainframe systems that can’t just be “turned off”, complex ETL processes built over decades, Undocumented business rules embedded in legacy code and data quality issues that migrate along with the data. And simply moving these systems to new platforms doesn’t solve the underlying challenges.
This is where AI and ML enter the picture – not as additional complexity, but as tools to make sense of and optimize this challenging landscape.
Role of AI in data modernization
1. Automated Data Cleansing
Now we all know one of the most time-consuming tasks in data management is cleaning and preparing data for analysis. Manual data profiling takes months and business analysts spend countless hours documenting data flows. All for hidden dependencies to be discovered only after something breaks. But this won’t be the case with AI-driven tools automating this process.
Common issues, duplicated customer records or inconsistent data entry formats can be easily highlighted with clustering and pattern recognition. Further, the data and outliers that might go unnoticed by traditional profiling methods can be easily identified through anomaly detections. And the best part is GenAI technologies now makes it easier to perform much more intelligent classification and profiling operations of structured and unstructured data, for which intensive manual effort was previously required.
2. Improved Data Quality Management
Now big contributors of poor data quality are inconsistent data formats and manual reconciliation processes. But now as discussed having patterns recognized in data will help in making intelligent adjustments before the actual migration. But there’s more to it.
For starters, Natural Language Processing (NLP) can dive into text fields and suggest fixes for misspellings, inconsistent terminology, and formatting errors. On the numerical side, machine learning algorithms are fantastic at spotting and correcting errors, whether it’s mismatches in accounting figures or incorrect product quantities. AI also uses probabilistic matching to analyze similarities in fields like names and addresses. This means it can confidently merge or remove duplicates while keeping your data intact. Plus, some machine learning models can fill in missing data points based on existing patterns. (But this approach should be used carefully to avoid introducing any bias into your dataset).
3. Advanced data mapping
AI has entered in many aspects of data integration and data mapping is obviously one of them. Think of it this way: you might need to translate “CH” from your source system into “Switzerland” in your target system. Now systems what makes this process even better with the of AI is its ability to uncover complex relationships between fields in both systems.
Unlike traditional methods that rely on manual effort or strict rules, AI can detect patterns and correlations that might not be obvious at first glance. For example, it can identify different naming conventions, spot inconsistencies in data formats, or even find links between data points that seem unrelated. This means that AI not only speeds up the mapping process but also makes it more accurate, helping organizations navigate the complexities of today’s data landscape with confidence. By leveraging AI for data mapping, businesses can ensure a smoother migration and ultimately improve their overall data quality.
Wrapping up
The value proposition of AI-driven data modernization is straightforward: it empowers organizations to establish a resilient and adaptable data foundation. Think of data modernization as akin to a heart transplant for businesses. Just as you wouldn’t rush a heart transplant, modernizing your data infrastructure requires careful planning and strategic safety measures to ensure smooth operations throughout the transition. At Polestar Analytics, we understand this critical nature of data modernization. With the right approach, organizations can modernize their data systems while reducing risks and improving outcomes. The future of data is here, and it’s smarter, faster, and more efficient than ever before.
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