How to Predict the Success of Your Predictive Analytics Project
With the advancement of artificial intelligence (AI) and machine learning technology, the power of data has grown tremendously over the years. Innovative cloud offerings are enabling companies of all sizes to analyze data trends and implement predictive solutions that are based on sound, mathematical models.
If getting started with predictive analytics were as easy as flipping a switch, everyone would be doing it! But our findings suggest that only 19% of midsize companies are actively planning their analytics initiatives. Even less, just 3% are exploring emerging technologies like artificial intelligence and machine learning.
The Challenges of Predicting the Future
Without the right partnership, predictive analytics projects can be unpredictable in terms of cost, schedule, and impact. Strategically focusing on attainable, valuable use cases from the start is critical to realize maximum ROI. Before embarking on a predictive analytics initiative, companies must answer the following:
- Is there enough data to support this initiative?
- What is the statistical significance of the data breadth, depth, correlation, skew, availability, and consistency?
- What specific actions am I hoping to take after a successful model is implemented?
- How do I measure my investment against true business value?
Despite the potential uncertainty, it is entirely possible to scale your approach to build a cost-effective, impactful model for your business needs. See how Old Second Bank is doing it right.
Demystifying Your Predictive Analytics Project
So, what does it take to predict the success of a predictive analytics project? The most critical work occurs long before a line of code is written. We refer to this step as the “Readiness Assessment.”
Phase 1: Readiness Assessment
Before diving into any predictive analytics project, a documented understanding of critical business goals and desired ROI is instrumental to driving value. Sharp, articulated questions need to be asked about historical data and the potential for future insight. These brainstormed hypotheses will drive data profiling activities, to validate the breadth, depth, availability, quality, and consistency of the data. With a compelling data inventory, strategic plans can start to take shape around a targeted list of hypotheses and candidate algorithms.
Phase 2: Plan of Action
A plan of action is created by prescribing time-boxed iterations based on the complexities documented in the readiness assessment. As a result, measurable assumptions will drive the management of budget and schedule. The iterative nature of the project plan also lends itself to frequent feedback, which is influential to expectation management (which in turn, contributes to the success of the predictive modeling).
Partnering in Success
At this point, midsize organizations may not have the advanced analytics expertise in-house to execute the project. A holistic partner like SWC can help in all facets of the development lifecycle. To learn more about our approach and how you can turn predictive analytics into your competitive advantage, contact us!