By Steven Lin
Disclosure: AI was not used to generate this post – though it was tempting.
This five-part blog series shares our opinions on and approaches to leveraging AI in your Master Data Management strategy. We will explore Semarchy’s history of using AI in MDM, our current approach, our vision of which industries are priorities when developing AI/ML tools, some examples, and how we are embracing this future.
First, let us invite you on a trip down memory lane.
A 10-Year History of Semarchy, MDM, and AI
- 2011: Semarchy founded in Lyon, France
- 2014: First pre-packaged AI/ML solution in xDM 3.0 with native Google Translate enricher to automatically translate text in xDM applications to over 80+ languages
- 2015: Semarchy nominated as “Cool Vendor” by Gartner
- 2019: Semarchy Recognized as Gartner Peer Insights Customers’ Choice for Master Data Management Solutions
- 2020: Launched REST API in xDM 5.2 to easily enrich your data from any content provider, web service, or AI/ML solutions
- 2021: Semarchy Named a Leader by Gartner in Magic Quadrant for Master Data Management Solutions
- 2022:ChatGPT Released to the world and immediately available to be used with Semarchy products
Wisdom from over a Decade of MDM Experience and Leadership
When new technological innovations such as generative AI are introduced, we often allow our opportunistic ambitions to outpace the strategic realities needed to achieve them. After all, people and processes are why 85% of data initiatives fail, not technology. This likely explains why, after millions of dollars invested in a data initiative, your teams still revert to using Excel – again.
Sound painfully familiar?
After over a decade of successfully delivering hundreds of MDM implementations, we have developed a leading approach for data ambitions of any size. This approach ensures the rapid implementation and adoption of data management apps, while creating a robust information infrastructure to leverage new innovations like AI.
Semarchy has always understood the impact technologies like AI can have on optimizing data management for business users, data stewards, and designers. We are obsessively customer-first.
This is why we developed pre-packaged AI capabilities in xDM nearly eight years before ChatGPT was released. We purposely build our products with a robust, open architecture, empowering you to leverage any state-of-the-art models in as little as five minutes to augment your teams’ data operations.
Just like we had the foresight to adopt AI in our leading MDM platform nearly a decade ago, we also understand that this new wave of AI innovations has unique opportunities and challenges. We are following AI/ML leaders like OpenAI, Google, Meta, and Anthropic which may be leveraged in our flexible, open innovation ecosystem.
The Duality of AI Opportunities and Challenges
Undoubtedly, these new AI/ML technologies are extremely powerful tools beyond just MDM use cases. However, with any great opportunity, healthy skepticism should be considered to ensure your data management initiatives are secure, strategic, and sustainable.
The Good: Latest research by IDC from over 2,000 early adopters of AI shows that they improved customer experience (CX), rate of innovation, competitiveness, margins, and employee experience (EX) by over 25%. Embracing AI will no longer be a nice-to-have but a necessary-to-have. Specifically with MDM, we envision AI as a copilot for business users, data stewards, and designers to optimize their work.
The Bad: On the other hand, McKinsey reveals that 99% of organizations using AI believe there are relevant risks and challenges related to AI, but on average, only 16.5% are working to mitigate them. The top five generative AI risks and challenges are inaccuracy, cybersecurity, intellectual property infringement, regulatory compliance, and explainability.
Such a stark difference between organizations’ challenges and active efforts to mitigate risks proves that AI’s future requires a few key elements.
- A strong understanding of the opportunities/challenges that leveraging AI will solve
- A focus on rapidly delivering value on foundational data assets to accelerate AI
- A modern, open, and flexible data ecosystem that can quickly adapt to changes
These are the key elements we prioritize to determine our approach to embracing the future of AI and continuing to ensure your successful data initiatives for the next decade.
What Is Semarchy’s Current Position and Future Approach to AI/ML in MDM?
Our AI/ML approach is the same as our leading approach to MDM – understand the problem, apply the technology, and rapidly deliver time to value pragmatically.
This means we will continue obsessing on helping you truly understand your data problems and apply the power and flexibility of our products to deliver rapid results in under a quarter.
We will focus on what we do best – provide the highest quality Master Data Management and integration software for any environment and domain. This allows you, our customer, to apply the specific technologies, AI or not, that are best adapted to your needs.
We will empower your organization to rapidly establish an adaptive information architecture today without constraining your ambitions for the future.
Our position on prioritizing core MDM capabilities does not mean we are bearish on AI in data management. In fact, we’re incredibly optimistic. However, we know that allowing you to quickly leverage third-party AI/ML technologies with Semarchy brings higher value and agility to you than the AI/ML that we develop in-house. The last thing we want is to prematurely invest in, develop, or unveil AI capabilities that fail to “deliver on their promises” just to stay relevant while ignoring the foundational needs of our customers.
We are listening, researching and thinking about how native AI capabilities may fit in our unified data platform. If your AI ambitions need a data leader, we’d love to chat.