by: Chris Knotts, PMP – ASPE Creative Director
As you drive your company forward, harness your data effectively to make sure you’re looking through the windshield, not the rearview mirror.
Tools and software for mining the data available to your organization have now become cheap enough and accessible enough to be available to even smaller-sized companies who wouldn’t have dreamed of them a few years ago. These days, increasing computing power and powerful automation are allowing everyone in the market to conduct amazingly effective predictive analytics. However, you have to know what you’re doing, and there are large pitfalls. In fact, most PADM projects fail, leaving management frustrated and wondering why they seem to be missing the boat on all the potential they’ve heard about regarding new data tools and big data solutions.
In today’s free white paper, data modeling expert Eric King of The Modeling Agency discusses the real-world pitfalls of a PADM initiative, and explains why most PADM initiatives fail, even in the face of these inexpensive and powerful new data tools. He then explains the fundamental factors at play when attempting predictive analytics, and what you need to watch out for to avoid failure in your own company.
In his day-to-day work leveraging data and conducting predictive analytics projects for his clients, Eric has found that with just a bit of education on the forces at play, the chance of success is dramatically increased. Here are a few of the main points covered in this Wednesday’s white paper:
- It might sound incredible, but effective implementation of a PADM project in your organization should be able to realize a 1000% ROI on every dollar invested. Company leaders are often dubious, but an increasing number of case studies and examples are proving it correct.
- Contrary to what you may think, PADM efforts rarely fail due to tactical or technical considerations. While much of the underlying mathematics continues to be quite complex, there’s a lot of software available (including open-source) that enables a general business practitioner without any advanced statistical training to quickly create great predictive models.
- Business impact should be the measure of success with PADM, not technical accuracy. However, one of the most common mistakes is to score a model’s results based on technical accuracy. Therefore, it’s easy to understand why a “successful” model leaves you frustrated when it doesn’t seem to deliver any real business results.
- Many professionals aren’t aware that there are strong industry standard processes available for PADM. Two of the most popular are SEMMA (from SAS) and CRISP-DM (vendor-neutral). Eric explains how to use these standards and the steps they outline to boost success rates in your own PADM projects.
Click to view the Predictive Analytics and Data Mining: Why Most Projects Fail and What Really Works white paper.
Further Reading: If you decide to leverage PADM, or wish to pursue data analysis beyond what your organization is currently doing, you might be interested in our Data Analysis Training Curriculum. We offer real-world training in data analysis, including strategic implementation and predictive analytics.
If you’re just interested in reading more about the state of big data right now and its implications for the business world, I highly recommend you start with this great article from last week’s Visual Studio Magazine, which is a great jumping-off point for some great perspective on the state of big data: it’s paradigm-changing potential and its breakneck pace.
The Evolving Definition of Big Data, by John K. Waters.
This resource series is offered in partnership with Global Knowledge.