My dad once gave me the book “Effective Problem Solving” by Marvin Levine. The book covers the creative problem-solving process with various puzzles, logic problems, and multiple problem-solving principles. It’s a great read, and if you’re looking to sharper your mind, check out the crypto arithmetic section at the end of this article. Levine has some important concepts that may shape your approach if you’re building a program around digital transformation or data modernization.
A few weeks ago, I discussed the problems with getting people to use data with some colleagues. Using data, and getting data, are not the same thing. If you’ve read any of my data literacy material, you can see a difference. Training people on the tools and building data platforms is the “if we build it, they will come” model, and it rarely works. Newer approaches combat that with better training, changing cultures, looking for outcomes over outputs, and investing in competency centers for data. It’s easy to see the drive for data literacy programs. Building an excellent data literacy program can help you build skills in your people and help make them more effective problem solvers with data. These skills tend to be grouped into four categories: collection, evaluation, application, and management (more on these in the following article and my upcoming talk at https://www.groupfuturista.com/FOWA2022.2/). Data literacy programs generally need to address the different categories effectively so that individuals have a way to use data and tools to be effective in decision making. Training teams in these categories uplifts your company and helps shift toward a data-driven culture. But does that mean everyone will become data literate soon, and you’ll have an army of citizen data scientists running around? Unfortunately, the answer is likely no because of the concept of intimate engagement.
In Levine’s book, he doesn’t cover big data strategies and complex data architecture, but he does cover what I think might be the most important concept in data: intimate engagement. Intimate engagement is how we used to find our next BI analyst or champion in the line of business. My team and I would look for people to train in the company that we thought “had the knack for data.” After all, building skills and centers of influence with existing teams pays back tenfold compared to convincing a business partner to invest in additional resources. We didn’t seek out people who could code or had a background in data. Don’t get me wrong, those are great, and if we had those resources, we would use them, but we were looking for something much more simplistic. We were looking for people who had a natural inclination to dig into concepts and lean into problems. That is precisely what Levine talks about in his book.
When something doesn’t work right, some people seek understanding of a problem, and others seek to find someone else to figure out the problem. The first group are your experts in problem framing. In the book, Levine gives an example of a stuck car seat. Some individuals sometimes say, “oh well, someone will have to figure this out for me.” In other cases, they might lean in, look at what’s blocking the seat, and find a bottle that had lodged in the seat. The second group intimately engages the problem, it doesn’t mean that they’ll always be able to fix it, but they’re leaning in to understand the issue. Those are the individuals you want to invest in and train in data. Those individuals are the diamonds in the rough in your enterprise. They can accelerate your data literacy and digital transformations at a rate much faster than you’re going.
I have had numerous experiences finding the next data talent by looking for those individuals. If you want to learn more about intimate engagement, problem-solving, and data, feel free to reach out to me!
Now, as promised, here’s some Fun with Crypto Arithmetic:
There should be a unique digit to be replaced with a unique alphabet.
A letter can represent 1 and only 1 number.
A number can replace 1 and only 1 letter.
The result should satisfy the predefined arithmetic rules, i.e., 2+2 =4, nothing else.
Digits should be from 0-9 only, the first digit cannot be 0.
Example:
S + M = MZ
In this case, it is likely that M is 1, since we can’t add two single digits to something that starts with anything other than 1. So we know that S + 1 = 1Z. The only digital that can be added to 1 to get a two digital number is 9, therefore 9 + 1 = 10 is the same thing as S + M = MZ. We solved it!
Here’s a harder one for you to try!
SEND + MORE = MONEY
Click Here For Answer