In our previous post, we discussed the nature and benefits of the Design Thinking approach that we follow since the beginning of our Community and which aims to develop an AI roadmap for enterprises that are willing to adopt the new technology. This piece introduces our recent development – the Intelligent Data Management concept focused around data extraction topics, enabling companies to harvest low hanging fruits and quickly enjoy the value of AI-driven enhancement.

Leverage the expertise gathered within the Global AI in MDM Community. It is still not too late to jump on the train.

When innovation meets feasibility

Many activities in MDM are repetitive and standardized. While modern technology is already able to bring efficiency gains through modern approaches like business rules for derivation, AI can take it to a whole new level and free up your employees to engage in more value-adding tasks, such as data quality management and data analysis.

The IDM (Intelligent Data Management) long-term vision is to automate all repetitive activities within the data management cycle, such as extracting information for material description or classification from various documents types, comparing information that is in the system with the source information, enriching data based on web-crawling, automating requests for data creation or change. The approach supports all phases in data management: from data extraction, through understanding to maintenance in the system.

Our IDM approach at CAMELOT focuses on the data extraction field – one example of it is retrieving information from unstructured sources (i.e. scanned documents). Another instance is utilizing the power of web-crawling and open-source data to obtain the relevant information for optimizing processes across the value chain, from vendor hierarchy optimization within procurement activities to fraud detection in the finance area.

Further emphasis of the IDM service is on the data understanding. This stage is supported by the AI-based data analysis and processing – IDM brings additional insights to ease the decision-making process. So far, the rule mining approach has found its application in deriving supply chain scenarios.

Finally, the data maintenance phase entails automated population of the ERP system, consistency checks, classification and matching. The idea behind IDM is to leverage the expertise gained through past projects and deliver it to further customers, adapted to their requirements, processes and data. Since the approaches have already been validated, the outcome and deliverables are solid and secure, and the implementation is accelerated.

Transforming the value chain with IDM

The main goal of the IDM approach is to leverage the way AI transforms the human-machine interaction processes and optimize data-related activities across the value chain, thus bringing additional benefit to companies. IDM converts the power of AI into value through enhanced decision making, improved data quality, lower costs and higher speed of processes implementation:

By handling over manually conducted tasks to algorithms, the quality of the end data can be significantly enhanced via excluding the notorious human error. This factor alone has an immense positive impact on the efficiency and speed of the daily work. Automation of single tasks like the input or change of data entries in the systems, on the other hand, can notably reduce efforts required to meet the compliance standards. Additionally, information extraction from the Web can provide previously unexplored data insights for decision-making support and building a competitive advantage on the market.

Besides increasing efficiency and value generation, IDM also carries the vision of human-centred functionality – user-friendly UI and possible integration of IDM solutions into SAP or internal enterprise systems make the process easier and more intuitive.

This combination of realistic AI technology scaling and the feasible approach to its implementation allows for a simplified yet more effective data management cycle.

Combined effort

The smooth implementation of AI technology is conditioned by many factors, but the key to success is the ability to correctly identify the business case that will bring guaranteed value and have a higher potential for generalization. We believe that this is achievable through combining the IDM approach with the data and knowledge companies have at their disposal. The data itself and customer-provided business insights are at the core of the IDM approach that encompasses MDM-based experience and validated data science models.

The knowledge we gathered at CAMELOT during the previous successful implementation of multiple PoCs allows companies to get inspiration from industry peers and profit by the established experience in IDM. The approach entitles several phases: from business case definition and data understanding to solution design, enterprise-wide implementation and hypercare.


CAMELOT is the home of the AI in MDM Community – the collaboration platform for AI forerunners with over 100 members. The Community is built on more than 15 years of proven track record in data and information management. Within the Community, we have already delivered many successful workshops and several PoC projects. CAMELOT combines data, people, technology and processes to deliver solutions that build competitive advantage at any stage of the value chain. Reach out to us and start your AI-driven journey!


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