Tips

Tips

• Data silos can live or die by a self-service BI strategy

  Self-service BI is a driving force behind the reshaping or possible demise of data silos as we know them. Vendors address data governance and corporate attitude adjustments.  Continue Reading

• Rules change for self-service BI subscription pricing models

  As self-service BI tools become commonplace, look for subscription pricing models to change according to the cloud, group data usage pricing and how people share their data.  Continue Reading

• 10 dos and don'ts for deploying self-service BI tools

  Self-service BI doesn't just happen. Organizations must ensure data quality and watch how analysts work. Experts offer 10 tips for enabling a self-service culture.  Continue Reading

• 10 features to look for in visualization tools for big data

  Big data is meaningless if it isn't understandable. Experts explain why users need data visualization tools that offer embeddability, actionability and more.  Continue Reading

• 6 big data visualization project ideas and tools

  These data visualization project examples and tools illustrate how enterprises are expanding the use of "data viz" tools to get a better look at big data.  Continue Reading

• 10 tips for implementing visualization for big data projects

  Organizations need to keep users and design at the forefront when launching data visualization efforts, according to experts. Find out why colors and sizing matter.  Continue Reading

• Choosing the best visualization tools for big data analytics

  Data-driven enterprises use visualization tools to tell the stories hidden in big data -- stories which help users turn information into profit. Here's how to choose the right tool.  Continue Reading

• 3 ways to make machine learning in business more effective

  Dun & Bradstreet analytics exec Nipa Basu offers three tips on how to integrate machine learning tools into business processes to help drive better decision-making.  Continue Reading

• Four challenges to successful predictive analytics models

  Analyzing customer interactions to create a predictive analytics model isn't foolproof. Expert David Loshin shares four factors that could stymie your efforts.  Continue Reading

• Building a data science team in today's data-centric climate

  Finding and training data scientists to build a data science team can be challenging. But in a recent webinar, a Gartner analyst offered tips on how to do it.  Continue Reading

• Seven good data visualization practices for visual integrity

  Data visualizations need visual integrity to ensure that the data they present can be interpreted correctly. Follow these design steps to help make visualizations trustworthy.  Continue Reading

• Beat the challenges of predictive analytics in big data systems

  Big data and predictive analytics may seem synonymous, but understanding the constraints of each discipline is the key to extracting business value from projects that combine them.  Continue Reading

• How predictive analytics techniques and processes work

  Predictive analytics is no longer confined to highly skilled data scientists. But other users need to understand what it involves before they start building models.  Continue Reading

• Five steps to build better predictive analytics applications

  Predictive analytics initiatives must be planned and managed effectively to ensure that they don't miss the mark on meeting business needs. Here's an approach to help keep them on track.  Continue Reading

• Hiring vs. training data scientists: The case for each approach

  Hiring data scientists is easier said than done -- so should you try to train current employees in data science skills? That depends on your company's needs, writes one analytics expert.  Continue Reading