Digital transformation is centered around the exciting prospects of artificial intelligence (AI) and machine learning (ML), as well as the growing prevalence of the Internet of Things (IoT). Organizations that effectively integrate these technologies into their operations, business models, and services will have an advantage over their competitors and thrive in the future technology-driven landscape.
However, there are potential challenges along the way, particularly if data quality is not properly addressed. Master data management (MDM) offers a solution to the data-quality problems that can hinder the potential of these emerging technologies and other IT initiatives.
Fortunately, there is a solution to the issues arising from poor data quality in IT initiatives such as AI, ML, and IoT. This solution is called master data management (MDM).
MDM is crucial for these technologies’ success because they rely heavily on high-quality data. MDM involves implementing technology infrastructure and processes that enforce data-quality standards, ensuring consistent and reliable information across enterprise systems.
While MDM is integral to enterprise data strategy, it specifically focuses on managing core, non-transactional data used throughout the organization, such as customer, product, supplier, and location data.
However, if the data used by AI is not of good quality, the outcomes generated by AI processes will be unreliable and may lead to undesirable consequences for businesses. These unwanted outcomes can include:
- Making different decisions for the same scenario.
- Approving a purchase from a vendor despite the parent company being previously disqualified.
- Suggesting a product to a customer even though the same product had been returned by the customer in the past.
Artificial Intelligence (AI) heavily depends on high-quality data to function effectively. While AI can be used to automate business processes and make faster and more reliable decisions, when misused, it can automate the wrong outcomes. These outcomes can have a profoundly detrimental effect on the success of your business, its reputation, and its willingness to pursue new AI projects, potentially resulting in falling behind your competitors.
Master data in multiple business processes must meet various data-quality dimensions to support automated processes. It is important to sustain data-quality efforts to prevent the decay of high-quality data. Unmaintained data will not be suitable for AI-supported processes or for analyzing data in IoT environments. The most compromised data in this challenge is the master data describing core entities involved in these processes.
MDM is the appropriate solution for managing this data and controlling its lifecycle. Complexity needs to be captured and encapsulated in a digital format that machines can understand to enable AI processes. MDM is the ideal solution for providing AI with a clear description of the core entities involved in business processes and IoT environments.
AVOID THE THE ENDLESS DATA CLEANING CYCLE
When faced with data quality issues in AI processing, many people’s initial response is to manually clean the data as a quick fix. However, this approach is expensive and unsustainable and becomes unmanageable as AI is used in more business processes. Instead, a better approach is to address the root cause of data-quality issues by implementing a capable Master Data Management (MDM) solution. A robust MDM solution can connect different enterprise systems, use its own machine learning capabilities to merge and match data, and create a reliable and trustworthy record of customer, product, and other data. By ensuring that the entire organization works from a single, accurate source of data, practitioners of AI and ML programs can be confident in the quality and timeliness of the data they are using.
THE RISK OF INCONSISTENT DATA CLEANING
When preparing training data for machine learning, it may be tempting to clean the data. However, doing so can lead to inconsistencies in the way different AI systems process information. To avoid this, it is important to use training datasets that are derived from already standardized production data with a consistent master data foundation.
Utilizing rationalized data in artificial intelligence is crucial for businesses to achieve positive outcomes. By ensuring that the master data used in AI processes is distinct, precise, consistent, and up-to-date, businesses can rely on reliable results. Moreover, these processes can be repeated consistently over time and the concept can be applied to other scenarios as well.
FINAL THOUGHTS: AI, IoT, AND MASTER DATA MANAGEMENT
The Internet of Things (IoT) has great potential and will lead to the widespread use of connected smart devices in our daily lives. As these devices play a larger role in areas like home security and frictionless purchases, the data that supports these initiatives will become increasingly important. Master data management will also become more crucial as data volumes grow and the IoT market matures with more companies participating.