Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the model predictions is critical for adoption. For link prediction in master data management, we have built a number of explainability solutions drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we present explanations for link prediction in a creative way, to allow users to choose explanations they are more comfortable with.

A recent Accenture research on the state of machine learning (ML) in enterprises indicates 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. The key challenges in scaling ML in enterprises are availability of quality data and ability to explain ML outcomes; here we will discuss how Master Data Management (MDM) can help address these challenges. In 1959, Arthur Samuel defined machine learning as "... gives computers the ability to learn without being explicitly programmed". And how exactly is that achieved? At its core, machine learning is the application of statistical methods to uncover patterns in the training data and make predictions or decisions without being explicitly programmed.

Last month, we announced the Informatica Intelligent Data Management Cloud. One of the key attributes of our industry-first offering is AI native at scale. CLAIRE is the AI powerhouse behind our Intelligent Data Management Cloud. Built on an enterprise unified metadata foundation, it provides AI-driven automation of data management activities. In this blog post, I'll discuss 10 ways AI improves master data management (MDM).

Data governance in data-driven organizations is a set of practices and guidelines that define where responsibility for data quality lives. The guidelines support the operation's business model, especially if AI and machine learning applications are at work. Data governance is an operations issue, existing between strategy and the daily management of operations, suggests a recent account in the MIT Sloan Management Review. "Data governance should be a bridge that translates a strategic vision acknowledging the importance of data for the organization and codifying it into practices and guidelines that support operations, ensuring that products and services are delivered to customers," stated author Gregory Vial is an assistant professor of IT at HEC Montréal. To prevent data governance from being limited to a plan that nobody reads, "governing" data needs to be a verb and not a noun phrase as in "data governance."

Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, risk assessment etc. Graph Neural Networks have been shown to be effective in predicting links with few or no node features. While a number of datasets exist for link prediction, their features are considerably different from real world applications. Temporal information on entities and relations are often unavailable. We introduce a new dataset with 10 subgraphs, 20912 nodes, 67564 links, 70 attributes and 9 relation types. We also present novel improvements to graph models to adapt them for industry scale applications.

STERLING, Va., Dec. 5, 2019 /PRNewswire-PRWeb/ -- EnterWorks, a leading provider of Master Data Management (MDM) and Product Information Management (PIM) solutions, has announced a live webcast event featuring Michele Goetz, Principal Analyst, Business Insights, Information Architecture and Artificial Intelligence, at Forrester. The webinar, "How AI, Machine Learning and Data Strategy Can Enable Compelling New Products & Experiences," will take place on Tuesday, December 10, 2019 from 11:00 am to 12:00 pm EST. It is sponsored by EnterWorks; Amplifi, an information management consultancy that helps the world's leading brands, retailers and manufacturers to harness and unleash the power of their data; and Sisense, a business intelligence software and analytics platform. The webinar will inform participants how artificial intelligence, machine learning and data strategy can enable compelling new products and experiences, and how deploying AI and ML depends on effective master data and its proper governance. According to Forrester's Goetz, many companies have initiated AI and ML projects only to find that they have not established the foundation for success that comes with implementing a comprehensive data management strategy and the platforms needed to make replicable and scalable success possible.

Master data is easily one of the most critical assets that a business possesses. With continuous digitization and the advent of the fourth industrial revolution, the value of master data and the importance of master data management are only going to grow. Before we proceed into the importance of master data management, let's understand what master data is. Gartner defines master data as "...the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts." Essentially, master data refers to all the static information that is used to identify the critical elements of a business.

As the year winds down, questions tend to arise about what the big trends of the past year have been and what the year to come may hold. With those questions in mind, we asked eight key influencers in the world of big data and analytics -- Chris Penn, vice president of marketing technology at SHIFT Communications; Jim Kaskade, CEO of Janrain; IT consultant Duane Baker; Bill Jensen, CEO of The Jensen Group; William McKnight, president of McKnight Consulting Group; Ronald van Loon, director of Adversitement; Dr. Manjeet Rege, associate professor of graduate programs in software at the University of St. Thomas, and Bob E. Hayes Ph.D., founder of Business over Broadway -- to take a look back at 2017 and look ahead at what's to come in 2018. Here's what they had to say. Chris Penn: Of all the associated technologies with big data -- machine learning, IoT, et cetera -- the one that really stormed the barricades was blockchain. I expected much more focus on IoT this year, and blockchain stole the show.

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve production planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning. In the $2 billion-plus supply chain planning market, ARC Advisory Group's latest market study shows production planning as being a critical application SCP solution representing over 25 percent of the total market. Production planning applications are used for both planning daily production at a factory to creating weekly or monthly plans to divvy up the production tasks that need to be accomplished across multiple factories. Machine learning is a form of continuous improvement.

Data Harmonization is an approach to Data Quality that is meant to improve the governance and usefulness of data across the enterprise. How does it do that? And how should a company go about implementing a Data Harmonization strategy? To answer these questions, DATAVERSITY spoke with Anil Kaul, co-founder and CEO of Absolutdata. Mr. Kaul was named one of the ten most influential Analytics Leaders in India. He has over two decades of experience in Data Analytics, market research, and management consulting.