With the rise of new digital marketing channels (such as search, display, mobile, and social media) as well as the continuing popularity of traditional marketing channels (including email marketing, direct mail, events, telemarketing, and more), today's marketer has plenty of ways to engage with customers. However, after purchasing a crazy amount of software and paying for expensive consultants to build multi-channel, multi-touch campaigns, one question remains: "where's the return on investment?"

Many marketers want to investigate their cross-channel efforts. However, they often use one tracking system for search, one for display, another for email marketing, yet another for social media, and the list goes on.

One of the common pushbacks with the use of analytics relates to the perceived ubiquity of imperfect data. This sentiment should be familiar to many: "We can't properly analyze and make decisions until we have corrected the quality issues in the data." Unfortunately, analytics must often be based on incomplete or "dirty" data. The first step in dealing with this reality is to understand and accept that information will likely never be perfect.

Sales professionals rely on an array of different criteria to be successful: strong personal skills, a good product, reliable technology, and efficient processes. To help sales reps be more effective, organizations have made huge investments in customer relationship management (CRM) systems and other technologies.

According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%? Despite the efforts of Cloud BI vendors making it easier for users to acquire, explore, and analyze data sources without IT dependency, lack of data literacy and analytic skills still hinder widespread adoption for data-driven decision making.  But the industry is undergoing a fundamental transformation. The mainstream arrival of Artificial Intelligence (AI) brings with it the potential to finally meet the demand for actionable, enterprise-wide, fact-based decision making.