Tips

Tips

Business intelligence data mining

• Better sentiment analysis can bolster customer data analytics

  Customer data analytics are easy to gather in the social media era -- but they can be misleading if based on sentiment analysis culled from automated social media monitoring.  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

• 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

• Tips on building an analytics infrastructure from scratch -- quickly

  Creating an analytics architecture out of thin air may seem like a daunting task, but it can help make businesses more competitive in a hurry.  Continue Reading

• Data discovery tools open up new opportunities for marketers

  Big data is opening up a world of new opportunities to marketers. Data discovery tools can help them take advantage.  Continue Reading

• Self-service analytics helps marketers overcome shortage of data pros

  All industries are having a hard time finding enough skilled data workers, but marketers see self-service analytics tools as a potential solution.  Continue Reading