Reflecting on a Job Market – Employee and Employer

To gain significant wealth in the US you have to take significant risk. Usually that means starting your own business or being on the ground floor with someone who is. The individuals who put their neck on the line deserve the spoils of that risk. The last three years proves the fittest survives in business.

We can somewhat reflect now. The once in a generation economy is behind us, so it’s time to see what the new world looks like. It’s lean and flexible. But a chasm is growing between the employer and the employees. Here are some stats from a Mercer survey I read about by Ben Rooney on CNNMoney called  Half of Workers Unhappy in their Jobs:

  • 32% of US workers are seriously considering leaving their job. Up from 23% in 2005.
  • Of the age group 25-34, 40% are seriously considering leaving. Within that number is 44% of employees who are 24 and younger. The cheap labor is ready to bolt.
  • And more alarming, 56% of senior managers are considering leaving. This compares to 34% of managers and 30% of non-managers. The experienced are also looking to jump to other opportunities.
  • A slightly different take, but 21% have disengaged from their employer, meaning they are not looking for a new job, but they are apathetic toward their current one. This could be burn out and it could mean the productivity gains via personnel has reached it’s limit.

Workers are getting disenfranchised by the circumstances of their employment. In addition to that, there are business owners who have moved away from the proper perspective. They’ve had leverage for over three years. Chances are they laid off some people. Those that remain should have a debt of gratitude. It could be worse.

The business owner who has survived is entitled to some fun, but they need to realize no one does it alone. I was out to dinner with a friend in the industrial fabrication and installation field of work. He had an exchange with his boss similar to the one in the movie below. I embellished it for effect, but much of this exchange is true, particularly the part about the water skis
http://www.xtranormal.com/watch/12155321/a-resignation-story

When Ideas Come Together

I sit here and type. Sometimes I have inspiration and sometimes its a slog. I do it because I love when ideas come rushing in. It’s like the end of the Usual Suspects when everything comes together. Its powerful and rewarding.

Good mysteries are fulfilling because we have to hunt for clues. They are rarely obvious and they are as much a study of logic and circumstance as anything else. It’s in our DNA to problem solve this way.

For this reason I’m optimistic about the long term future. The connectedness of the world is bringing figurative detectives together, each with their own clues, to solve problems. Ideas have a better chance to grow. Incremental improvements are good, but we are after leaps forward.

Take this chart from CNNMoney.com about Cisco’s prediction on the expansion of the internet.

And check out these videos from Steven Johnson about where ideas come from. The first one is a short artistic explanation and the second is a TED Talk. I really enjoy the story at the end.

–>http://video.ted.com/assets/player/swf/EmbedPlayer.swf

Defining the Future for the Class of 2011

Congratulations to the class of 2011. You’ve earned the gratification of moving your tassel from one side of the cap to the other.

What does the future hold? What is out there? You’ll be told to find yourself, follow your passion, and chase your goals. And many of you will wonder what those are. There’s debt, perhaps an entry level job, and monthly bills. Before you can commit to your boundless dreams you already have these torments and everything that goes along with it. Chances are you’ll be figuring out credit cards, bad bosses, and hung over early morning meetings. Every day is a new day.

If you learned anything in school, I hope you’ve learned how to think. To ask why? Anyone can follow directions, good thinkers are people who understand why they exist. Great thinkers design the instructions. What is the path? What are the steps? “If this happens, then do this” and the decision tree associated with it. Working through these scenarios develops an ability to cope with complexity. Said another way – for greater enlightenment, do you want a happy meal toy or a jet engine?

And I beg you to create something. The world is a better place when people work to assemble rather than tear apart. If you’re a business person I’d start with thinking about value proposition. Think about a situation and how things work together. What is the context for which a transaction operates? Consider the decisions people are making and the information they are using. What drives them to act and is the desired behavior? Is there a status quo? Have people accepted things as they are?

The world you experience is very different than the world I know. And that is good. There are generally two types of knowledge explicit and tacit.

  • Explicit Knowledge – is knowledge that has been or can be articulated, codified, and stored in certain media. It can be readily transmitted to others. The information contained in encyclopedias (including Wikipedia) are good examples of explicit knowledge.
  • Tacit Knowledge – involves learning and skill but in a way that is difficult to transfer from one person by means of writing it down or verbalizing it. Tacit knowledge can consist of habits and culture that we do not recognize in ourselves. I can tell you how to ride a bicycle, but you won’t know how until you learn to balance.

Graduation means you’ve probably spent close to $1,000 on books over the last few years acquiring explicit knowledge. You’ve studied in your dorm room and memorized facts about amortization. You’ve read what the top of Kilimanjaro looks like.

Now is the time to look yourself. It doesn’t have to be the top of a mountain, a high point works just fine. Experience the edge and feel the risk. Trace the path you’ve taken to this simple point. Once you’ve climbed one high point, you’ll climb another and another.

Nothing worth doing is easy and life isn’t fair. Your experience is unique, always ask why, and focus on creating something, anything. And remember, every day is a new day.

Using Data as a Predictor of Sports Success

There’s a huge celebration going on this week – a celebration of decision making. You see the NFL Draft starts Thursday (4/28/11) and runs through Saturday (4/30/11) and fans tune in to see who their team selects. No games are played, just people’s names being called.

Why do we care? The simple answer is hope. We’ve entrusted the future of our favorite teams to a room full of guys with spreadsheets. We want to believe they have the magic formula for selecting the players who succeed in the NFL. They’ve studied film, measured height, weight, speed, interviewed the candidates, and surveyed other experts. They’ve quantified all these inputs and ranked the candidates. Most of the time they tier them for purposes of trading up or down. Teams win Super Bowls because of these three days.

It’s a lot of data and yet every year mistakes are made. As a General Manager, the person ultimately making the decision, you need the hits to be proportionally more successful than your misses. And you need to learn from your data year over year to see which inputs pan out and which ones do not. From there you can use heuristics to simplify the ranking order and reduce the risk of missing on a selection.

Below are two videos. One is from the Sloan Sports Conference and it features Peter Tingling. I’m a fan of Mr. Tingling and his company, Octothorpe Software (this is not a paid endorsement). Peter provides a presentation about how how successful NHL drafts are.

The second video is from the most famous sixth round pick ever – Tom Brady. He is your classic case of not using the data correctly.

http://www.kaltura.com/index.php/kwidget/wid/_203822/uiconf_id/1898102/entry_id/1_bukfpvkn/

Simple Heuristics That Make Us Smart – A Book Review

Quick Take: Simple Heuristics That Make Us Smart is a collection of academia based essays proving the comparative value of decision making based on good enough information. The examples and anecdotes are good, but there is complex math to wade through. It isn’t a leisure read. However, each section can be consumed on it’s own. If you’re a student of decision making, whether it’s group dynamics or individual situations, then this book is a good heuristics reference.

Simple Heuristics that Make Us Smart Cover

Detail Review: Many of us have a comfortable chair which serves as our place to relax.
Its great for 40 winks. But why do we relish peacefully falling asleep
in a chair? Most of the time it’s because we are mentally exhausted. Everyday we are faced with an ever changing list of choices to make and each has a list of known variables and all kinds of factors which are unknown. We try to streamline choices that have worked so we don’t need to concentrate on it. I take the same route to work everyday even though there are probably another ten ways to get there, for instance.

I wish I had a computer in my head to compute all the different inputs into making a decision. I could continually collect data and analyze it practically to a 100% decision certainty. But I don’t have a computer or unlimited time, instead I rely on heuristics. Heuristics are simple methods for using particular cues and constraints to make a choice. Gerd Gigerenzer, Peter M. Todd, and The ABC Research Group authored this tome as a study of how accurate specific heuristics are.

Here are a few heuristics covered in the book:

Recognition
Definition – If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion.
Example – If I ask 100 Americans which city in Germany is more populated Berlin or Saarburg? The results will be close to 100% correct – Berlin is more populated. Of the 100 people few, if any, will recognize Saarburg as a city, but practically all of them will have heard of Berlin. Because of that recognition they will answer Berlin even though they know little about the actual number of people who live in either city.

Take the Best
Definition – When making a judgment based on multiple cues, the criterion are tried one at a time according to their cue validity, and a decision is made based on the first criterion which discriminates between the alternatives.
Example – Suppose we ask the question about population again, but instead of Saarburg we use Frankfurt. Berlin and Frankfurt are both recognizable so we must use other reasons to discriminate population. We pose a list of usual indicators of large populations – historical relevance, it’s a capital, tourism, sports teams, and so on. From that list we rank the list based on which ones usually are more of an indication of population and try to separate the two. We compare Frankfurt and Berlin for tourism and realize that Berlin is much more of a destination than Frankfurt is. We stop there and don’t review the other reasons. We take the best separator – tourism – and decide to invest no more time in evaluating. Berlin is the answer.

Take the Last
Definition – When making a judgment based on multiple cues, the criterion are sorted according to what worked last time. It uses memory of prior problem solving instances and works from what was successful before.
Example – I’m now comparing Frankfurt and Munich in population. I’ve heard of both so I can’t use Recognition. I use Tourism as the candidate since it worked with Berlin and Frankfurt. This time I go with Munich because they’ve hosted an Olympics and is more of a destination than Frankfurt. This answer is correct and time and energy was saved because I didn’t need to sort through all the other criteria.

In addition to those there are:

  • Franklin’s Rule – calculates for each alternative the sum of the cue values multiplied by the corresponding cue weights (validaties) and selects the alternative with the highest score.
  • Dawes’s Rule – calculates for each alternative the sum of the cue values (multiplied by a unit weight of 1) and selects the alternative with the highest score.
  • Good Features (Alba & Marmorstein, 1987) selects the alternative with the highest number of good features. A good feature is a cue value that exceeds a specified cutoff.
  • Weighted Pros (Huber, 1979) selects the alternative with the highest sum of weighted “pros.” A cue that has a higher value for one alternative than for the others is considered a pro for this alternative. The weight of each pro is defined by the validity of the particular cue.
  • LEX or lexicographic (Fishburn, 1974) selects the alternative with the highest cue value on the cue with the highest validity. If more than one alternative has the same highest cue value, then for these alternatives the cue with the second highest validity is considered, and so on. Lex is a generalization of Take the Best
  • EBA or Elimination by Aspects (Tsersky, 1972) eliminates all alternatives that do not exceed a specified value on the first cue examined. If more than one alternative remains, another cue is selected. This procedure is repeated until only one alternative is left. Each cue is selected with a probability proportional to its weight. In contrast to this probabilistic selection, in the present chapter the order in which EBA examines cues to determine by their validity, so that in every case the cue with the highest validity is used first.
  • Multiple Regression is a statistically analysis of how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This is beyond the capacity of a normal human and usually requires a resources like a computer.

The book uses the city example to run a test against a few heuristics and Regression testing (computing intensive). The results are startling when you consider the number of cues needed to reach the decision (a low number for Take the Best and Take the Last and a high number for the other three).

Here’s a chart showing relative performance for this particular case study:

As you can see, Take the Best and Regression Analysis are very similar in performance. This means if you pick the right Heuristic to use for the situation you can save time and resources and still get the performance that is comparable for the trade off (time and energy).

So what does this mean? Sometimes it’s the difference between life and death.

A
man is rushed to a hospital in the throes of a heart attack. The doctor
needs to decide quickly whether the victim should be treated as a
low-risk or a high-risk patient. He is at high risk if his life is
truly threatened, and should receive the most expensive and detailed
care. Although this decision can save or a cost a life, the doctor does
not have the luxury of extensive deliberation: She or he must decide
under time pressure  using only the available cues, each of which is,
at best, merely an uncertain predictor of the patient’s risk level. For
instance, at the University of California, San Diego Medical Center, as
many as 19 such cues, including blood pressure and age, are measured as
soon as a heart attack patient is admitted. Common sense dictates that
the best way to make the decision is to look at the results of each of
those measurements, rank them according to their importance, and
combine them somehow in to a final conclusion, preferable using some
fancy statistical software package.

Consider in contrast the simple decision tree below, which was designed
by Breiman and colleagues to classify heart attack patients according
to risk using only a maximum of three variables. A patient who has  a
systolic blood pressure of less than 91 is immediately classified as
high risk – no further information is needed. Otherwise, the decision
is left to the second cue, age. A patient under 62.5 years old is
classified as low risk; if he or she is older, the one more cue (sinus
tachycardia) is needed to classify the patient as high or low risk.
Thus, the tree requires the doctor to answer a maximum of three yes/no
questions to reach a decision rather than to measure and consider 19
predicators, letting life-saving treatment proceed sooner.

 

 

To wrap up, the book has many interesting essays as chapters, ranging from bicycle races, hindsight bias, ants, mate selection, and bargaining. It’s a solid 365 pages with small font. The math and the science can be dense, but the applicability of the results are real. It doesn’t sugar coat what goes into making heuristics worthwhile – a lot of up front analysis. It does however show how powerful those paths or decision trees can be once they are implemented.

Gerd Gigerenzer has other books that are probably more digestible for the heuristically curious (Gut Feelings: The Intelligence of the Unconscious and Calculated Risks: How to Know When Numbers Deceive You) but if you’re into behavior and why particular decision paths are more economical than others, then this book is a good educational read.


Other Reviews:
The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices – A Book Review / How Pleasure Works – A Book Review / Why We Make Mistakes – A Book Review / Drive: The Surprising Truth About What Motivates US – A Book Review / Rules of Thumb – A Review / I Hate People – A Review / The Job Coach for Young Professionals – A Review / A Review of The Fearless Fish Out of Water: How to Succeed When You’re the Only One Like You / A Quick Review of Johnny Bunko (a manga story)

Differentiating Using Strategy and Technology

The Academy Awards were a few weeks back and the popular movie The Social Network was nominated for Best Picture. It didn’t win the award, but it did elevate Facebook into a cultural phenomenon. It’s no longer another website – it’s Facebook. People care about it like their Nike running shoes, Apple iPod, and Starbucks coffee.

Each of these brands has used slight advantages in their products to become the dominate company in the space. How or why does this happen? Well, first I’ll mention luck. It always plays a role. In addition to luck, it’s the people.

Individuals and teams within these companies differentiate their offerings. They do so within a cost structure that maintains competitiveness and they do so with an eye toward value. Most people think of value as what Wal-Mart offers. One product 10 cents cheaper than a competitor and that is true in a commodities evaluation. Paper towels are paper towels. Value becomes much more abstract when the offering – product or service – has an association related to it. Starbucks originally pulled people in because the coffee was stronger. The association was that it woke up better than other options. And Apple combats technophobia because they create electronic devices that are easy to use.

This value is marginal at first, but then it snow balls. Getting it to snow ball is the key and then building on that is paramount. Facebook used exclusivity as the differentiator and then opened up the site to ride the network effect. Now it can exploit it’s pure numbers for monetary gain.

Earlier this year Goldman Sachs in a backroom deal valued Facebook at $50 billion dollars. Valuations like this have some to speculate that there is another tech bubble. Groupon, Google, Facebook, and others are the poster children.

In the world of the internet, small differences in your products can be the difference in sinking or swimming. Because of that Silicon Valley is leading the way in an escalating war for tech talent. Google is offering $20,000 more than average to the people they’ve targeted. Some firms are teaching their employees how to be entrepreneurs. In Silicon Valley it’s an inevitability, might as well make it a perk.

Do I think its a new tech bubble? I don’t. How engineers are using the internet now is very different than 15 years ago. Now it’s used to implement strategies that were inconceivable just three years ago. New approaches can separate and new technology can accelerate. What goes into the making of a Best Picture? It’s more than just film, it’s artistry.

Your Greatest Weakness

I’m the type of person who relies on metaphors and analogies. It’s just the way I absorb information. So as the sun shone on my face this past weekend, I couldn’t resist comparing the first warm up of the season to the optimism of a reborn employment market. Just like Chance the gardener said in Being ThereIn the garden, growth has it seasons. First comes spring and summer, but then we have fall and winter. And then we get spring and summer again.”

With hiring thawing out, the inevitable uptick in interviews will commence and we’ll see more media stories about the topic. For instance, over to HBR.org Priscilla Claman has a great blog entry called The Worst Interview Question (and How to Answer It). The focus of the writing is on the question:

 “What is your greatest weakness?”

The question from an interviewer standpoint is intended to show how the interviewee handles uncomfortable interactions. If an interviewee has prepared well, then it’s hard to gauge whether the interviewee can perform when unknown circumstances come up, which is bound to happen in the workplace. This type of awkwardness can paint the picture of how this person would react.

But as noted in the blog article, there’s downsides to the question. The first is that it can be embarrassing. And starting off a relationship with embarrassment is not usually a good idea. There’s lots of movies like this. The second is that strengths and weaknesses change depending on the culture and function the person is involved with. For instance, I love analogies is that a weakness? It depends. Because of this grey area interviewees create work around answers like “I’m a workaholic” so they don’t paint themselves into a corner.

However, as the blog states, there are a few good ways to reply. Check
out the cheesy xtranormal video I created this weekend while messing
around for an example.

 

 

http://www.xtranormal.com/site_media/players/jw_player_v54/player.swf

Working Thoughts 2/15/09
NatGeo Has Me Hooked Lately

Working Thoughts 2/15/08
Teachers Who Have the Creative Freedom to Teach

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.”