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More than 50 Years Later, Machine Learning Is as Relevant as Ever

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Machine learning continues to evolve and bring real-world business benefits

We just completed a fascinating e-book that I encourage you to read. It covers an exciting technology that we can find in almost every aspect of our daily lives. It can make our online experiences quicker and more focused; it can help identify fraud, prevent human trafficking, and even improve medical care. I’m talking about machine learning, or ML. A vast discipline with countless nuances, machine learning has been around for decades and invokes different reactions depending on who you talk to, but one thing we can all agree on: It’s been around for a while and is here to stay. Today it extends the benefits of predictive analytics by carrying out operations on its own that would normally be performed by a human – but at a much faster rate.

Machine Learning Business Technologies

Dun & Bradstreet uses ML in many ways. As a data and analytics company, we understand firsthand the challenges of dealing with seemingly insurmountable mountains of data. Two essential elements of achieving success are the use of trusted data and innovative analytics to turn that data into actionable insights. Machine learning is at the forefront of many new efforts in both areas, and its vast application potential is being realized every day in innovative new ways. Machine learning technologies, tools, and models play key roles in data aggregation and the process of deriving insights from that data.

When we decided to write this e-book, I set up half a dozen interviews with various stakeholders from around the world to get their take on machine learning as a concept, how their work intersects or influences it, and how Dun & Bradstreet uses ML to enable customers to achieve new efficiencies and financial gains because of it. In our organization, data is king, and some of our ML applications focus on allowing organizations to automatically process large quantities of data to deliver answers, recommendations, and predictions. ML systems extend the benefits of predictive analytics by carrying out autonomous operations that would normally be performed by a human – but at a much faster rate.

Turning to Data-Driven Analytics

Today, all business functions are turning to data-driven analytics and insights to manage this increasing uncertainty, while better understanding their organizations’ customer bases and growing their businesses. Analytics is the primary enabler to derive meaning from data. This information is then used to drive business growth. One of my favorite sections of this e-book was provided by Saleem Kahn, Global Leader, Data Innovation, Global Content. He advised us on our section about how Dun & Bradstreet uses ML-based neural nets to look through information on a company and predict its SIC code based on what can be found on their website. In addition, Kahn explained how machine learning can be used in clustering company activities to derive “business signals.” By scanning news and media events from diverse sources and making connections about the content, Dun & Bradstreet may be able to identify precursors to upcoming actions.

This is useful when developing a type of business health index to determine which companies demonstrate strong growth potential and which might fail in the next three months. This can improve sales prospecting and arm enterprises with more information upon which to base decisions. In addition to Kahn’s branch of expertise, this informative e-book explores central topics such as:

  • Delivering Better Insights, Faster
  • Machine Learning and Analytics for Targeted Marketing – Accelerating Time to Value
  • Machine Learning and Analytics for Risk Management
  • Increasing Precision for Better Decisions
  • Machine Learning for Deeper Company Insights – Knowing Your Customers Better

Download the e-book here to learn more about how Dun & Bradstreet is using machine learning to help its customers make data-inspired, analytically powered decisions.

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