Four Ways to Illustrate the Value of Predictive Analytics
My new (and first!) TDWI Best Practices Report was published a few weeks ago. It is called Predictive Analytics for Business Advantage. In it, I use the results from an online survey together with some qualitative interviews to discuss the state of predictive analytics, where it is going, and some best practices to get there. You can find the report here. The Webinar on the topic can be found here.
There were many great questions during the Webinar and I’m sorry I didn’t get to answer them all. Interestingly, many of the questions were not about the technology; rather they were about how to convince the organization (and the senior executives) about the value in predictive analytics. This jives with what I saw in my research. For instance,”lack of understanding of predictive analytics” was cited as a key challenge for the discipline. Additionally, when we asked the question, “Where would you like to see improvements in your predictive analytics deployment?”, 70% of all respondents answered “education.” It’s not just about education regarding the technology. As one respondent said, “There is a lack of understanding of the business potential” for predictive analytics, as well.
Some of the questions from the audience during the Webinar echoed this sentiment. For instance, people asked, “How do I convince senior execs to utilize predictive analytics?” and “What’s the simple way to drive predictive analytics to senior executives?” and “How do we get key leaders to sponsor predictive analytics?”
There is really no silver bullet, but here are some ways to get started:
- Cite research: One way is to point to studies that have been done that quantify the value. For instance, in the Best Practices Report, 45% of the respondents who were currently using predictive analytics actually measured top- or bottom-line impact or both (see Figure 7 in the report). That’s pretty impressive. There are other studies out there as well. For instance, academic studies (i.e., Brynjolffson et al., 2011) point to the relationship between using data to make decisions and improved corporate performance. Industry studies by companies such as IBM suggest the same. Vendors also publish case studies, typically by industry, that highlight the value from certain technologies. These can all be useful fodder.
- Do a proof of concept: However, these can’t really stand alone. Many of the end users I spoke to regarding predictive analytics all pointed to doing some sort of proof of concept or proof of value project. These are generally small-scale projects with high business impact. The key is that there is a way to evaluate the impact of the project so you can show measurable results to your organization. As one respondent put it, “Limit what you do but make sure it has an impact.” Additionally, think through those metrics as you’re planning the proof of concept. Additionally, someone in the organization is also going to have to become the communicator/evangelist to get people in the organization excited rather than fearful of the technology. One person told me that he made appointments with executives to talk to them about predictive analytics and show them what it could do.
- BI foundation: Typically, organizations that are doing predictive analytics have some sort of solid BI infrastructure in place. They can build on that. For instance, one end user told me about how he built out trust and relationships by first establishing a solid BI foundation and making people comfortable with that and then introducing predictive analytics. Additionally, success breeds success. I’ve seen this countless times with various “new” technologies. Once one part of the organization sees something that works, they want it too. It grows from there.
- Grow it by acting on it: As one survey respondent put it, “Analytics is not a magic pill if the business process is not set up.” That means in order to grow and sustain an analytics effort, you need to be able to act on the analytics. Analytics in a vacuum doesn’t get you anywhere. So, another way to show value is to make it part of a business process. That means getting a number of people in the organization involved too.
The bottom line is that it is a rare company that can introduce predictive analytics, and behold! It succeeds quickly out of the gate. Are there examples? Sure. Is it the norm? Not really. Is predictive analytics still worth doing? Absolutely!
Do you have any suggestions about how to get executives and other members of your organization to value predictive analytics? Please let me know.
Posted by Fern Halper, Ph.D. on January 20, 2014