1.Critical system failure

Your ERP system crashes in the middle of the night and you are not aware of it until the next morning. Restart and recovery will take several hours, causing a significant loss of revenue.

IT personnel determine that the system crash was due to the failure of an of an aging server with insufficient capacity to handle an ever-increasing amount of transactional data. The CIO refuses to allocate more funds to the outdated and poor performing ERP system infrastructure and charges you with finding a cost-effective solution that will minimize the chances of future system failures.

Stability is important. Hotels, casinos, and resorts have guests they need to serve right now, today. Hospitality software solutions in place help them do that by taking care of the basics: reservations, check-ins, folios and check-outs, along with a staggering variety of in-house services and their associated charges. Essential software like hotel PMS is pretty “sticky” that way; it’s easier to stick to what you’ve got than it is to change. Why fix what isn’t broken?

Here’s the thing. Five years from now – perhaps sooner – the way guests engage with hospitality providers will likely look very different. Innovative solutions and platforms will be necessary to scale to that new paradigm, whatever it may be. So how do hospitality organizations know when it’s time to start planning for that? How do decision-makers know when it’s time to get unstuck? Here are 3 considerations to help answer that question.

As new hospitality technology emerges and as organizations increasingly invest in it, the ways people do their jobs change in response. Most of the time, this means greater efficiency, and a way for teams to concentrate on key tasks while the technology takes care of the necessary minutiae. Some of the time though, the rise of certain technologies can cause some concerns that with certain functionality in place, the human factor will get lost.

Hospitality AI functionality in hotel revenue management and in hotel pricing tools is a case in point. The conversation around automation via algorithms has been too easily positioned with a “versus” between machine learning solutions and professional expertise. Yet, the features of the technology and years of knowledge and experience held by revenue and strategy teams isn’t adversarial. The relationship between them is more about achieving greater focus and clarity. How do science-based algorithms in revenue management and pricing solutions help your organization do that? Let’s look at some key points.

Traditionally, almost all business intelligence and analytics solutions were designed as general-purpose platforms or desktop tools suitable for any industry, department, or use case. That does mean, of course, that the burden of developing data models, data pipelines, dashboards, and reports falls to the customer. They effectively start with a 'blank sheet of paper,' which, in theory, can be developed to fit the organization's exact needs. However, the reality is that the organization may not have either the skills, resources, or time to develop an analytics solution that quickly meets business requirements.

The answer to avoiding this overhead of entirely homegrown analytics is to use a modern analytic application that can deliver ready-to-use analytics, right "out-of-the-box." Deployment and adoption of these analytic applications are accelerated because of built-in content developed by industry domain experts that understand the industries, roles, data, business applications, and specific analytics required by typical business user personas. A significant amount of development time can be saved because this domain-expertise-driven content can replace typical internal development processes.

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.