Tips

Tips

• 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

• Tableau shows software subscription model is not just for cloud

  Subscription-based software pricing associated with the cloud can also be useful in on-premises deployments, as shown by Tableau's full embrace of the subscription approach.  Continue Reading

• Ten steps to start using predictive analytics algorithms effectively

  A successful predictive analytics program involves more than deploying software and running algorithms to analyze data. This set of steps can help you put a solid analytics foundation in place.  Continue Reading

• Apply data science models effectively, not just for their own sake

  Having data scientists build analytical models doesn't do much for an organization if the models don't help generate business benefits. Here are some ways to make sure they do.  Continue Reading

• Right data mining data sets are a must for proper predictive modeling

  Predictive analytics applications can go off track quickly if data scientists and other analysts don't make good choices on the data sets they're looking to mine and analyze.  Continue Reading

• Three predictive modeling flaws that cripple data science projects

  Data science can be incredibly valuable if done right, but just as damaging if done wrong. Here, a data science expert discusses three common predictive modeling pitfalls.  Continue Reading

• Real-time streaming analytics systems need help from message brokers

  Real-time analytics applications typically involve multiple streams of data that need to be properly organized and coordinated, a job that calls for new message queuing technologies.  Continue Reading

• BI self-service needs new thinking to truly serve business users

  To be truly useful to a broad set of business users, self-service BI tools need to become easier to use. An increased focus on metadata and artificial intelligence can help with that.  Continue Reading

• Five ways to get the most out of A/B testing tools

  Many businesses lack a clear A/B testing strategy when implementing changes to their websites, which means they often miss out on potential opportunities to drive improvements in site performance.  Continue Reading