Ensuring Success When Using Predictive Analytics
Think of predictive analytics as a bright bulb powered by your data. The light (insight) from predictive analytics can empower your strategy, streamline your operations, and improve your bottom line. The followings four recommendations can help you ensure success for your predictive analytics initiatives.
Foster a culture of change
Predictive analytics should be adopted across the organization as a whole. The organization should embrace change. Business stakeholders should be ready to incorporate recommendations and adopt findings derived from the predictive analytics projects. The outcomes of a predictive analytics projects are only valuable if the business leaders are willing to act on them.
Create a data-science team
Hire a data-science team whose sole job is to establish and support your predictive analytics solutions. This team of talented professionals— comprising business analysts, data scientists, and information technologists — is better equipped to work on the project full-time. Including a range of professional backgrounds can bring valuable insights to the team from other domains. Selecting team members from different departments in your organization can help ensure a widespread buy-in.
Use visualization tools effectively
Visualization is a powerful way to conveying complex ideas efficiently. Using visualization effectively can help you initially explore and understand the data you’re working with. Visual aids such as charts can also help you evaluate the model’s output or compare the performance of predictive models.
Use predictive analytics tools
Powerful predictive analytics tools are available as software packages in the marketplace. They’re designed to make the whole process a lot easier. Without the use of such tools, building a model from scratch quickly becomes time-intensive. Using a good predictive analytics tool enables you to run multiple scenarios and instantaneously compare the results — all with a few clicks. A tool can quickly automate many of time-consuming steps required to build and evaluate one or more models.