Predictive analytics, the mistakes that hurt the most

Disappointing results, barriers to implementation or problems that arise unexpectedly during the construction of a predictive analytics solution are some of the disappointments that many companies have to face on their journey towards experiencing a new way of understanding business analytics. Every company worth its salt tries to use large volumes of data for predictive analysis. The benefits and business justification are the first arguments that those who opt for this type of solutions have in mind. However, not everything is technology: predictive analytics is not just technology. Knowing some common obstacles that organizations face when applying predictive analytics is the quickest way to detect and overcome them.



Failures in predictive analytics that could have been avoided The technical aspect is not the only thing that counts in predictive analytics , as some of the most common errors demonstrate: 1. Lack of executive leadership: As with any new tool that is planned to be implemented, there needs to be an executive sponsor who determines the company's priorities, establishes the direction to follow and justifies the investment. No matter what the new system is capable of, if no one changes their Chinese Student Phone Number List workflow to use it correctly, it is a failure and the leader plays an essential role in this process. His role involves: Lead the organization through the change entailed by the implementation of the new predictive analytics system . Take the opportunity to modify all the aspects that can be improved. Know how to properly incentivize and motivate users to use the new tool. 2. Neglecting data: most organizations have a significant data quality problem that they either are unaware of or lack a business justification to serve as an excuse to address it. Any predictive analytics application that you wish to implement requires data of sufficient quality to be loaded and collated.





Introducing a business application that can use data in a powerful new way can often serve as justification for that data cleansing process. This data cleaning must be included in the planning, as it will consume time and resources, surely to a greater extent than can be anticipated a priori. In any case, it is interesting to incorporate data cleaning as part of the application in the chosen predictive analysis tool, since data quality should not be addressed as a one-time event but as a continuous process over time. 3. Do not limit the scope of the predictive analytics initiative : Many companies have unrealistic expectations when implementing predictive analytics in their organization. The result of a wrong approach is an equivalent project scope. To avoid this, you must seek advice from experts and keep in mind that: Most organizations can handle a small system that grows in value over time, but very few can absorb massive change immediately. It is preferable to opt for an iterative approach, with deadlines that assign each phase a goal associated with specific business value.