Shadow Banning, Machine Learning, and MDM: What's the Connection?

Shadow Banning hit the news last week, with Twitter caught in the fray. The social giant’s search results were called into question; however, Twitter Head of Product Kayvon Beykpour clarified that Shadow Banning was not taking place, pointing to machine learning (ML) instead.

Shadow Banning is not a new concept – it’s been around since the early days of web forums. Instead of completely removing accounts, the posts of disruptive users can be demoted or hidden. This improves the health of the online community and discourages offensive content.

In the case of Twitter, the issues arose from a bug in the site’s latest algorithm, which incorporates behavioral signals and machine learning. Using Artificial Intelligence (AI), Twitter’s algorithm looks at behavior and interactions such as how often an account is muted, blocked, reported, retweeted, liked, etc. ML classifies such actions as either positive or negative experiences and makes adjustments accordingly.

This type of technology puts a spotlight on the role of ML and data management, and the intriguing shift from data control to engagement.  The explosion of data along with emerging technology including AI, ML, and IoT are the driving forces behind this digital evolution.

Connecting Machine Learning and MDM

Master Data Management (MDM) is well-known for its ability to break down silos and create a golden record of trusted data. When combined with machine learning, MDM gets a boost.

For example, according to Andrew White of Gartner, “Deep learning might help us discover where our master data is kept. Finding where our master data is, embedded copies all over the place inside and between business systems in a complex landscape of on prem and cloud apps is a hard task.  Deep learning might be able to ‘spot’ where the most frequently referenced data reside… But finding where our master data exists is not equal to MDM – it is just part of the overall set of tasks needed to sustain MDM.”

When facing a Big Data environment, ML can aid in master data discovery and help make crucial processes automated and repeatable at scale. This may improve an organization’s productivity, empower data stewards, and deliver a new level of actionable insight for the business.

Consider this:

  • ML can enable organizations to uncover and establish patterns, make intelligent associations, and suggest new rules
  • Combined with MDM, machine learning helps automate tasks that are overwhelming or even impossible for humans to manage
  • ML can help make contextual recommendations for business users to boost MDM processes and governance

“Future Proofing” MDM

In technology platforms, “future proofing” means that core data management hubs can provide the data to harness the advances in other technologies, such AI and ML, along with the ability to swap them out when newer, better tech emerges. Such agility enables companies to focus on business model innovation by knowing that their platforms can always keep pace.

To that point, EnterWorks recently introduced a significant advance in the data management field with the release of our Enable Agile Data Fabric™ (ADF). Enable ADF extends the foundational role of Multi-domain MDM in driving business results across industries, geographies, and company types.

The solution weaves together structured and unstructured data, internal and external applications, and virtual and physical data stores for digital empowerment of businesses of all types. The graph-like data structure simplifies inherent complexities in relationships across multiple data domains. Furthermore, by granting secure access to our microservices layer arising from our N-Tier architecture, we streamline and accelerate our delivery of new data and cloud services.

Simply stated, architecture is key to innovation.  A data architecture that is agile across multiple data domains can multiply profits by mastering data complexity. Therefore, organizations must consider the agility of their MDM solution, along with the ability to “future proof” as emerging technology takes hold.

How can the EnterWorks Enable Agile Data Fabric™ advance your organization? Read more here.

Want to learn more about the ROI of MDM?  Download our latest White Paper here.

Kerry Young

Kerry Young

Kerry Young joined EnterWorks in 2006 when Ennovative, Inc., the multi-channel publishing software company he co-founded, was acquired by EnterWorks. He directs EnterWorks’ operations and leads EnterWorks’ professional services and consulting organization, ensuring effective customer implementations and ongoing success. Mr. Young brings more than 25 years of technology and business management experience to EnterWorks, having served as CTO for a subsidiary of the Dow Chemical Company, and earlier as VP, Information Technology for Marshall Industries, a $1.7 billion industrial electronics distributor. He previously managed information systems for a subsidiary of McDonnell Douglas Corporation. Mr. Young holds a B.S. degree in Computer Science from Cal Poly, San Luis Obispo and an M.B.A. from California State University Fullerton.

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