To remain relevant in today’s world, digital enterprises must bridge the gap between internally and externally generated data while continuing to derive meaningful insights. With the sheer amount of data that continues to be generated every second, he cannot undermine the importance that MDM brings in the age of cognitive computing.
Just as most companies around the world have embarked on their MDM journey, the maturity of an MDM program—the ability to harness the value of diverse data—is what separates chaff from grain. Take a sample retailer who wants to run targeted campaigns across specific customer segments. In these situations, no campaign can take place unless the digital footprint of customer interactions and customer intelligence across multiple channels, touchpoints, and social networks are captured correctly. It doesn’t always give the desired result. Underlying it all is what we call the core customer dimension or master data.
According to recent statistics, “70% of a data scientist’s time is spent collecting and preparing data, not building and deploying predictive models.”
Staying true to the spirit of cognitive computing and enjoying the many benefits of cognitive computing requires strict master data management. This includes elements of syntax, semantics, discovery, integration and quality.
Semantics and taxonomies
Modern data modeling techniques and ML help transform schemas from disparate sources to holistic
A data representation applicable to multiple types and origins of data used to form the golden record of master data. The use of graph technology has brought about a paradigm shift in the data modeling space by becoming schemaless and promoting multi-dimensional views that are essential for all “system of record” use cases.
data architecture
Embedding AI and ML models means rethinking core data storage and consumption. Machine learning systems that require feeds from multiple siled stores of master data reek of inefficient and error-prone processes. In addition, inconsistent definitions with traditional systems become bottlenecks as far as performance and response time are concerned.
Efficient and accurate processes require a single reference system of record.
data quality
Cognitive typically relies on repeatable, correlation-based methods and requires clean, accurate and standardized data. In the absence of a holistic MDM system, organizations are not only dealing with incomplete and inconsistent data, but also significantly sacrificing their time-to-value ratio even though they have invested heavily in data reduction. toedge technology
Just as an MDM is responsible for maintaining a standard data catalog within an enterprise, there is a tremendous amount of work that can potentially be offloaded to AI and ML technologies.Rapid analysis by determining system of record
data stewardship
Stewardship activities are one of the inevitable but essential activities necessary to maintain the confidentiality of master data. But over time and volume, this can take a toll on overall throughput and efficiency. This is exactly where ML can come to the rescue by prioritizing and routing jobs to steward groups based on interpretation from previous results.
extended data
With a multitude of transactions, social media feeds, and big data, MDM itself has great potential to be scaled to build 360-degree views that are essential for AI and ML.
AI and ML demand a vast field of opportunity as far as data is concerned, and MDM plays a very important role.Because cognitive is not just a lot of data, the valuable part is leveraged by the training dataset
Simply put, MDM is the common thread within the data fabric that improves the use of the power of information to help organizations gain competitive advantage.
