4 Tips for Promoting Predictive Analytics in Your Organization
How can you encourage adoption of predictive analytics without executive support?
- By Fern Halper
- September 26, 2017
We recently held a webinar in conjunction with the release of the TDWI Navigator for Predictive Analytics to discuss the market landscape, opportunities, and obstacles with the sponsors of the report. We received many great questions from the audience -- so many, in fact, that we did not get a chance to answer them all. I thought I would answer one here:
"We know that C-level sponsorship is needed to begin creating a data culture. What can be done to promote predictive analytics from the bottom up?"
It is true that promoting a culture of analytics is easier if it starts from the top. Executives can help to evangelize the concept and fund the work. In fact, we see that those leaders who have VP or Chief in their title tend to be able to measure impact more easily than those who do not. However, we've seen organizations successfully deploy predictive analytics from other levels. It can take more time to percolate up to the executive level, but it can be done. Here are some tips:
Tip #1: Start with a proof of concept around a real business problem
This can be a small project using some inexpensive, free, free-trial, or open source predictive software -- and I'm assuming someone has the skills to use it. The important thing is to find a real problem that is meaningful to the business. Ideally, you can articulate the problem in a way the business understands it.
For instance, many organizations will start with one area of interest -- such as a specific marketing problem with known metrics -- and build a proof of concept that can be measured. A proof of concept can help sell predictive analytics because it demonstrates the business value. When the organization sees the value, it is more likely to buy into the overall concept and invest in building out a program.
The idea is to build quick wins and grow from there. That also means using data that is easily accessible.
Tip #2: Publicize the project
Once you have results you think are meaningful, share them. Some people write about what they are doing and put it in the company newsletter to try to get the word out. Others make visuals and post them on bulletin boards around the office. Some teams trying to get an analytics program off the ground will hold lunch sessions or mentoring sessions to get others excited about the technology.
Tip #3: Set up meetings to evangelize the concepts and benefits
We often hear from our audience that they feel like they are in sales when trying to get those higher up in the organization excited about analytics. It's true that you need to become a bit of a salesperson to spread your ideas to others.
Some people make appointments with executives to explain what they've done and get them interested. They will share what competitors in the industry are doing and start to build a relationship. Others are able to get a number of executives in a meeting to discuss their proofs of concept and potential projects.
One thing to remember is the audience. Executives may not be interested in the exact algorithm you used for your analysis. Speak in business terms they will understand and to which they will relate. That is why Tip #1 is so important.
Tip #4: Have a plan
Be careful what you wish for -- it might come true. Once executives start to see the benefits of predictive analytics, they will probably want more of it. Do you have a plan to make it work? This plan should include the right skills, technology, and processes -- and the requisite funding and time frame.
It is also important to learn how to keep expectations in check. We consistently see that a skill shortage is the greatest barrier organizations face in dealing with more advanced technologies such as predictive analytics. Plan for that.
Want to learn more about organizational best practices for deploying advanced analytics? Join us at the next TDWI Leadership Summit in Orlando, December 4-5, 2017.
Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is the director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at firstname.lastname@example.org, on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.