CoolData blog

1 April 2015

Mind the data science gap

Filed under: Training / Professional Development — Tags: , , — kevinmacdonell @ 8:10 pm

 

Being a forward-thinking lot, the data-obsessed among us are always pondering the best next step to take in professional development. There are more options every day, from a Data Science track on Coursera to new masters degree programs in predictive analytics. I hear a lot of talk about acquiring skills in R, machine learning, and advanced modelling techniques.

 

All to the good, in general. What university or large non-profit wouldn’t benefit from having a highly-trained, triple-threat chameleon with statistics, programming, and data analytics skills? I think it’s great that people are investing serious time and brain cells pursuing their passion for data analysis.

 

And yet, one has to wonder, are these advanced courses and tools helping drive bottom-line results across the sector? Are they helping people at nonprofits and university advancement offices do a better job of analyzing their data toward some useful end?

 

I have a few doubts. The institutions and causes that employ these enterprising learners may be fortunate to have them, but I would worry about retention. Wouldn’t these rock stars eventually feel constrained in the nonprofit or higher ed world? It’s a great place to apply one’s creativity, but aren’t the problems and applications one can address with data in our field relatively straightforward in comparison with other fields? (Tailoring medical treatment to an individual’s DNA, preventing terrorism or bank fraud, getting an American president elected?) And then there’s the pay.

 

Maybe I’m wrong to think so. Clearly there are talented people working in our sector who are here because they have found the perfect combination of passions. They want to be here.

 

Anyway — rock star retention is not my biggest concern.

 

I’m more concerned about the rest of us: people who want to make better use of data, but aren’t planning to learn way more than we need or are capable of. I’m concerned for a couple of reasons.

 

First, many of the professional development options available are pitched at a level too advanced to be practical for organizations who haven’t hired a full-time predictive analytics specialist. The majority of professionals working in the non-profit and higher-ed sectors are mainly interested in getting better at their jobs, whether that’s increasing dollars raised or boosting engagement among their communities. They don’t need to learn to code. They do need some basic, solid training options. I’m not sure these are easy to spot among all the competing offerings and (let’s be honest) the Big Data hype.

 

These people need support and appropriate training. There’s a place for scripting and machine learning, but let’s ensure we are already up to speed on means/medians, bar charts, basic scoring, correlation, and regression. Sexy? No. But useful, powerful, necessary. Relatively simple and manual techniques that are accessible to a range of advancement professionals — not just the highly technical — offer a high return on investment. It would be a shame if the majority were cowed into thinking that data analysis isn’t for them just because they don’t see what neural networks have to do with their day to day work.

 

My second concern is that some of the advanced tools of data science are deceptively easy to use. I read an article recently that stated that when it’s done really well, data science looks easy. That’s a problem. A machine-learning algorithm will spit out answers, but are they worth anything? (Maybe.) Does an analyst learn anything about their data by tweaking the knobs on a black box? (Probably not.) Is skipping over the inconvenience of manual data exploration detrimental to gaining valuable insights? (Yes!)

 

Don’t get me wrong — I think R, Python, and other tools are extremely useful for predictive modelling, although not for doing the modelling itself (not in my hands, at least). I use SQL and Python to automate the assembly of large data files to feed into Data Desk — it’s so nice to push a button and have the script merge together data from the database, from our phonathon database, from our broadcast email platform and other sources, as well as automatically create certain indicator variables, pivoting all kinds of categorical variables and handling missing data elegantly. Preparing this file using more manual methods would take days.

 

But this doesn’t automate exploration of the data, it doesn’t remove the need to be careful about preparing data to answer the business question, and it does absolutely nothing to help define that business question. Rather than let a script grind unsupervised through the data to spit out a result seconds later without any subject-matter expertise being applied, the real work of building a model is still done manually, in Data Desk, and right now I doubt there is a better way.

 

When it comes to professional development, then, all I can say is, “to each their own.” There is no one best route. The important thing is to ensure that motivated professionals are matched to training that is a good fit with their aptitudes and with the real needs of the organization.

 

18 January 2015

Why blog? Six reasons and six cautions

Filed under: CoolData, Off on a tangent, Training / Professional Development — Tags: , — kevinmacdonell @ 4:12 pm

THE two work-related but extracurricular activities I have found the most rewarding, personally and professionally, are giving conference presentations and writing for CoolData. I’ve already written about the benefits of presenting at conferences, explaining why the pain is totally worth it. Today: six reasons why you might want to try blogging, followed by six (optional) pieces of advice.

I’ve been blogging for just over five years, and I can say that the best way to start, and stay started, is to seek out motives that are selfish. The type of motivation I’m thinking of is intrinsic, such as personal satisfaction, as opposed to extrinsic, such as aiming to have a ton of followers and making money. It’s a good selfish.

Three early reasons for getting started with a blog are:

1. Documenting your work: One of my initial reasons for starting was to have a place to keep snippets of knowledge in some searchable place. Specific techniques for manipulating data in Excel, for example. I have found myself referring to older published pieces to remind me how I carried out an analysis or when I need a block of SQL. A blog has the added benefit of being shareable, but if your purpose is personal documentation, it doesn’t matter if you have any audience at all.

2. Developing your thoughts: Few activities bring focus and clarity to your thoughts like writing about them. Some of my ideas on more abstract issues have been shaped and developed this way. Sometimes the office is not the best environment for this sort of reflective work. A blog can be a space for clarity. Again — no need for an audience.

3. Solidifying your learning: One of the best ways to learn something new is by teaching it to someone else. I may have had an uncertain grasp of multiple linear regression, for example, when I launched CoolData, but the exercise of trying to explain data mining concepts and techniques was a great way to get it all straight in my head. If I were to go back today and re-read some of my early posts on the subject, which I rarely do, I would find things I probably would disagree with. But the likelihood of being wrong is not a good enough reason to avoid putting your thoughts out there. Being naive and wrong about things is a stage of learning.

Let’s say that, motivated by these or other reasons, you’ve published a few posts. Suddenly you’ve got something to share with the world. Data analysis lends itself perfectly to discussion via blogs. Not only analysts and data miners, but programmers, prospect researchers, business analysts, and just about anyone engaged in knowledge work can benefit personally while enriching their profession by sharing their thoughts with their peers online.

As you slowly begin to pick up readers, new reasons for blogging will emerge. Three more reasons for blogging are:

4. Making professional connections: As a direct result of writing the blog I have met all kinds of interesting people in the university advancement, non-profit, and data analysis worlds. Many I’ve met only virtually, others I’ve been fortunate to meet in person. It wasn’t very long after I started blogging that people would approach me at conferences to say they had seen one of my posts. Some of them learned a bit from me, or more likely I learned from them. A few have even found time to contribute a guest post.

5. Sharing knowledge: This is the obvious one, so no need to say much more. Many advancement professionals share online already, via various listservs and discussion forums. The fact this sharing goes on all the time makes me wonder why more people don’t try to make their contributions go even farther by taking the extra step of developing them into blog posts that can be referred to anytime.

6. Building toward larger projects: If you keep at it, slowly but surely you will build up a considerable body of work. Blogging can feed into conference presentations, discussion papers, published articles, even a book.

Let me return to the distinction I made earlier between intrinsic and extrinsic motivators — the internal, more personal rewards of blogging versus the external, often monetary, goals some people have. As it happens, the personal reasons for blogging are realistic, with a high probability of success, while the loftier goals are likely to lead to premature disillusionment. A new blog with no audience is a fragile thing; best not burden it with goals you cannot hope to realize in the first few years.

I consider CoolData a success, but not by any external measure. I simply don’t know how many followers a blog about data analysis for higher education advancement ought to have, and I don’t worry about it. I don’t have goals for number of visitors or subscribers, or even number of books sold. (Get your copy of “Score!” here. … OK — couldn’t resist.)

The blog does what I want it to do.

That’s mostly what I have to say, really. I have a few bits of advice, but my strongest advice is to ignore what everybody else thinks you should do, including me. Most expert opinion on posting frequency, optimum length for posts, ideal days and times for publishing, click-bait headlines, search engine optimization and the like is a lot of hot air.

If you’re still with me, here are a few cautions and pieces of advice, take it or leave it:

1. On covering your butt: Some employers take a dim view of their employees publishing blogs and discussing work-related issues on social media. You might want to clear your activity with your supervisor first. When I changed jobs, I disclosed that I intended to keep up my blog. I explained that connecting with counterparts at other universities was a big part of my professional development. There’s never been an issue. Be clear that you’re writing for a small readership of professionals who share your interests, an activity not unlike giving a conference presentation. Any enlightened organization should embrace someone who takes the initiative. (You could blog secretly and anonymously, but what’s the point?)

2. On “permission”: Beyond ensuring that you are not jeopardizing your day job, you do not require anyone’s permission. You don’t have to be an expert; you simply have to be interested in your subject and enthusiastic about sharing your new knowledge with others. Beginners have an advantage over experts when it comes to blogging; an expert will often struggle to relate to beginners, and assume too much about what they know or don’t know. So what if that post from two years ago embarrasses you now? You can always just delete it. If you’re reticent about speaking up, remember that blogging is not about claiming to be an authority on anything. It’s about exploring and sharing. It’s about promoting helpful ideas and approaches. You can’t prevent small minds from interpreting your activity as self-promotion, so just keep writing. In the long run, it’s the people who never take the risk of putting themselves out there who pay the higher price.

3. On writing: The interwebs ooze with advice for writers so I won’t add to the noise. I’ll just say that, although writing well can help, you don’t need to be an exceptional stylist. I read a lot of informative yet sub-par prose every day. The misspellings, mangled English, and infelicities that would be show-stoppers if I were reading a novel just aren’t that important when I’m reading for information that will help me do my job.

4. On email: In the early days of email I thought it rude not to respond. Today things are different: It’s just too easy to bombard people. Don’t get me wrong: I have received many interesting questions from readers (some of which have led to new posts, which I love), as well as great opportunities to connect, participate in projects, and so on. But just because you make yourself available for interaction doesn’t mean you need to answer every email. You can lay out the ground rules on an “About” page. If someone can’t be bothered to consider your guidelines for contact, then an exchange with that person is not going to be worth the trouble. On my “About this Blog” page I make it clear that I don’t review books or software, yet the emails offering me free stuff for review keep coming. I have no problem deleting those emails unanswered. … Then there are emails that I fully intend to respond to, but don’t get the chance. Before long they are buried in my inbox and forgotten. I do regret that a little, but I don’t beat myself up over it. (However — I do hereby apologize.)

5. On protecting your time: Regardless of how large or small your audience, eventually people will ask you to do things. Sometimes this can lead to interesting partnerships that advance the interests of both parties, but choose wisely and say no often. Be especially wary of quid pro quo arrangements that involve free stuff. I rarely read newspaper travel writing because I know so much of it is bought and paid for by tour companies, hotels, restaurants and so on, without disclosure. However, I’m less concerned about high-minded integrity than I am about taking on extra burdens. I’m a busy guy, and also a lazy guy who jealously guards his free time, so I’m careful about being obliged to anyone, either contractually or morally. Make sure your agenda is set exclusively by whatever has your full enthusiasm. You want your blogging to be a free activity, where no one but you calls the shots.

6. On the peanut gallery: Keeping up a positive conversation with people who are receptive to your message is productive. Trying to convince skeptics and critics who are never going to agree with you is not. When you’re pushing back, you’re not pushing forward. Keep writing for yourself and the people who want to hear what you’ve got to say, and ignore the rest. This has nothing to do with being nice or avoiding conflict. I don’t care if you’re nice. It’s about applying your energies in a direction where they are likely to produce results. Focus on being positive and enabling others with solutions and knowledge, not on indulging in opinions, fruitless debates, and pointless persiflage among the trolls in the comments section. I haven’t always followed my own advice, but I try.

Some say “know your audience.” Actually, it would be better if you know yourself. Readers respond to your personality and they can only get to know you if you are consistent. You can only be consistent if you are genuine. There are 7.125 billion people in the world and almost half of them have an internet connection (and access to Google Translate). Some of those will become your readers — be true to them by being true to yourself. There is no need to waste your time chasing the crowd.

Your overarching goals are not to convince or convert or market, but to 1) fuel your own growth, and 2) connect with like-minded people. Growth and connection: That’s more than enough payoff for me.

6 October 2014

Don’t worry, just do it

2014-10-03 09.45.37People trying to learn how to do predictive modelling on the job often need only one thing to get them to the next stage: Some reassurance that what they are doing is valid.

Peter Wylie and I are each just back home, having presented at the fall conference of the Illinois chapter of the Association of Professional Researchers for Advancement (APRA-IL), hosted at Loyola University Chicago. (See photos, below!) Following an entertaining and fascinating look at the current and future state of predictive analytics presented by Josh Birkholz of Bentz Whaley Flessner, Peter and I gave a live demo of working with real data in Data Desk, with the assistance of Rush University Medical Center. We also drew names to give away a few copies of our book, Score! Data-Driven Success for Your Advancement Team.

We were impressed by the variety and quality of questions from attendees, in particular those having to do with stumbling blocks and barriers to progress. It was nice to be able to reassure people that when it comes to predictive modelling, some things aren’t worth worrying about.

Messy data, for example. Some databases, particularly those maintained by non higher ed nonprofits, have data integrity issues such as duplicate records. It would be a shame, we said, if data analysis were pushed to the back burner just because of a lack of purity in the data. Yes, work on improving data integrity — but don’t assume that you cannot derive valuable insights right now from your messy data.

And then the practice of predictive modelling itself … Oh, there is so much advice out there on the net, some of it highly technical and involving a hundred different advanced techniques. Anyone trying to learn on their own can get stymied, endlessly questioning whether what they’re doing is okay.

For them, our advice was this: In our field, you create value by ranking constituents according to their likelihood to engage in a behaviour of interest (giving, usually), which guides the spending of scarce resources where they will do the most good. You can accomplish this without the use of complex algorithms or arcane math. In fact, simpler models are often better models.

The workhorse tool for this task is multiple linear regression. A very good stand-in for regression is building a simple score using the techniques outlined in Peter’s book, Data Mining for Fundraisers. Sticking to the basics will work very well. Fussing with technical issues or striving for a high degree of accuracy are distractions that the beginner need not be overly concerned with.

If your shop’s current practice is to pick prospects or other targets by throwing darts, then even the crudest model will be an improvement. In many situations, simply performing better than random will be enough to create value. The bottom line: Just do it. Worry about perfection some other day.

If the decisions are high-stakes, if the model will be relied on to guide the deployment of scarce resources, then insert another step in the process. Go ahead and build the model, but don’t use it. Allow enough time of “business as usual” to elapse. Then, gather fresh examples of people who converted to donors, agreed to a bequest, or made a large gift — whatever the behaviour is you’ve tried to predict — and check their scores:

  • If the chart shows these new stars clustered toward the high end of scores, wonderful. You can go ahead and start using the model.
  • If the result is mixed and sort of random-looking, then examine where it failed. Reexamine each predictor you used in the model. Is the historical data in the predictor correlated with the new behaviour? If it isn’t, then the correlation you observed while building the model may have been spurious and led you astray, and should be excluded. As well, think hard about whether the outcome variable in your model is properly defined: That is, are you targeting for the right behaviour? If you are trying to find good prospects for Planned Giving, for example, your outcome variable should focus on that, and not lifetime giving.

“Don’t worry, just do it” sounds like motivational advice, but it’s more than that. The fact is, there is only so much model validation you can do at the time you create the model. Sure, you can hold out a generous number of cases as a validation sample to test your scores with. But experience will show you that your scores will always pass the validation test just fine — and yet the model may still be worthless.

A holdout sample of data that is contemporaneous with that used to train the model is not the same as real results in the future. A better way to go might be to just use all your data to train the model (no holdout sample), which will result in a better model anyway, especially if you’re trying to predict something relatively uncommon like Planned Giving potential. Then, sit tight and observe how it does in production, or how it would have done in production if it had been deployed.

  1. Observe, learn, tweak, and repeat. Errors are hard to avoid, but they can be discovered.
  2. Trust the process, but verify the results. What you’re doing is probably fine. If it isn’t, you’ll get a chance to find out.
  3. Don’t sweat the small stuff. Make a difference now by sticking to basics and thinking of the big picture. You can continue to delve and explore technical refinements and new methods, if that’s where your interest and aptitude take you. Data analysis and predictive modelling are huge subjects — start where you are, where you can make a difference.

* A heartfelt thank you to APRA-IL and all who made our visit such a pleasure, especially Sabine Schuller (The Rotary Foundation), Katie Ingrao and Viviana Ramirez (Rush University Medical Center), Leigh Peterson Visaya (Loyola University Chicago), Beth Witherspoon (Elmhurst College), and Rodney P. Young, Jr. (DePaul University), who took the photos you see below. (See also: APRA IL Fall Conference Datapalooza.)

Click on any of these for a full-size image.

DSC_0017 DSC_0018 DSC_0026 DSC_0051 DSC_0054 DSC_0060 DSC_0066 DSC_0075 DSC_0076 DSC_0091

31 May 2014

Presenting at a conference: Why the pain is totally worth it

One morning some years ago, when I was a prospect researcher, I was sitting at my desk when I felt a stab of pain in my back. I’d never had serious back pain before, but this felt like a very strong muscle spasm, low down and to one side. I stood up and stretched a bit, hoping it would go away. It got worse — a lot worse.

I stepped out into the hallway, rigid with pain. Down the hall, standing by the photocopier waiting for her job to finish, was Bernardine. She had a perceptive eye for stuff, especially medical stuff. She glanced in my direction and said, “Kidney stone.”

An hour later I was laying on a hospital gurney getting a Toradol injection and waiting for an X-ray. It was indeed a kidney stone, and not a small one.

This post is not about my kidney stone. But it is a little bit about Bernardine. Like I said, she knew stuff. She diagnosed my condition from 40 feet away, and she was also the first person to suggest that I should present at a conference.

At that time, there were few notions that struck terror in my heart like the idea of talking in front of a roomful of people. I thought she was nuts. ME? No! I’d rather have another kidney stone.

But Bernardine had also given me my first copy of Peter Wylie’s little blue book, “Data Mining for Fundraisers.” With that, and the subsequent training I had in data mining, I was hooked — and she knew it. Eventually, my absorption with the topic and my enthusiasm to talk about it triumphed over my doubts. I had something I really wanted to tell people about, and the fear was something I needed to manage. Which I did.

To date I’ve done maybe nine or ten conference presentations. I am not a seasoned presenter, nor has public speaking become one of my strengths. But I do know this: Presenting stuff to my counterparts at other institutions has proven one of the best ways to understand what it is I’m doing. These were the few times I got to step back and grasp not only the “how” of my work, but the “why”.

This is why I recommend it to you. The effort of explaining a project you’ve worked on to a roomful of people you’re meeting for the first time HAS to force some deeper reflection than you’re used to. Never moving beyond the company of your co-workers means you’re always swimming in the same waters of unspoken assumptions. Creating a presentation forces you to step outside the fishbowl, to see things from the perspective of someone you don’t know. That’s powerful.

Yes, preparing a presentation is a lot of work, if you care about it enough. But presenting can change your relationship with your job and career, and through that it can change your life. It changed mine. Blogging also changed my life, and I think a lot more people should be blogging too. (A post for another day.) Speaking and writing have rewarded me with an interesting career and professional friendships with people far and wide. These opportunities are not for the exceptional few; they are open to everyone.

I mentioned earlier that Bernardine introduced me me Peter Wylie’s book. Back then I could never have predicted that one day he and I would co-author another book. But there it is. It gave me great pleasure to give credit to Bernardine in the acknowledgements; I put a copy in the mail to her just this week. (I also give credit to my former boss, Iain. He was the one who drove me to the hospital on the day of the kidney stone. That’s not why he’s in the acknowledgements, FYI.)

Back to presenting … Peter and I co-presented a workshop on data mining for prospect researchers at the APRA-Canada conference in Toronto in 2010. I’m very much looking forward to co-presenting with him again this coming October in Chicago. (APRA-Illinois Data Analytics Fall Conference … Josh Birkholz will also present, so I encourage you to consider attending.)

Today, playing the role of a Bernardine, I am thinking of who I ought to encourage to present at a conference. I have at least one person in mind, who has worked long and hard on a project that I know people will want to hear about. I also know that the very idea would make her vomit on her keyboard.

But I’ve been there, and I know she will be just fine.

11 February 2014

Teach them to fish: A view from IT

Filed under: IT, Training / Professional Development — Tags: — kevinmacdonell @ 5:52 am

Guest post by Dwight Fischer, Assistant Vice President – CIO, Information Technology Services (ITS), Dalhousie University

(When I read this post by our university’s CIO on his internal blog, I thought “right on.” It’s not about predictive modelling, and CoolData is not about IT. But this message about taking responsibility for acquiring new skills hit the right note for me. Follow Dwight on Twitter at @cioDalhousieU — Kevin)

I recently recommended OneNote to a colleague. OneNote is a venerable note-taking and organizational tool that is part of the Microsoft Office suite. I spoke to the merits of the application and how useful and versatile a tool it is, particularly now that it is fully integrated with mobile devices through the cloud. I suggested that she look online and find some resources on how to use it.

Busy as she is, she asked her administrative assistant to look up how to use OneNote, who in turned called the HelpDesk looking for support. The Help Desk staff need to know a lot of information, but software expertise is not the type of thing they can and are able to provide deeper-level support. Unless they were to use the software on a day-in, day-out basis, how could they? As it was, the caller did not get the support she expected.

If that individual instead had gone to Google (or Bing, Yahoo, YouTube, whatever) and asked the question, they would have received a torrent of information. All she needs to understand is how to ask or phrase the question.

  • “Tips on using OneNote”
  • “OneNote quick Tutorial”
  • “Help with OneNote”

It occurs to me that we have provided support to our clients for so long, they have developed an unhealthy dependence on IT staff to answer all their issues. Meanwhile, the internet has developed a horde of information and with it, many talented individuals who simply like to share their knowledge. Is it all good information? Not always, but if you just do a little searching and modify your search terms, you’ll certainly find relevant information. Often times you’ll find some serendipitous learning as well.

We need to help our clients make this shift. Instead of answering their questions, coach them on how to ask questions in search engines. Give them a fish and they’ll eat for a day. Teach them to fish and they’ll eat heartily. And save the more unique technology questions for us.

P.S. I used to go to the bike store for repairs. I could do a lot of work on my bikes, but there were some things I just couldn’t do. But with a small fleet, that was getting expensive. I started looking up bike repair issues in YouTube and lo and behold, it’s all right there. I might have bought a tool or two, but I can darn near fix most things on the bikes. It just takes some patience and learning. There are some very talented bike mechanics who put out some excellent videos.

2 December 2013

How to learn data analysis: Focus on the business

Filed under: Training / Professional Development — Tags: , , , — kevinmacdonell @ 6:17 am

A few months ago I received an email from a prospect researcher working for a prominent theatre company. He wanted to learn how to do data mining and some basic predictive modeling, and asked me to suggest resources, courses, or people he could contact. 

I didn’t respond to his email for several days. I didn’t really have that much to tell him — he had covered so many of the bases already. He’d read the  book “Data Mining for Fund Raisers,”  by Peter Wylie, as well as “Fundraising Analytics: Using Data to Guide Strategy,” by Joshua Birkholz. He follows this blog, and he keeps up with postings on the Prospect-DMM list. He had dug up and read articles on the topic in the newsletter published by his professional association (APRA). And he’d even taken two statistics course — those were a long time ago, but he had retained a basic understanding of the terms and concepts used in modeling.

He was already better prepared than I was when I started learning predictive modeling in earnest. But as it happened, I had a blog post in draft form (one of many — most never see the light of day) which was loosely about what elements a person needs to become a data analyst. I quoted a version of this paragraph in my response to him:

There are three required elements for pursuing data analysis. The first and most important is curiosity, and finding joy in discovery. The second is being shown how to do things, or having the initiative to find out how to do things. The third is a business need for the work.

My correspondent had the first element covered. As for the second element, I suggested to him that he was more than ready to obtain one-on-one training. All that was missing was defining the business need … that urgent question or problem that data analysis is suited for.

Any analysis project begins with formulating the right question. But that’s also an effective way to begin learning how to do data analysis in the first place. Knowing what your goal is brings relevance, urgency and focus to the activity of learning.

Reflect on your own learning experiences over the years: Your schooling, courses you’ve taken, books and manuals you’ve worked your way through. More than likely, this third element was mostly absent. When we were young, perhaps relevance was not the most important thing: We just had to absorb some foundational concepts, and that was that. Education can be tough, because there is no satisfying answer to the question, “What is the point of learning this?” The point might be real enough, but its reality belongs to a seemingly distant future.

Now that we’re older, learning is a completely different game, in good ways and bad. On the bad side, daily demands and mundane tasks squeeze out most opportunities for learning. Getting something done seems so much more concrete than developing our potential. 

On the good side, now we have all kinds of purposes! We know what the point is. The problems we need to solve are not the contrived and abstract examples we encountered in textbooks. They are real and up close: We need to engage alumni, we need to raise more money, we need, we need, we need.

The key, then, is to harness your learning to one or more of these business needs. Formulate an urgent question, and engage in the struggle to answer it using data. Observe what happens then … Suddenly professional development isn’t such an open-ended activity that is easily put off by other things. When you ask for help, your questions are now specific and concrete, which is the best way to generate response on forums such as Prospect-DMM. When you turn to a book or an internet search, you’re looking for just one thing, not a general understanding.

You aren’t trying to learn it all. You’re just taking the next step toward answering your question. Acquiring skills and knowledge will be a natural byproduct of what should be a stimulating challenge. It’s the only way to learn.

 
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