Predictive Analytics – What you MUST know!

The customer stories published about implementing Predictive Analytics into your own “happily ever after”?  I’m going to share a few things you can do to prepare for this next chapter of your Predictive Analytics narrative.

PEOPLE: Identify your Predictive Analytics cast of characters 

ProducerWho is funding this? How are they wooed?
ProtagonistsWho is leading this charge? Who are the champions?
AntagonistsWho may be opposed to or afraid of this development? How can you get them on your side?
Dynamic CharactersWho will be impacted by this development? How can you gain their support and make them feel comfortable and excited about these changes?
Static CharactersWho are the team members that you’ll need help from, but won’t see a big day-to-day impact from this change? IT? DBA’s? How can you entice them to be on your team?
Fairy GodmotherThe biggest hurdle in “Going Predictive” is what you don’t know; trustworthy guidance from an experienced Predictive expert is crucial to your success.

PROCESS: Create your Storyboard (with the Producer in mind)
Define the story

  1. What line of business is asking for Predictive Analytics capabilities?
  2. What kind of outcomes are they looking to impact? (Customer churn? Supply chain? Marketing? Employee Productivity? Etc.)
  3. What is the cost of ____? (a new customer (customer churn model), diminishing system down time, a fraudulent insurance claim, a patient re-admittance, etc.) This will help with your ROI calculations later.

Interact with your cast

  1. Educate your cast on how adding Predictive Analytics will improve their life and day-to-day activities. Be an evangelist for “Data Backed Decision Making” and spread the culture and excitement!
  2. Brainstorming Session: Use the Lodestar Post-It Note Exercise to gather all ideas and input from your cast. Elicit areas worthy of concern as well. Consolidate and prioritize ideas with the group, and keep a list of flagged issues to address (data access, training, implementation, deployment, budget, etc.).
  3. Keep regular meetings with your cast and build a strong team environment.

Create a plan

  1. Account for lengthy data prep (80% of building a model is obtaining and cleansing data), especially if your analyst is new to Data Mining.
  2. Consider time and cost for coaching, training, and staffing needs.
  3. Draft a diagram or flow chart for how you think your process will work, push it to your cast to refine. Look at where data is coming from and going to – label data, processes, and checkpoints with cast members associated with each. (Bubbles as data, Squares as Processes, and Triangles as Checkpoints/Approvals for example)
  4. Build in REGULAR Testing & Monitoring:
    • BUSINESS Success Metrics (is the model achieving the correct result?)
    • STATISTICAL Success Metrics (is the model accurate? Is it useful/significant?),
    • SUPPLEMENTARY Success Metrics (note other changes based on employing the predictive model i.e. system downtime is waning, employee satisfaction is up, overtime is down, etc.)

Selling the script

  1. Use the Nucleus Research ROI Tool to help build your case and craft your presentation from the CFO’s point of view! How does this make or save the company money? Overlay your diagram or flowchart with areas that will cost something as a starting point, compare this to the “cost to ____” (from “Define the Story” Part C) and determine how many you need in order to see a return on the investment. You can also calculate (or conservatively estimate) how long that would take, often times a matter of weeks or months.
  2. Now it’s time to meet with your Producer (probably the CFO), and gain funding! Bring your Fairy Godmother and Protagonists with you!

TOOLS: Setting the stage with the right props

Data is your biggest prop – It's what creates and fuels your Predictive Model

  1. Quality: Data which are “noisy” (errors and missing data) cannot be compensated for by any Data Mining Technique in the book. A good set of predictors can fail because of an error which masks its effect.
  2. Quantity & Suitability: Be sure you have enough of the right data, it should be large enough to represent the target population and cover all possible outcomes.

Data Mining Tool – You'll need a software solution with these key features

  1. Will work with your data and existing data housing, BI, and other systems.
  2. Is user friendly (unless you have a Statistician/Coder on staff that’s familiar with a more complex tool)
  3. Is easy to bring in-house (to cut down on consulting costs)

CRISP-DM – “Cross Industry Standard Process for Data Mining”

  1. Developed as an internationally recognized definitive process for mining data.
  2. Your Protagonist Analysts should be well versed
  3. Should be a main theme in your storyboard

As a part-time Predictive Fairy Godmother, I think this checklist uncovers a mindset for a successful Predictive Analytics adventure. Feel free to click your heels…err…call or e-mail me to help you get started!

Using Analytics Effectively – 5 Tips

With Analytics becoming more mainstream, we still see many companies shying away from it; perhaps because they have been burned by previous attempts. Here are five tips for any organization looking to turn the corner and start being successful in using their data to make decisions:

  1. Make data and analysis available to employees: Analytics veterans will tell you there is big value in allowing employees to see and use data. The power transfer that occurs when this is permitted inspires insights in employees’ own work, which helps analytics programs grow and evolve; thereby providing even more value and improvement. Be sure to always ask for users’ assistance and to put some of their ideas to work. These insights will help guide the progression of the project, all the while strengthening employees’ commitment to the data analytics strategy.
  2. Return on Investment should be measured early & often: Recording wins from data analytics projects is important; however, it is a bottom line requirement and may not be enough to continue to win budgetary support. Times are tight for organizations of all sizes and in all markets, so when it comes time to verify that dollars were well spent on your data analytics efforts, be sure to include: clear measurements on cost benefit (in dollars), hours of employee time saved (in hours and dollars – and include value of re-assignments), as well as the improved outcomes that were seen (that may not have a direct monetary value i.e. improved employee morale, improved customer satisfaction, etc.).
  3. Hire an expert: Some organizations opt to skip consulting assistance in lieu of pre-recorded training modules and learn to do it themselves for the sake of saving a few bucks. Unfortunately, this mindset causes sluggish returns on the Analytics investment, and we have seen analytics programs get cut for this very reason. Go ahead and get yourself that consultant and ask for a start-up package with training, or better yet, if you can find an expert – HIRE THEM, and then learn from them! Their wisdom and experience can speed your first few projects into quick wins that will help boost data-use culture within your department and maybe across the organization. These wins will also help you continue to get budget allocations, and when word gets around about your success, you may be surprised to see who else from your company comes knocking at your door.
  4. Get to the point: Top executives’ support is vital to keep analytics projects afloat, and the information and insights analysts develop are vital to top executives’ decision making. “Clear, concise and, most of all, brief” should be your mantra if you are presenting at the executive level. Speak as if you are an executive and be sure you are presenting the product of your analysis, and not the analysis itself. In order to get top leaders to support your data analytics program, they must understand the results of your analysis and how they align with achieving the organization’s overall mission(s). This is actually the largest piece we find lacking in the talent pool for analysts – people with statistical and/or data mining experience AND Business Sense.
  5. Seek to envelop the organization in data-use culture: Eventually, analytics will be standard operating procedure in all areas of your organization. However, this will take time and numerous successful analytics projects to win over management at all levels. When this happens, there will be a need for on-the-job analytics training for employees to meet the new demand from management (again – hire an expert!). The return on investment for analytics projects (particularly predictive analytics) is so high that the cost for training and/or hiring an expert ends up paying for itself through speedier results and measurable ROI.

These are just a few highlights from data analytics veterans who have seen just about everything. If you have been reluctant to get started, or feel as if you have been burned before but are still feeling interested – we are happy to help!