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How to Sound Smart About Something Most People Mess Up

By | Culture, Healthy Stuff | No Comments

When it comes to data and making decisions, most of us leave an incredibly valuable tool sitting unused in our proverbial decision-making toolboxes. PROBABILITY, defined as the likelihood something will or will not happen. You may know it as “the odds”. We don’t use this tool because we may not have wrapped our heads around it (acceptable, we can learn!) or because we think it doesn’t help us (a myth, let me convince you why).

So, why is this and and why is it important?

I’ll get right back to that, but I first need to ask a favor: PAUSE — Please do not search on the internet for the term “probability”. If you do, one of the very first things you will read is how a coin toss has 50/50 chances of landing on heads or tails. Herein lies a common next misstep – it is incredibly easy to interpret equal chances as meaning that the odds don’t matter or that an answer is unknowable. This is far from the case, but even the those of us who understand probability mess this up in certain situations.

Case in point, there is a true story of a world leader deciding if he should authorize a now infamous military action and he says on record, “this is a 50-50 shot” while deciding. Only it wasn’t. The official probability calculations were closer to 5:3 in favor of the operation being successful. He authorized the action and it was successful. The probability odds were right and it was not equivalent in likelihood to a coin toss.

So why did a well-educated and experienced leader say it was a toss-up decision when if he were using those same 5:3 odds while selecting his fantasy sports team he would likely have been discussing why that data was helpful? There is a pervasive gut tendency to think certain decisions are either guaranteed or unknowable when they are actually likely or unlikely. Throw in a healthy dose of not wanting to be wrong, and we have a recipe for saying something can’t be known, when degrees of certainty are actually quite knowable.

I’d like to make the case that this affects everyone reading. We come to conclusions and make decisions every day based on what we expect to happen, and we often do so with minimal data backing up those expectations. The value of harnessing probability is that it examines how certain you should be and how right you’ve been in the past. Probability tells you that if there are 9:1 odds something is likely to happen, the right answer is “yes!”, and not “maybe” or “I don’t know”.

How can this help you? Consider the following examples …

Question: “What are the odds of X sports team winning their next game?”

Answer: Between Vegas betting odds, fantasy leagues, sport media, and sports fans A LOT of people feel comfortable having this probability scenario.

Now think about these ….

Question: “Do we think the Smith family will renew their membership with us?”
Or how about …
“If I ask our newest member to donate to our campaign, do you think they will?”

Answer: “I think maybe, but that’s really hard to know!”
Or better yet …
“That’s a coin toss, I’m not sure we can know until we try.”

Examining and analyzing data is one of the biggest reasons I love my role at Daxko. You can use probability in your daily work or in your life as well. And seeing that most people are in the process of selecting their fantasy football teams right now, it’s a great time to put probability to work.


Constance M. is a Product Manager who enjoys organizing stuff and all things Joss Whedon.

data-blog

Data is Meaningless (Not Really – Hear Me Out)

By | Board, Industry, Leadership, Mission Delivery, Organizational Health, Trends Reports & Surveys | 3 Comments

As the new Daxko Product Manager focused on all things reporting and analytics across Daxko products, I spend 100% of my time listening to users, documenting needs, and forming and managing plans to ensure that what we do now will improve what is delivered to customers in the short and long terms. This is sincerely fun stuff.

In my 13 years of working with data in different jobs — from U.S. Space Command (yep, you can ask me about satellites) to federal child welfare benchmarking (happy to chat about child well-being trends) to Y-USA and Y data (program, membership and impact – you name it!), my hands-down favorite thing is this…

Data doesn’t answer questions well.

Nope, I’m not kidding.

Intuitively, we all know this. If your blood pressure is 160 over 110, you think, “that’s not good” … BUT if it was 170 over 120 when you measured it two weeks ago, then all of a sudden those very same numbers make you think, “I’m improving! This is good (or at least better)”. Those “bad looking” numbers are telling you you’re moving in the right direction.

As you might infer, “how was that collected?”, “so what?”, “compared to what?” and “well, that depends … “ are my go-to responses. That’s because if we don’t answer these questions we are in danger of not understanding what is actually going on and making bad decisions as a result.

Data need to be many things to be meaningful. At a minimum, it needs the following:

#1: It needs to be correct. A nurse measuring your blood pressure needs to not only know how to use the exact model of the cuff they put on your arm, they also need to read, remember, and write-down the right two numbers while using it. To go one further, the doctor who reads what the nurse wrote has to be able to decipher his or her handwriting or the whole process is nullified. The industry word for this is data integrity. It’s self-explanatory why it’s vital, but it’s also very easily not achieved — or even realized if you don’t have it.

#2: It needs to be presented in context. This is how we know what’s good, bad, or trending in a certain direction. In this example, we have blood pressure guidelines for healthy ranges. Not only do those exist, but there are different ranges for how old you are, if you’re male or female, or if you’re on a plan with your doctor to reach a certain goal. Heck, they can even change over time as new research emerges. Good health care providers will also tell you to consider this information in combination with other factors, such as family history, diet, weight, etc.

#3: It needs to be digestible. You could have access to the best information in the world, but if you can’t explain what you have, you can’t use it. If we all needed to explain systolic (top number) and diastolic (bottom number) of blood pressure, many would feel overwhelmed or get stuck on information that doesn’t answer their questions. In this example, if #1 and #2 above are done well, it’s much easier to take-in your blood pressure ratio than also having to take-in all the research behind it.

I’m drawn to analysis and reporting because good data is different than available data. I would argue that meaningful data is more important than Big Data, we’ve-always-collected-that data, and that’s-interesting data.

So let’s revise what’s above…

Data collected via sound methodologies and presented in appropriate context in a way that can be understood answers questions VERY well.

Daxko is working to give customers accurate, relevant, digestible data to our users. Some of the ways we are making this happen:
  • Improving our data warehouse so all customers will have just the right (depending on the needs of their organization) access to the  data they need
  • Elevating the custom reports user experience to provide easy, quick data points in context that will make a difference to the organization
  • A quick and accurate measurement of the positive difference you are making with your members (think of it like a cause-driven nonprofit NPS Score)
  • Refining the Donor Index to allow fundraisers the ability to create targeted outreach  campaigns just for donors

You can reach me at cmiller@daxko.com if you have thoughts about Daxko data and reporting – I’d love to talk to you.

Data is Meaningless (Not Really – Hear Me Out)

By | Culture, Life at Daxko | No Comments

As the new Daxko Product Manager focused on all things reporting and analytics across Daxko products, I spend 100% of my time listening to users, documenting needs, and forming and managing plans to ensure that what we do now will improve what is delivered to customers in the short and long terms. This is sincerely fun stuff. 

In my 13 years of working with data in different jobs — from U.S. Space Command (yep, you can ask me about satellites) to federal child welfare benchmarking (happy to chat about child well-being trends) to Y-USA and Y data (program, membership and impact – you name it!), my hands-down favorite thing is this…

Data doesn’t answer questions well. 

Nope, I’m not kidding. 

Intuitively, we all know this. If your blood pressure is 160 over 110, you think, “that’s not good” … BUT if it was 170 over 120 when you measured it two weeks ago, then all of a sudden those very same numbers make you think, “I’m improving! This is good (or at least better)”. Those “bad looking” numbers are telling you you’re moving in the right direction. 

As you might infer, “how was that collected?”, “so what?”, “compared to what?” and “well, that depends … “ are my go-to responses. That’s because if we don’t answer these questions we are in danger of not understanding what is actually going on and making bad decisions as a result. 

Data need to be many things to be meaningful. At a minimum, it needs the following: 

#1: It needs to be correct. A nurse measuring your blood pressure needs to not only know how to use the exact model of the cuff they put on your arm, they also need to read, remember, and write-down the right two numbers while using it. To go one further, the doctor who reads what the nurse wrote has to be able to decipher his or her handwriting or the whole process is nullified. The industry word for this is data integrity. It’s self-explanatory why it’s vital, but it’s also very easily not achieved — or even realized if you don’t have it.

#2: It needs to be presented in context. This is how we know what’s good, bad, or trending in a certain direction. In this example, we have blood pressure guidelines for healthy ranges. Not only do those exist, but there are different ranges for how old you are, if you’re male or female, or if you’re on a plan with your doctor to reach a certain goal. Heck, they can even change over time as new research emerges. Good health care providers will also tell you to consider this information in combination with other factors, such as family history, diet, weight, etc.

#3: It needs to be digestible. You could have access to the best information in the world, but if you can’t explain what you have, you can’t use it. If we all needed to explain systolic (top number) and diastolic (bottom number) of blood pressure, many would feel overwhelmed or get stuck on information that doesn’t answer their questions. In this example, if #1 and #2 above are done well, it’s much easier to take-in your blood pressure ratio than also having to take-in all the research behind it. 

I’m drawn to analysis and reporting because good data is different than available data. I would argue that meaningful data is more important than Big Data, we’ve-always-collected-that data, and that’s-interesting data. 

So let’s revise what’s above…

Data collected via sound methodologies and presented in appropriate context in a way that can be understood answers questions VERY well. 

You can reach me at cmiller@daxko.com if you have thoughts about Daxko data and reporting – I’d love to talk to you.


Constance M. is a Product Manager who enjoys organizing stuff and all things Joss Whedon.