A Dan Pink Speaking Experience

A couple of weeks ago I was staring at my computer screen and in comes an Instant Message asking if I knew Dan Pink was speaking in Charlotte? The IM was from Jill, a work friend for over 10 years. I had no idea about the event, but I was excited. She sent me the link to the UNCC NEXT Speaker Series and I promptly bought a $40 ticket.

The day of the event arrived, but I wasn’t sure where to go. The Blumenthal has several stages and the one I was looking for was the Booth Playhouse. Luckily, there was an event before hand for networking, so I figured I could follow the crowd. It was easy. There were several people standing in the hall welcoming Dan Pink fans and pointing to will call for picking up tickets. I was in extrovert mode and introduced myself to several other attendees, but the response I got was uncomfortable friendliness, forced smiles and all. After a few of these interactions, I realized the people I was trying to chat up were college professors. Maybe they aren’t used to networking in a real business world? Undaunted, I bought a beer and spotted someone who wasn’t part of the school clique. I introduced myself to Darren and we discussed Pink’s books.

Although we are standing in the lobby of a small theatre, it sort of feels like a post modern fashion store. There are doors at the ends, but the entire area is visible through clear windows. I wasn’t at the mall, but I could have sworn I saw some t-shirts on sale for $250. Thankfully, Jill arrived and we discussed our day of work.

We decided to head in early to get a good seat. I heard it was interactive so I wanted to be near the front. However, when we walked in I was very stunned to see the first eight rows or so were reserved for VIPs. It isn’t a big venue so this preferential seating situation was a bit much. For $40 I should be able to sit close.

I met another friend as we were deciding where to sit. My inner voice was screaming “yea!” that this friend showed up. There’s always a rewarding feeling when someone else tries out music, a book, or a restaurant you suggested and this was the same appreciation.

The lights dimmed and the last few seats were taken. I noticed Peter Gorman, the Superintendent of the Charlotte-Mechklenberg schools, sitting across from us – not a VIP either. I’m not sure who kicked it off. It was either the Chanceller or the President of UNCC. He was kind of funny. The Dean of the Business School then introduced Dan to the audience.

I’ve viewed most of the videos for Drive and was nervous that Dan would stick to the script. He mostly followed the themes but he certainly was able to ad lib. He did his homework and talked about the local area some. He quizzed the audience about motivation and interacted with a few different guests. Throughout the session some slides were used to highlight the research that reinforced his points. Time flew by and it felt like it was short, but he spoke for about 70 min.

Overall, I enjoyed my first Dan Pink speaker series. I went with friends and made some connections. Next time I’m going to penetrate the inner circle though 🙂

Working Thoughts 2/10/09
Sustaining Large Economic Growth is Key for the US

The Train with No Known Destination

Last week news broke of Eric Schmidt leaving the CEO post at Google. He’s replaced by Larry Page. Speculation is that Schmidt no longer felt he was in control of the company. The triumvirate of Sergey Brin, Larry Page, and Eric Schmidt had become a duopoly of Brin and Page, the founders. The genesis of their relationship is rooted in the need for someone who knew how to run a big company – Schmidt. Around 2000 when Google was  preparing to go public it was growing at an immense rate. The size of the company had surpassed the experience level of 20 somethings. The founders would concentrate on a start up atmosphere of constant disruption. Disruption is where money is made.

At some point, every successful company grows out of it’s novelty state. The disruption becomes the norm. Competitors look for weakness and stagnant ideas. Being a perpetual start up is the dream of people like Brin and Page. But how do you do it?

Intelligent continual employee turnover.

The enterprise must become a train with no known destination, just stops letting people get on and get off. When the enterprise becomes “the destination” then protection ensues. People can be very good at their jobs, but if they are doing the same thing for more than three years then you have to wonder why? Why isn’t the job evolving? Why isn’t it automated? Why is it needed?

Many large companies, including Google, want to be smaller. Being nimble is key. But wanting a start up mentality and structurally building it in to the culture is not the same. There are a lot of tough conversations to be had. For instance, Netflix has a running practice of “adequate performance gets a generous severance package” and they apply a keeper test which is pretty simple: which people would you fight to keep, at any cost, if they told you they were leaving in two months? This is supplemented by honest conversations about the employee’s commitment and ability to deliver. No surprises.

The NY Times in their weekly section called The Corner Office interviewed Jeremy Allaire, chairman and chief executive of Brightcove. He talked about his conversations with his work force. He said he asks them “What are you trying to do? Where are you trying to head?” This survey reinforces the need to be ever improving.

When the culture of the company is to evolve the job, to morph it, to leave it, or to destroy it (automate) then, as an employee, you know when it’s time for a change. Just ask Google.

The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices – A Book Review

Quick Take: The world of Data Management is becoming exposed and books like this one are a great starter guide for practitioners to understand what goes into initiating a Data Governance program. There’s no secret sauce or magic and that’s mostly the point.

Detail Review: There was once a time when people didn’t have enough information.  Now there is too much of it.  And in a few years we’ll supposedly have smart appliances and talking toasters. Well, maybe not talking, but data is becoming more ubiquitous.

Over the last decade you’ve probably been on vacation and asked “is there a good pizza place around here?” and a friend responded “according to Google, there are 8 pizza places within 5 miles of here.” You picked up the phone and called one but the number was no longer in service. Being persistent, you tried another, ordered a large pepperoni and got it 30 minutes later. Unfortunately, crackers with ketchup would have tasted better.

Companies like Google are working on this, but these were two examples of poor data quality. And data quality is a data management issue. In the case above, the phone number being out of service could be because the pizza place is closed or it could be incorrect phone digits. Not sure. The taste, or lack of, is shows a failure in relevancy – “is there a good pizza place around here?” is a two part question.

The author, Sunil Soares,
is an IBM Director in the Software Group. He has worked with over 100 clients across multiple industries and has years of consultant experience. I don’t know him, but I’ve worked with a coworker of his, Doris Saad. She did a wonderful job with extending a data governance model with an IBM flavor.

Back to the book. The aesthetics are decent. It’s a paperback consisting of 125 pages of content and another 28 of appendix material. The font is average size and the construction of the chapters is typical of a business book – bullets and concise paragraphs. The front cover is a washed out blue with the illustration of the Unified Process on it. 

The introduction is by another IBM lead, Steven Adler. He provides an example of a time he wanted to apply for a refinance. He completed the forms but there was an error with the type of loan. There was no way to deal with the mistake except to start over, which he did. This small classification issue resulted in much more rework – missing forms, open quotes, and back and forth communication. These are the type of inefficiencies a good data management programs help with. I like my pizza example better 🙂

Being a governance person, I especially like how early in the book he frames up the role of governance. Many people believe it’s about policing decisions i.e. exceptions. But it’s about getting stakeholders to make decisions. Soares states:

“Data Governance is the discipline of treating data as an enterprise asset. It involves the exercise of decision rights to optimize, secure, and leverage data as an enterprise asset. It involves the orchestration of people, process, technology, and policy within an organization, to derive the optimal value from enterprise data. Data Governance plays a pivotal role in aligning the disparate, stovepiped, and often conflicting policies that cause data anomalies in the first place.”

I also liked this line”

“Treating data as a strategic enterprise asset implies that organizations need to build inventories of their existing data, just as they would physical assets.”

The reason is because it’s hard to manage what you can’t count. If you don’t have an inventory then how will know if things have changed. It seems so obvious, but it isn’t. Making a concept like data tangible is vital to getting everyone on board.

He validates this point by offering some great questions during the Govern Analytics chapter.

  • How many users do we have for our data, by business area?
  • How many reports do we create, by business area?
  • Do the users derive value from these reports?
  • How many report executions do we have per month?
  • How long does it take to produce a new report?
  • What is the cost of producing a new report?
  • Can we train the users to produce their own reports?”
    • Would a BI Competency Center help?

Additional questions I add are:

  • Are new data generated by analysts?
  • Is the new data reincorporated back into the operational processes?
  • Are the reports sensitive? How is access to the data handled?

And page 15 offers this realistic picture of why data governance often fails:

“Most organizations with stalled Data Governance programs identify these symptoms:

  • “The business does not see any value in Data Governance.”
  • “The business thinks that IT is responsible for data.”
  • “The business is focused on near-term objectives, and Data Governance is considered a long-term program.”
  • “The CIO cut the funding for our Data Governance department.”
  • “The business reassigned the data stewards to other duties.”

Once you’ve gotten your bosses on board with doing Data Governance, it’s time to identify an approach. Soares has a IBM Maturity Model (below). It’s not a bad one. I’ve designed a few different governance related maturity models and I like this one because it eschews the levels and goes with relationships.

  1. Data Risk Management and Compliance is a methodology by which risks are identified, qualified, quantified, avoided, accepted mitigated, or transferred out.
  2. Value Creation is a process by which data assets are qualified and quantified to enable the business to maximize the value created by data assets.
  3. Organizational Structures and Awareness refers to the level of mutual responsibility between business and IT, and the recognition of fiduciary responsibility to govern data at different levels of management.
  4. Stewardship is a quality-control discipline designed to ensure the custodial care of data for asset enhancement, risk mitigation, and organizational control.
  5. Policy is the written articulation of desired organizational behavior.
  6. Data Quality Management refers to methods to measure, improve, and certify the quality and integrity of production, test, and archival data.
  7. Information Lifecycle Management is a systematic, policy-based approach to information collection, use, retention, and deletion.
  8. Information Security and Privacy refers to the policies, practices, and controls used by an organization to mitigate risk and protect data assets.
  9. Data Architecture is the architectural design of structure and unstructured data systems and applications that enables data availability and distribution to appropriate users.
  10. Classification and Metadata refers to the methods and tools used to create common semantic definitions for business and IT terms, data models, and repositories.
  11. Audit Information Logging and Reporting refers to the organizational processes for monitoring and measuring the data value, risks, and effectiveness of data governance.

From here the book dives into each one of these areas with specific actions that need to happen. I noted a few below.

Ultimately, I view this book as a good asset for getting started with Data Governance work. Howe
ver, it lacks some real best practices beyond suggesting the use of certain IBM tools. Governance is as much about getting people to compromise as it is about whether the metrics are in a red or green status. A playbook outlining the tasks won’t help in the relationships and politics  that this often boils down to. Is the pizza good? It just depends on who  you ask.

Other notes:

Page 38: This paragraph is critical. The nuance of it can go unheeded.

“It is important to recognize that a “1” rating is not inherently bad, and a “5” rating is not necessarily good. The Data Governance organization had to work with IT and business stakeholders and (preferably) develop a business case to determine whether it is feasible to increase the rating for a given category in the desired future state.

Page 42: I consider a charter to be pretty self explanatory, but the reality is it isn’t. This is a good recap.

“The Data Governance charter is similar to the Articles of Incorporation of a corporation. The charter spells out the primary objectives of the program and its key stakeholders, as well as roles and responsibilities, decision rights, and measures of success.”

Page 42: The break down of the Data Governance structure is pretty good too.

“The optimal organization for Data Governance is a three tier structure. The Data Governance council, at the pinnacle of the organization, includes senior stakeholders. At the next level down, the Data Governance working group consists of members who are responsible for governing data on a fairly regular basis. Finally, the data stewardship community had day-to-day, hands-on responsibility for data.

Page 79:

“Here are some of the responsibilities of an executive sponsor:

  • Have ultimate responsibility for the quality of data within the domain
  • Ensure the security and privacy of all sensitive data, such as PII and PHI, within the domain
  • Appoint data stewards with day-to-day responsibility for dealing with the data quality, security, and privacy issues within the domain
  • Establish and monitor metrics regarding the progress of Data Governance within the domain
  • Collaborate with other executive sponsors in situations where business rules collide, to ensure that the enterprise continues to derive maximum value from its data

Page 79-80:

“When a data stewardship program reaches maturity, the data steward should report into the business. At this point, it is important to ensure that there is a some level of oversight across all the data stewards, to ensure a consistency in roles and responsibilities and to develop a sense of community.”

Some commentary, the notion of a community is important. This data culture change is not just a top down manifest. You need to get everyone, especially projects, viewing data differently than they have been.

Page 95: There is a good example of a business rule which establishes which record is authoritative.

“Fortunately, that is where the rules of data survivorship come into play. The Data Governance rules of survivorship state that life insurance is the best source for birth date because that information determines premiums. Similarly, homeowner’s insurance is the best source for address information because that data is directly tied to the entity being insured.”


Picking the Best Team

“You wouldn’t believe it.” he says, although I’m sure I not only would believe it, but I can top it. “I was in first place in my league until Aaron Rodgers has a concussion and has to sit out. My back up was whoever the guy is from the Lions.” I pretended to listen, but I’m really thinking about my own team. These fantasy football stories aren’t for the person hearing the story, they’re for the person telling it. Occasionally it’s about how they won the league, but much more often, the tale is about a loss. “If Rodgers didn’t get hurt…” There’s always an “if.” He’ll be back next year.

The beauty of fantasy football is every year you get to pick your team. Most of the elite players – the A players – are taken in a draft or auction. The handful you select become your guys, your team. You then get a chance to pick up free agents during the season to supplement the team. It’s fun to look over the team and watch on Sundays.

Something similar is happening in the business world. We’ve hit a sweet spot with demand and productivity and it’s creating a Fantasy Football type of workforce.

Suppose for simplicity sake there are five types of employees – A, B, C, D, and F.

F
- Isn't skilled for the job
- Doesn't show up on time
- Doesn't care
D
- Isn't skilled for the job
- Shows up on time
- Training doesn't work
- Tries hard
C
- Skilled for the job
- Doesn't do anything beyond what is asked
- Performance is adequate
- Must be trained for every part of the job
B
- Skilled for the job
- Performance is good
- Quickly learns new aspects of the job
A
- Skilled for the job
- Performance is excellent
- Quickly learns new aspects of the job
- Can proactively expand the scope of the role
- Able to streamline or automate aspects of the job

Normally a big company, and for shorter durations, a smaller one too, will tolerate D employees in the hopes that they can become at least a C employee. But when the economy is tough F, D, and many C employees are let go. The business just can’t support them. This is your basic business cycle economy or put another way, aggregate demand for goods and services is down. When people stop buying, revenue suffers and revenue pays the bills like payroll and health care.

But what’s weird is corporate profits hit an all time high in the third quarter of 2010. A staggering $1.66 trillion. Up from $1.61 trillion in the second quarter and $1.30 trillion in the third quarter of 2009. A tremendous growth rate. Well, when you look closer at it, much of the third quarter growth was from the financial industry and the value of those gains tend to be theoretical or only materialize over long horizons.  But either way, profits are out there and cash is sitting on the books of many large companies. So why aren’t they hiring?

The reason is because of demand and the A employees. Demand is just enough to keep the machines running and creating economies of scale opportunities. But demand isn’t too high to hire extra workers (temporary hires are filling the gaps when needed) to pick up the slack. Meanwhile A employees are reviewing how the processes work and identifying inefficiencies. They are re-engineering their companies without causing disruption. Productivity goes up and meets a slowly improving demand level. The cycle continues.

A employees are talented and are getting raises. And just like in Fantasy Football, they are carrying their teams. If the raise isn’t there, they are being cherry picked by other companies.

This somewhat sounds like a structural economy issue as well. The demand is there, but the skills for the D, C, and B employees aren’t. This is an effect of globalization. The demand is there, but it’s being met not in the US, but in countries like India and China – manufacturing jobs particularly.  This doesn’t spell doom for D, C, and B employees because the scales tend to even out. The rising tide (cheap labor isn’t so cheap) in countries like China will make companies look again at the US, but while that is happening, education – learning how to problem solve – needs to take priority. Otherwise, their skills won’t be differentiated from other workers and other countries. We need more A employees.

This isn’t about labor anymore. It’s about talent. Elite performers get picked and others just fill out the roster or so the story goes.

Working Thoughts 1/11/2008
Examine Each Job as One of Many Crime Scenes

Working Thoughts 1/11/2009
Different Paths to Owning a Professional Sports Team

Working Thoughts 1/11/2010
Job Creation in the 2000s?

Value Creator – CEOs

Chief Executive Magazine and chiefexecutive.net are running the third annual list of CEOs that are value creators and value destroyers . The standard measurement of a CEO doing well is the stock price, but there are influences on a stock price that may or may not happen. For instance Apple’s stock price barely budged when news broke that the iPhone was going to be offered on the Verizon network. The stock didn’t move because it was already priced in – investors expected it.

The measurement Chief Executive Magazine uses is:

Economic Margin (EM) is calculated as (operating cash flow – capital charge)/invested capital. Companies with positive EM (greater than 0 percent) are creating wealth; those with negative EM are destroying it.

Here’s a portion of the list :

Top 10

Overall Ranking ’09 Rank Change from ’09 Rank MVIC 3 Yr. EM EM Change Management Quality Score Company CEO CEO Last Name
1     A A A A Priceline.com Jeffery H. Boyd Boyd
2 23 21 A A A A AFLAC Daniel P. Amos Amos
3 2 -1 A A A A Federated Investors J. Christopher Donahue Donahue
4 35 31 A A A A Apple Steven P. Jobs Jobs
5 4 -1 A A B A Amazon.com Jeffrey P. Bezos Bezos
6     A A B A Colgate-Palmolive Ian M. Cook Cook
7 7   A B A A Ecolab Douglas M. Baker, Jr. Baker
8     A A A A DeVry Daniel Hamburger Hamburger
9 40 31 A A B A Fastenal Willard D. Oberton Oberton
10 6 -4 A A C A C.H. Robinson Worldwide John P. Wiehoff Wiehoff

November 2010 Jobs Report and Wages

Here are the job market and compensation numbers for November 2010 (based on the job report):


Net gain
of 39,000 jobs in the month

  • Private sector payrolls increased by 50,000
    • Down from 160,000 last month
    • Worst performance in 10 months

  • Analysts expected an overall gain of 150,000
  • September was revised to a loss of 24,000 jobs from an original reading of 95,000 lost and a revised loss of 41,000
  • October was revised to a gain of 172,000 from an original reading of 151,000
  • The revisions for August, September, and October added 145,000 jobs to the economy
  • 6.1
    million people have been jobless for more than 6 months (long term
    unemployed) – virtually unchanged from August, September, and October

    • 41.9% of the unemployed are long term unemployed – inched up from 41.8% last month and 41.7% the month before
  • The main type of hire was for Temporary Help Service (+40,000) and since September of 2009 this employment has improved by 494,000
    • Its normally an indicator of an improving economic cycle, but a year of it indicates uncertain business conditions

  • Job Openings and Labor Turnover Survey (JOLTS), shows that job openings increased by 351,000 in October
  • The total number of job openings in October was 3.4 million, while the total number of unemployed workers was 14.8 million
  • The ratio of unemployed workers to job openings improved to 4.4-to-1 in October

Unemployment rate went up to 9.8%

  • Analysts predicted it would be 9.6%
  • The unemployment rate has been over 9% for 19 months – the longest such streak since the early ’80s
  • The employment to population ratio is 58.2% – relatively unchanged
  • The
    U-6 report, which is a broader group to count (workers who are part
    time but want to be full time and discouraged worker), stayed at 17.0%. This indicates the increase of the unemployment rate to 9.8% is a reflection of more people actively looking for jobs in November (these individuals are only counted if they are actively looking)
  • The unemployment rate for those with a college education is 5.1%
    • Highest in 40 years

  • PMI,
    a measure of manufacturing pace, is 56.6% and the 19th consecutive
    month of readings over 50 percent. Anything above 50% means the
    machines are running
  • Productivity, measured for the quarter, showed tepid growth of 2.3%

Specific Segment Job numbers:

  • Manufacturing lost 13,000 jobs
  • Construction lost 5,000 jobs
  • Retailers lost 28,100 jobs
  • Leisure and Hospitality Services gained 11,000 jobs
  • Government sector lost 11,000, Federal gained 2,000
  • Education and Health Services grew by 30,000 jobs
    • Health Care and Social Assistance grew by 34,000

  • Professional and Business Services grew by 53,000
    • 39.500 jobs added in Temporary Help

Wage (can be revised):

  • The average weekly paycheck (seasonally adjusted) is $642.87 – a decrease of $1.91
  • The average hourly earning (seasonally adjusted) is $19.19
  • Average
    weekly hours and overtime of production and nonsupervisory employees on
    private nonfarm payrolls by industry sector, seasonally adjusted is
    33.5 hours

Bureau of Labor Statistics

Job Report Stats Summary

Scared of Ideas or Open to Change?

He hears the alarm clock, hits snooze, and lays there for ten minutes somewhere between sleep and awake. “In the Hall of the Mountain King” by Edvard Grieg plays:

He does what I think is one of the hardest things in the world to do, he puts the first foot on the floor in the morning. He goes to the bathroom, runs the shower, and peers into the mirror. Everyday its the same. Same time, same song, same struggle. Everyday.

Routines are good for many aspects of our lives. We need to focus on what is different in our environment and routines keep us safe to do so. But the comfort of a routine can be disabling as well. For instance, there’s a field of study called Terror Management Theory and it describes what people do to repress an awareness of mortality. Here’s an excerpt from HarvardBusinessReview.com called Employees See Death When You Change Their Routines which enumerates three means for warding off these thoughts:

Studies show that we create three existential buffers to protect us from this knowledge: Consistency allows us to see the world as orderly, predictable, familiar, and safe. Standards of justice allow us to establish and enforce a code of what’s good and fair. Culture imbues us with the sense that we have contributed to, and are participating in, a larger and enduring system of beliefs.

As a manager it’s important to know which of your employees are lulled into this perceived safe zone and will need some coaxing when change is on the horizon. They’ll want to hold onto the way things are – they’re good at them, they understand what’s expected, and they are familiar – but it’s counterproductive. You’ll need to invest in re-establishing these buffers for them…

Unless they are risk takers. Many entrepreneurs don’t like routines. They want constant change with a little bit of chaos mixed in. Companies like Google seek them out because they tend to be disruptors and a disruption can be a money maker. Just last week the NY Times ran an article about how Google gave 10% raises across the board. Google’s growth has brought with it the bureaucracy of a big company. Some entrepreneurs are fleeing the company. The reason is because they can’t affect change quick enough. Their supply of patience is sapped.

Both types of worker, the comfort in routine and the risk taker, must answer this question posed by Bob Brennan of Iron Mountain to this employees:

What do you recommend we do?

You can get a real sense for who’s invested in moving the company forward, and who’s watching the company go by, with that very simple question.

Q. Why?

A. People lay out problems all the time. If they’ve thought through what should be done from here, then you’ve got somebody who’s in the game, who wants to move, and you can unlock that potential. Bystander apathy or the power of observation, in and of itself, is not very valuable. There are amazingly eloquent diagnosticians throughout the business world. They can break down a problem and say, “Here’s your problem.” But it’s prescriptions that matter. So how do we move from here, and what specifically do you recommend?

Working Thoughts 11/29/07
It’s Not a Recession but it Sure Feels like It

Working Thoughts 11/29/08
There Are Jobs for Low Level Employees?