The hardest element of the entire project is probably establishing a data-driven culture in a firm. In our article on datasets Synthesis AI for machine learning strategy, we touched on this subject briefly. The first step in using machine learning for predictive analytics is to address data fragmentation.

For instance, one of the most pressing analytics issues in this context is data fragmentation in a crucial area of expertise. In the hotel industry, the divisions in charge of physical property learn quite personal information about their clients. The credit card information of visitors, the facilities they select, even their home addresses, whether they use room service, and even the drinks and meals they order while they are there are all known to hotels. But the service that allows customers to reserve these apartments can treat them like total strangers.

Various departments, and even various tracking points within a department, silo this data. A CRM may be available to marketers, but those customers are unrelated to web analytics. If you have numerous channels for engagement, acquisition, and retention, converging all data streams into centralized storage isn’t always practicable, but it’s usually doable.

Typically, a data engineer, a professional in charge of developing data infrastructures, is in charge of gathering data. However, if you need help in the early phases, you can hire a software engineer with database expertise.

Your staff may be burdened by the tedious nature of data collecting and feel overburdened by your directives. The likelihood is that individuals will view these chores as yet another bureaucratic whim and let the job drift if they are required to continuously and manually create records. Salesforce, for instance, offers a respectable toolkit for tracking and analyzing salespeople’s activities, but manual data entry and activity logging annoy salespeople.

Robotic process automation systems can be used to resolve this. RPA algorithms are straightforward, rule-based bots that can do monotonous, repetitive activities.