Businesses across all industries are experiencing a data explosion from a variety of newer sources like the Internet of Things (IoT) sensors and connected devices. As cloud-based technologies continue to innovate, businesses are focusing their resources on leveraging technology and services to gain the most from corporate data assets. This overload of data makes it challenging to keep up with traditional data management systems that don’t give users full access to and control of data. Data agility is important for keeping up with new technologies and the demands of data management. As businesses embrace Artificial Intelligence (AI) and Machine Learning (ML), they need to adopt improved data management technologies to maintain relevance and stay competitive.
One of the best practices businesses are implementing is the use of MDM solutions. Master Data Management ensures that shared corporate data, or master data, is consistent and accurate across the corporate network. Master data and the related reference data and metadata drive business processes, undergo analytics, and are controlled with governance processes.
The right MDM software has key features such as multi-style MDM, real-time access to secure data, data and workflow visualization, and a customizable, user-friendly interface. Successful MDM helps businesses increase revenue, improve employee productivity, optimize supply chains, identify and act on insights from real-time analytics, improve customer satisfaction, and improve compliance.
What’s the future of Master Data Management?
MDM solutions will remain the single source of truth for Big Data analysis, which means businesses will need an enterprise-wide MDM strategy to build future data stores on. This will result in improved insights across all types of data and sources, as well as the flexibility to utilize new types of data. Users will need MDM tools that aid in decision making and knowledge management. Effective data management provides consistent corporate data across the corporate network, helps businesses better understand their customers, increases data quality for better decision making and forecasting, and greater accountability of data throughout the lifecycle of data.
How does machine learning fit in with Master Data Management?
MDM solutions will need to adapt and react to data demands quicker, which is where artificial intelligence comes in. Leveraging machine learning adapts data from a source and distributes it to a consumer quickly. ML uses algorithms to create a prediction based on current data, and the more data it consumes, the more it learns. New data can also be accounted for by looking at prior interactions, eliminating the need for traditional extract-transform-load (ETL) approaches. The deep learning capabilities of ML can greatly enhance MDM so long as users understand what problems deep learning can assist with.
ML is a subset of artificial intelligence that gives computer systems the ability to learn from their experiences without being programmed to do so. Machine learning algorithms fall into two categories, supervised and unsupervised learning. With the supervised machine learning approach, programs can apply previous knowledge to new data using pattern recognition and labeled examples. The machine is taught how to perform a function and then able to understand how to perform the function based on training data.
With the unsupervised machine learning approach, an algorithm is trained to perform functions using unstructured data. Unsupervised algorithms are better able to perform complex tasks and functions than supervised algorithms. There are several machine learning models that can be categorized as either supervised or unsupervised learning or a combination of the two. Future Insights highlights several present-day applications for machine learning including classification of datasets, image recognition, video surveillance, news coverage, financial security and fraud detection, natural language processing, use in the healthcare sector, retail, and social media platforms.
MDM solutions will continue to be the basis for Big Data analysis as an effective MDM strategy is the backbone of Big Data applications. The application of machine learning in MDM will help businesses better leverage corporate data to gain better data analytics and more accurate insights from all data across corporate networks.