Tips

Tips

• 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 data scientists and other highly skilled analysts. 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

• Compelling data story starts with effective visualization of data

  Data visualizations can transform analytics data into actionable business information. But remember to keep things easy to understand and remember your audience, an expert cautions.  Continue Reading

• Telling data stories calls for common syntax on analytics info

  Data storytelling is just the latest phase in the long history of passing on knowledge to people, expert David A. Teich explains. It's just the tools that have changed.  Continue Reading

• IBM Watson computer strives for analytics stardom

  After achieving Jeopardy! success in 2011, IBM is trying to boost its Watson cognitive computing system back into the spotlight through acquisitions and by building out its Watson-based commercial products.  Continue Reading