Worst Practices in Predictive Analytics
Why Some Predictive Analytics Strategies Succeed – And Others Don’t
Ever wonder why so many companies sing the praises of predictive analytics, yet a few will claim that it was a waste of their time and money?
More and more organizations are giving predictive analytics a try, in an effort to improve forward-looking planning and decision-making through more accurate forecasting of future events, behaviors, and outcomes. The potential benefits of predictive analytics are endless. If you are one of those companies that properly defines and implements your strategy, you’ll realize tremendous value. If you don’t, however, you’ll likely fail to achieve the expected advantages.
I’ve been involved in many predictive analytics initiatives, and have seen many mistakes made during the formulation of plans, as well as during the deployment of related solutions. While most of these missteps are minor, and can easily be corrected during the course of the project, some are quite detrimental, and can cause an initiative to completely fall apart.
In a new white paper titled “Worst Practices in Predictive Analytics”, we take a close look at some of the more crucial, project-derailing errors companies often make. Some of these include:
· Failing to focus on a specific business problem or question. Without solid, well-defined goals in mind, projects are likely to be plagued by ongoing changes that delay implementation and drain resources.
· Ignoring crucial steps in the process. By skipping over such important tasks as data preparation and access, companies may find that their information is not quite “analytics-ready”, which can compromise the integrity of results.
· Spending too much time evaluating models. Over-assessment and non-stop testing will lead to constant fine-tuning, which – in some cases – may result in the model never being deployed at all.
· Investing in complex predictive analytics tools that are hard to deploy and difficult to use, or attempting to build the needed solutions in-house. This can be expensive and time-consuming, and will often yield little or no returns.
· Failing to operationalize findings, which can prevent end users from applying the insight gained to make better business decisions.
The good news is, there are steps that can be put into place to prevent such mistakes. For example, choosing the right supporting solution, one that allows organizations to easily and economically build predictive models, manipulate the results, and deploy them to end users in a way that is easy to interpret and use, can play a vital role in the adoption and success of a predictive application. And, knowing what problem you’re trying to solve or what questions you’re trying to answer can help you better target your efforts, so you can realize value much faster.
If your organization is ready to embark on a predictive analytics project, be sure to check out our new white paper, “Worst Practices in Predictive Analytics” to learn more about what pitfalls exist and how to avoid them, as well as the key steps required for building and deploying effective predictive applications.