The Gallup Q12

The Gallup Q12 Index Gallup’s employee engagement work is based on more than 30 years of in-depth behavioral economic research involving more than 17 million employees. Through rigorous research, Gallup has identified 12 core elements — the Q12 — that link powerfully to key business outcomes. These 12 statements emerged as those that best predict employee and workgroup performance. The Twelve Questions are: 1. Do you know what is expected of you at work? 2. Do you have the materials and equipment to do your work right? 3. At work, do you have the opportunity to do what you do best every day? 4. In the last seven days, have you received recognition or praise for doing good work? 5. Does your supervisor, or someone at work, seem to care about you as a person? 6. Is there someone at work who encourages your development? 7. At work, do your opinions seem to count? 8. Does the mission/purpose of your company make you feel your job is important? 9. Are your associates (fellow employees) committed to doing quality work? 10.Do you have a best friend at work? 11.In the last six months, has someone at work talked to you about your progress? 12.In the last year, have you had opportunities to learn and grow?

Engaged Employees

Study after study shows that engaged employees are more loyal, they’re more productive and they yield increased customer satisfaction, higher financial returns and greater shareholder values..

from a 2016 Dreamforce session “How Salesforce Uses Culture + Tech to engage employees” https://www.youtube.com/watch?v=JTHhwjJXn54

The “State of the Global Workplace” study found 13% of employees engaged (“inovating”), 24% actively disengaged (“sabotaging”) and 63% disengaged (“sleepwalking”)

Data Tai Chi

taichiI used to know a guy who would say “Tai Chi is not a martial art. Tai Chi is the exercise a gentleman does every morning so that he will be able to practice martial arts.”

The same is true of enhancing your organization’s data culture. We call this “Human Intelligence“, a collection of practices that you do in your organization to prepare it for the demands of living in a data culture. No matter which of the many tools or systems of analytics you try to use you will need people who work well in certain kinds of groups, people who are happy in an environment that demands constant learning, people who can navigate situations that are full of unknowns and risk.

Every situation is both a challenge to practice and an invitation to learn, and, in the end, being able to learn is a harder thing than being able to practice. I always remember my father, who was brilliant at his work, saying “I learn more now in a day than I used to learn in years.”

To be able to do effective work and effective learning individuals are dependent on the organizational culture that supports (or hinders) them. Organizations are dependent on the individuals who work within them to build and maintain the data cultures they need to thrive and survive.

These two principles, Mutual dependence and prioritizing learning are the core of a program of Human Intelligence. The mutual dependence between individuals and their groups and the immense  advantage granted if they can leverage every situation, whether the outcome is positive or negative, to drive themselves forward into new insights and new skills, is something that must be worked at and exercised by all organizations that want to succeed.

The Data Culture Uplift Workbook: 7 Steps towards Change

workbook-image3 If you think your organization needs to improve its data culture, what can you do ? What is a practical course to take to get achieve that improvement?

Step 1: Set a baseline. Have an organization wide discussion about what you are going to attempt and then have everyone take the Data Fluency Inventory (a survey available on this site). The result will be a score that indexes your organization’s current data culture. This will give you a base for discussing what changes you should attempt and whether or not the changes you try are successful.

Step 2: Get help. A lot of the problems around data culture come from the tunnel vision that all of us have about our daily activities. Find a helpful outsider, a teacher, a tutor, or a helpful consultant that can be part of your discussions and who will be able to look at your organization with fresh eyes.

Step 3: Try to decide what kind of organization you are in terms of your data culture.

Are your people already data aware and your decisions already guided and evaluated using data ? If so you are in  “recon pull”, you can give mission style orders (where you tell people what their goal is but not how to get there) and expect group leaders to be flexible and creative as they respond to conditions on the ground.

Does your organization mostly collect historical data? Do you use reports to illuminate the past but not to guide future decisions. Do your members require education and encouragement to come to grips with the complexity of their data ? This is an organization in “command push” mode. You will need to give people detailed plans to follow while you put programs in place to up their game. You will need to look at your hiring process and at what kind of training and opportunities for independent work you offer. There is nothing wrong with finding yourself in this mode. It just means you have a lot of training and build  up to do. Its a good thing to know that, right now, you should focus on walking rather than trying to run.

When you make a self assessment you are in a position to try to make plans to change. The plans you make and the things you try should match the current state of your data culture.

Step 4: Start working on a data flow map. In order to come to grips with your data culture you will need to know what data is flowing through your organization, who is changing it and where it can be found. There are tools you can use to make your map, or you can just put it in a spreadsheet or a word document.

The important things about a data flow map are true about the all such maps, no matter how complex they are.

The map, and changes to the map, need to be part of your daily discussions about data.

The map will never cover all of your data flows, but the effort of maintaining it will boast your data culture no matter how complete, or incomplete, it is.

Step 5. Add some habitual routine to your daily practice that makes you look up from your tunnel focus on the current problem and the current state of your data. You can decide that every time you find yourself in a discussion about data your will play the “penny game”. Or you can play “kick the bucket” and put three little buckets on your conference table and take the time to discuss what data you think is in each bucket before you make any decisions.

Step 6. Make plans for change. Implement your changes. The plans you make and the way you try to implement them will be different depending on your own self-assessment as an organization. But while you’re making your plans and while your implementing them, take the chance to practice the penny game or kick the bucket. As you work through your plans diagram the pieces of your data flows that your are thinking about into your data flow map.

Step 7. After you have worked away at your changes for a while….Go back to step 1 and repeat.

Have another organization wide discussion and go back and have everybody take the Data Fluency Inventory again. Compare this score with your base line score. Discuss what you expected to happen and what actually happened. Talk about what you might try next.

Just like an exercise program, don’t expect your success to be spectacular, and do expect that you will often fall back and have to start the process again.

 

 

 

 

Playing “A Penny for your Thoughts”

heads How can flipping a coin help you solve problems? Most of our day we are operating out of habit and deeply ingrained responses, we rarely bring our full consciousness to the issues at hand.
Just like grace before meals, or the common blessings, the penny game is a habit that imports a little conscious attention during the flow of the days business.
When you find yourself worried about, talking about, making plans about, complaining about some issue having to do with data…. take a coin out of your pocket.
Flip it once….. heads is “known”, tails is “unknown”
now flip it again, and combine the second result with the first result.
You will get one of the four corners of the “data square”
the known know
the known unknown
the unknown known
or
the unknown unknown.
Spend just a few minutes considering the fix you’re in from the point of view of that corner of the data square: let’s say you got
first flip: known, second flip:unknown.
So what about the current situation do you “know” that your “don’t know”? Is there any chance that handling things differently could bring some of the unknown knowledge to light? What effect does missing this knowledge have on your chances of success? Do the people in your organization realize that there is missing knowledge when they operate in this area? …. you can generate a list of questions from any corner of the square.
You don’t have to try to analyze your current situation from all four corners… one will be enough to shake you loose from your fixation on the problem at hand and give you a chance to give it a little context, and that’s the first step in coming to grips with your data.
The human mind can only focus on a few things at once (but puts up the front of being aware of “everything”) the goal is not to train yourself to be “more aware of data” , there is no trick that will give you a superhuman mind, the goal is to train yourself to always check on things you may not be thinking of, and the need for that never goes away.

Walk before you can Run

walk While its great to think that companies can improve their data culture and become more successful by empowering their members and leveraging their creativity, it may be that many organizations are not in a place where that works for them…
In military theory there are 2 different strategies that armies can take. One is called “recon pull”: you have highly trained troops, and officers that have been trained to use their own initiative to exploit the immediate situation (in the US Marines this is called “mission orders”, your orders tell you the goal, not how to get there). The other is called “command push”: if your people lack training and experience then you plan everything from the top, lay out all the possible options and what everybody is supposed to do when they happen, think of the scripts that are prepared for sales calls… Then everybody is expected to “follow instructions”, and do what the manual says in all cases to get their work done.
I think it would be good to recognize when an organization just does not have the training and experience in its staff to start out by trying to be “agile” and recommend that they start where they are by trying to be good at “command push”, but do it in a way that will also gradually grow them towards the strengths and abilities that would make a cultural shift possible.  So you’re saying… lets make your current culture stronger and more explict and start doing things that will build up the attitudes and skills you need…. sort of like trying to train a water polo team where most of the members didn’t know how to swim.
The idea would be that then when they tried to shift to a more agile stance there would be a much higher chance they would be successful at it  and that if they try it without preparation they will fail and end up in an even worse position than they were in the beginning
Knowing and accepting your current situation is a big part of becoming “data oriented” I think this would make consulting with people about their data culture much more of a “realistic” and “real world” prospect. and it would mean that you would not be labeling such weak organizations “bad”, just recognizing the real situation they are in and giving them affirmation for being willing to face up to it and help about things they can do to get “stronger”  sort of like the exercises the therapist gives you when you have a bad back as opposed to starting out a new class in karate

Kick all Three Buckets

For any decision or plan there are three different kinds of data to consider. Let’s say three different “buckets” of data.

greenbucket The green bucket. This is the data you already have and understand.

yellowbucket The yellow bucket. This is data that I know is available, but that I judge is not important enough to spend the resources to gather and analyze.

redbucketThe red bucket. This is data that I don’t know is out there, but that potentially could have a big effect on my plan or decision.

The 2016 Presidential race offers us contrasting example. For the Clinton campaign the green bucket was really big. They invested heavily in big data and opposition research.

Since their data operation was so extensive that they had actually mapped every potential voter and what their issues might be, we can assume that for them the yellow bucket was small. There was probably not much data that they did not feel it was worthwhile to go out and get if they suspected it existed.

In the red bucket for the Clinton campaign we can put the “wikileaks” emails. Something that came out of left field and had enormous potential to change the race.

For the Trump campaign the green bucket was small. They did make some investment in data, but they relied most on their rallies. Their yellow bucket was big, there was a lot about the voters that they did not know and that they judged that it would not be worth while to try and find out. The Trump campaign’s red bucket turned out to have a lot of surprises linked to his past, which they were not prepared for since they made a conscious decision to not do operational research against their own candidate.

The three buckets are always there, but human beings have a hard time staying aware of them. The human mind has a limited capacity for attention, but is great at fooling itself into thinking that it is aware of everything that is going on. The bucket metaphor is a tool for forcing yourself to open up your awareness around critical decisions. Just like a pilot who goes through his checklist everything he takes off, you can’t trust yourself to “naturally” think of all the options.

— from a 2016 Dreamforce presentation: The Secret Emotional Life of Data https://www.youtube.com/watch?v=5Itw8v0w37o

Two Pennies for your thoughts

When you’re trying to change people’s attitudes it helps to have a simple exercise that they make part of their everyday routines. Here’s an exercise that you can make a building block of your organization’s data culture.

Whenever you find yourself discussing any question that might bear on data take out a coin and flip it twice. Depending on what combination of heads and tails comes up, discuss your data issue from one of the corners of the data square:

headsheadsHeads, Heads: The Known Knowns. Talk about what you know you know about your data. Think about whether you should know more, or whether you should have additional data.

headstailsHeads, Tails: The Known Unknowns. There is always more data out there that you can afford to collect. Talk about what you could know about your data, but you have decided that its not worth it to try and know. Talk about why it is impractical to move these issues into the Known Knowns.

tailsheadsTails, Heads: The Unknown Knowns. There are always things you already know, but that you are forgetting you know, or not paying attention to what you know. Talk about what data you might already have that you could bring into the discussion about this issue.

tailstailsTails, Tails: This is the tricky one. There is data that you don’t know you don’t know. Sometimes this can prove fatal if you don’t know about some development that is going to radically change the playing field. Sometimes its just a sign of limited exposure, and you need to invest more in scouting the field. Talking about the unknown unknowns can seem like you’re addressing a black hole, but it something you have to always keep in mind.