Avon Products — Is an IT Project worth $5 billion?

Eighteen month ago Avon Products stopped its global SAP implementation, a.k.a., the “Service Model Transformation” or SMT project.  In its 8-K regulatory filing, Avon stated that it planned to write off the $125 million investment in SMT.  The Canadian SMT pilot failed to produce the results Avon was looking for, and the CEO decided to nix the rest of the global rollout.  On the day of the 8-K filing, Avon shares closed at $16.82. Today the stock sells for $6.75, down 60% from the filing date.  The Dow is up 12% in the same period.  If Avon tracked the rise in the Dow, it’s market cap would be more than $8 billion, versus today’s actual market cap of $3 billion.

Is an IT Project worth $5 billion?  According to Information Week, “the technology worked, but it was so hard to use that Avon salespeople — many of them part timers who network among friends and hold in-home parties — left the company in droves.”  This tells us that the Avon sales force didn’t want to use a system that made their lives more difficult!  We hear the same complaint from doctors about their Electronic Medical Records systems.

A company’s worth can pivot considerably based on whether it invests in IT wisely. Perhaps Avon should have spent its IT dollars toward giving its salespeople the tools to make more money, more easily, and in less time!


Computerizing Health Care Is Making Us Poorer, Not Healthier

Healthcare.gov. Electronic Medical Records.  HIPAA.  Meaningful Use.  Telemedicine.  Insurance Coverage.  COBRA.  Privacy.  Medical Errors.  Productivity Requirements.  No-Recourse Billing.  The Cloud.  For the remainder of this decade, the health care industry will be overwhelmed by managing its information, and less able to help you manage your health.  The long-term outcome may be good, but the near term is sure to be rocky.  The launch debacle at Healthcare.gov is the first act in what will likely be a sordid tale.

The private practitioner cedes his Lexus to the repo man and gives up his country club membership because he can’t afford to computerize his practice.  Corporate and university health systems are gobbling up independents and group practices, because they are the only ones that can afford the massive fixed cost investment of health care IT.

Like other sectors, health care is starving for information in a swelling sea of data.  Medical costs are skyrocketing, and this is partly due to the exponential explosion of health care data, enabled by the cheap storage, ubiquitous bandwidth, and hungry technology vendors.

Go to a doctor today and then examine your bill.  Do you understand it?  Is it correct?  What recourse do you have if it’s not?  Did you receive a bill from the doctor’s hospital in addition to the doctor’s bill, even if you didn’t go the hospital?  Thought your insurance coverage would cover it, right?  What can you do?

The Federal Government entered the fray via Meaningful Use, which pays doctors for computerizing, but the legions of IT consultants are the real winners.  They’ve thrown their Re-Engineering, Six Sigma and Sarbanes-Oxley brochures into the trash to make room for their health care IT services.  The big consulting firms lobby lawmakers to codify complexity so they can sell our heath care providers a lifeline out of it.

Until heady ambitions are distilled down into doable projects, we will continue to wait for results.  We must curtail the data deluge and pare the consulting legions or our health care industry won’t get healthier.

Jon Bellman






A Brother From Another Mother — Is your EMR or EHR Project in the Black Hole?

To my healthcare industry friends and colleagues, I hope you will watch this video.  How far along is your Electronic Medical Record or Electronic Health Record project?

IBM Deal Mostly Wasted Money State Says

How does Pennsylvania’s failed Unemployment Compensation system relate to your EMR or EHR project?  Large projects often fail! EMR and EHR projects are waiting in their acorns to be the ERP disasters of the late ’90s and early 00’s.  The bottom line: PA made a decision in 2006 to spend $107M.  In 2013, after spending $153M, PA booted the upgrades into the trash and is now trying to reboot their 2006 system!

Pennsylvania hired Carnegie-Mellon’s SEI (think Level 5 CMMI) to evaluate IBM’s project with the state before pulling the plug.  My guess is the state already decided to pull the plug, but needed to bring a “big-gun” consultant to justify terminating IBM.

Now back to EMR & EHR.  Why will your massive healthcare IT project fail?  Long-term projects with changing requirements and teams usually do.  Who pays?  Not the software company that wooed you and payed the lobby dollars that mandated the need for these systems.  Certainly not the consulting firms, who get paid by the hour – their goal is always more hours!  The real question is that when you finally go live with a portion of your EMR or EHR (after you have written off the rest) will it keep your patients healthier and safer and will it enable your healthcare organization to earn a profit?

IBM, have you given back the money?  IBM, please tell us what percentage of the billings were IBM staffers versus subcontractors?



Run Faster — Jump the Big Data and Analytics Hurdles

I ran a half-marathon on Sunday.  Here were my splits, according to my trusty Ironman watch:

Mile 1:                        10:01

Mile 2:                        09:14

Mile 3:                        08:57  * (Forgot to hit the lap button, so I averaged Mile 3 and Mile 4)

Mile 4:                        08:57  *

Mile 5:                        08:48

Mile 6:                        09:19

Mile 7:                        09:08

Mile 8:                        08:44

Mile 9:                        09:02

Mile 10:                      09:07

Mile 11:                       09:10

Mile 12:                      08:32

Mile 13:                      08:30   ** (Forgot to hit lap button, so estimating the Last Tenth)

Last Tenth:                00:51   **

Total Time:     1:58:20 (1 hour, 58 minutes, and 20 seconds)

Pace:                09:02 per mile


I did some checking and found my times for the past thirteen half-marathons I’ve done, dating back to:

Sunday’s:        1:58:20

2011:                 1:56:10

2010:                2:02:11

2009:               1:55:16

2009:               2:01:29

2008:               1:55:19

2008:               1:53:09

2007:               1:46:31

2007:               1:49:20

2006:               1:52:07

2004:               1:58:10

2004:               2:02:30

2004:               2:09:42


Avg. Time:      1:56:56

St. Dev.:          0:06:10

Pace:                08:56 per mile


Throwing out my best and worst times, the numbers change to:

Avg. Time:      1:56:44

St. Dev.:          0:04:17

Pace:                08:55 per mile


Since my worst time was the least recent and my best times were in the middle of the study period, I conclude that I haven’t gotten slower with age or faster with experience.  

Relating this to the world of Analytics, or slicing our new lifestyles of Bigger and Big Data into smaller and smaller chunks to recognize patterns and improve behavior, one might conclude that I am an outstandingly consistent mediocre half-marathoner.

How can I improve my race times?  BY RUNNING FASTER.  Will analyzing the above help me run faster?  Suppose I also included the following additional data in my analysis:

  • Course difficulty (These thirteen marathons were run on six different courses)
  • Race temperature, wind speed, humidity, etc.
  • My race weight
  • The meal I ate the night before
  • Whether I ran alone or with a buddy

There are other independent variables that I could list that undoubtedly impacted my race day performance.  That said, what would be the point of recognizing patterns in yesterday?  Sure, patterns would be intriguing to contemplate, but if I want to run faster tomorrow I should:

  • Run easier courses
  • Lose ten pounds
  • Find a fast running buddy to train with
  • Don’t waste my time analyzing yesterday

Analytics and Big Data are often not the answer to better decisions or better performance.  At best, these en-vogue tools may find diamonds in the rough or identify difficult-to-detect signals.  In reality, nearly all data is noise and noise isn't worth listening to. 

If you want your customers to buy more from you, don't squander your margins building robust analytical tools.  Ask your customers how you can do better and then just do it.

See you on the streets.