Thursday, March 31, 2016

Data and values

There have been some prominent new calls for more and better data on water during the last couple weeks. Charles Fishman kicked things off with an opinion piece in the New York Times saying that the best and simplest answer to changing how we think about water is to "fix water data." Several days later, the White House held a first-of-its-kind water summit. During the live event, many speakers made references to better data, which were further echoed in the event materials

In the background, I and many others chimed in on Twitter with some differing perspectives. On the Public Record (here and here) and John Fleck (here) wrote good, thoughtful commentaries, with many good, thoughtful comments by readers. Then came a brief letter to the editor in the LA Times from a couple of researchers from the University of California saying in essence that data also needed to be turned into useful information. Then came an interview with a tech sector leader focused on water data for, you know, innovation and stuff came out in Water Deeply. I'm starting to think this issue might stay elevated for a while, and can't help but have some thoughts about it. 

As always, context really matters and data is (yes, I'm going to use the singular) one of those things that can, of course, be really, really good to have. We can all point to examples where data is helpful. Of course it is, we use it every day. On the other hand, we can all probably point to examples where more data is not that helpful, and in fact makes things more confusing. And whether we lean more heavily in one direction or the other probably has everything to do with where we are sitting and the kinds of choices we have to make at any given moment.

From my limited, California-centric, supply-heavy focus, I do have a lot of questions about how and where and what kind of more and better data will be useful. A lot of those same questions are raised in the OtPR and Fleck pieces, with solid examples. And, yet, I still find I have some things to say.

The first is that, in my experience, data collection, management, and (especially) analysis is not cheap. Or simple. In any way. Sure, one could argue they are getting easier in some ways with more sophisticated tools (sorta kinda). But the problems also amplify. More data in new formats means figuring out how to reconcile it with the old. It requires constant updating on every level, from actual data points to storage to stats. Figuring out signal to noise from more and better data is harder in most cases. It takes time. And very talented people who can actually get their hands on said data, which is a whole other can of worms. Then there is the interpretation (e.g., does urban water in California account for 10 or 20 percent of use?).

The second is the *much* bigger question of what we do with all this water data. In my mind, many of the most pressing issues we are dealing with are actually about values. Really deeply held and diverging values. And when it comes to values, data is of limited utility. It just is. 

Maybe it would help to be more clear about the cases where we do need more/better water data. Which questions are we trying to answer? Which can't be answered with data? Which are the biggest priority? What kind of more/better data do we need? Who gets to decide? How will they decide? Who will have access to it? What ethical guidelines are we going to agree to? What do we do with the more/better data once we have it?

For me, even those questions largely lead back to values, where data isn't so helpful. I do really believe many of the biggest frontiers right now are deeply psychological (and not so much from a cognitive or behavioral perspective). Which is why I write so much these days about the things that I write about. Relational work, especially around emotions, conflict, and discomfort in science. Complexity. Compassion. Deep listening. Grief. They are absolutely not the most easiest or cheapest or most popular things to think about, but trying *not* to simplify the questions or the answers is, in my mind, the work that really needs more attention. 

Related writing:


  1. Great article. I think data can be used to create more certainty if metrics are reported referencing the data. For example, Shasta reservoir has over 4,000,000 acre feet of water as of today with a snowpack in its watershed of 95% of normal. Last year it had 2,700,000 acre feet with a snowpack of 5% of normal. Recognizing this data means that farmers relying on this water supply north of the delta are receiving 100% of their water allocation. It might also suggest that urban and environmental water users of this water supply would receive the same. If you look at reservoirs South of the Delta, this story is not as plentiful.

    Using this data, practical decisions can be made to create certainty of the water supply for different regions. It can also relieve frustration and anxiety provided the data is used objectively and without political purpose.

  2. Disclaimer: I've had 24 years of field work in collecting water data.

    On Fishman's oped piece, I could not agree more. We need more quality water data to better manage what little we have of this valuable resource. Fishman was looking at the issues from a 'top down' approach.

    About your blog post, you covered the bases well. I spent several moments drilling down in your deep link references. I particularly loved the commentary and the corresponding comments in the OtPR blog.

    The funny thing in all of this is that nobody addressed the actual mechanics of how to accomplish getting the detailed water data needed.

    The brutal truth is that water data collection is not easy and there is much support work (site visits, O&M work, Discharge Measurements, verification of collected data, etc) that drives the quality of the collection.

    The interesting thing with hydrological data collection practices is that we are just beginning to get 'good' at it. In the last 10 years, the quality of the instrumentation and data collection equipment has skyrocketed. I can collect higher time series resolution data and incorporate site Meta Data into the collection. Our tail end analytical software is getting better too.

    Detailed data comes at a cost. Of course, I'm looking at this from the bottom up.


    1. Hi Dave, thank you for sharing your perspective. I agree that the mechanics of data collection, management, analysis, etc are important in this discussion. I have also been in positions where I do all of those things and agree 100% with you that it is just not easy. I like your phrase that "detailed data comes at a cost." Sums it up well. Now, I also see the highly political side a lot too, and it's really not easy from that end either. Thanks again.