The Value of Data to a Community


The truth most of us discover
 

We can all benefit from having more data on hand about our habits. Diets are a simple example, and still relevant in January of a New Year. Regardless of which program works for you, part of it is creating more efficiency between what we consume and what we accumulate in the system.

    The word is appropriate, because we're talking about a system of interconnected parts. Although it's tempting to jump on the fad bandwagon and clean house every so often, the best approach to create lasting change and improve wellness is the judicious way, following a personalized nutrition program. What works for one person, doesn't work or apply across the board to the other.

    There are some universal principles we can adopt. So many calories in and more calories out if we want to lose weight. Counting calories by writing everything down is a way to become aware of what we consume, taking our weight weekly, a way to see outcome and adjust based on the information.

    But the principle itself has no information about what and how our specific body works. We may have allergies, experience intolerance for certain foods that although great for others, actually slow down our metabolism.

    Nutrition is a serious discipline that requires some study and research, but also experience i practice. And it's not just about weight control, but helping make sense of the chemistry, which comes down to knowing how to read the data, learning about our personal context (lifestyle), and cross referencing to data from a larger group of people.

    A new book by Lisa Mosconi helps us understand the relationship between our cognitive power and what we eat. In Brain Food (out in March), Mosconi, who unsurprisingly grew up in Italy, reviews brain science, the microbiome, and nutritional genomics, noting that the dietary needs of the brain are substantially different from those of the other organs.

    The benefits of a better diet are clear, and the answer is not fillers in packaged goods. It's always my test when I go to a supermarket how many ingredients are listed on the package? Made in Italy is easy to tell, few and recognizable. In fact, whole grain and organic foods are available at low cost at Coop in Italy and even Switzerland (though everything is more expensive there).

    Nutrition value labels, list of ingredients, and place of origin are all data points we likely use when we go grocery shopping to make sense of what's available. How many of you go to more than one store or supermarket, as available?

    Four markets and two specialty stores are in my rotation, one fairly distant but quality eating at a reasonable price is worth every penny in gas and time. Trader Joe's does have a surprisingly high number of products imported from Italy. We do love our simple food, and knowing what's in it.

    Imagine if you had visibility into what foods stores plan to stock across stores, instead of having to physically go (many still catching up online), and check. Many families build their weekly shopping routines based on proximity. What would happen if more data and information about inventory and even best times of day/week to shop were available for a certain geography?

    Most food chains have loyalty programs, yet until very recently there did not seem to be an intelligent use of purchasing habits coupons were pretty random, and I'm not talking about Target and the famous case of targeting expecting women in The Power of Habit.

    From what markets stock on the shelves, we know what sells. But we don't know what would sell, were it available. It would be fascinating to learn about potential trends from foods people buy in aggregate, and forecast local taste and opportunity, for example. We can infer some of the data from restaurants — but in the U.S. things have become quite standardized, which is both good and bad for research purposes.

Data has value to a community

    The point, beyond the incontrovertible fact that I am a foodie — hence the aggressive exercise regiment — is that data is valuable to understand what makes a community, well, a community.

    Data geeks and industry professionals likely do collect all kinds of data on their household, maybe comparing with their family in other states of countries — we do — but most of us would benefit even more with a broader set, with evidence for their county, say.

    However, data sets sit with different companies in different systems, none of which we, the subjects, have access to it. So we could not even apply a better analysis and technology to it than what each specific company has chosen to use, which may or may not provide insights, depending on who is doing the analysis and why.

    It's a bit of a pickle, pun intended, because it could really help us make better decisions if we had more visibility into what's going on in our region. A good example of this dilemma emerged from Amazon's HQ2 bids. As the New York Times chronicles#, the tech giant benefited from a treasure trove of data on each community that submitted an entry.

    Each city put its best foot forward, outlining technical schools, number of faculty, access to specific demographics, and even adding discretionary incentives, normally not on the books, “special grant funds, special bond funds, naming of roads,” things nobody can find or learn, unless they're put together in a bid of this kind.

    It was a great way to start amassing data on physical assets otherwise not available to cross reference with digital data trails. But what about the communities themselves? This is also an opportunity for the community to do a deeper dive about Denver#, an entry made public.

    In fact, the difference between the Denver bid and a promotional site (see Philadelphia# for an example), can help us see the value of data, and not just benefits and sound bites, to educating a community and illuminating possibilities, beyond an Amazon's HQ office.

    We've become accustomed to data going one way, from us to organizations, and hardly think about the value of aggregate data to our own community. While anecdotal evidence we experience of what sells, how much construction is going on, traffic volumes, peak gym season, new schools, and so on, is useful, we tend to notice what is available — hence availability bias — and miss what we don't know.

   Better/more data is useful for so many decisions we make in life. Even the position of a house in a block can make a big difference to snow drift or foliage collecting on the lawn — small things that over the years do increase friction and maintenance.

    Same for where the windows are relative to sunlight, and how big a switch is your power on (hint: it it's small, pick a house on a switch with more houses, you'll have the power back sooner, or not even lose it.) Imagine now having the big picture on traffic trends for the past ten years — where they're increasing, which roads take the brunt, etc. etc.

    On the pragmatic side, we can start by becoming more aware of the positive role of data in our lives (and not just organizations' content cherry picked to appeal to us) and seek to be more observant, withholding premature judgment by cross referencing it with other data.

    If you'll pardon one more abstraction to the big picture, learning to think better starts with ingesting better data (vs. opinion), and cross referencing it across disciplines. Working from actual data and first principles is better. Sound bites can sound neat, but end up biting us in surprising ways.

    This is why it's a good idea to get as close to the source as possible — actually read Socrates, and Homer, and the classics from many cultures, the foundational works from many disciplines. We want to learn ourselves how to form an opinion, which involves principles, values, and proof.

    It's helpful to have a guide, but think of it more like cooking than baking. Don't stop at the recipe — better to use as a guideline to get curious about a dish, improve it by adding of personal taste and reference points. Simpler works for my palate, but this doesn't exclude layers of flavor.

    Now imagine that community is an organization, and the data highlights behaviors. Then imagine what it would be like to cross reference that data on behaviors with data about customer experience based on outcomes. How valuable would it be to learn what is helpful and what detracts in aggregate and cross reference it with outcomes?

   For more food-related metaphors, are conversations more like cooking or baking?

 

[image Calvin & Hobbes]