Beyond the ordinary

I was in East Germany (DDR) when the Berlin wall came down. The background to this story is too detailed and irrelevant to go into here. What is relevant about this story is how quickly things unraveled in the DDR. Before 1989, there were many problems, but everything still had the appearance of being solid. Those people that had a grip on power, appeared to have a firm grip, and even though there was dissent, change did not seem imminent. Those in power in the DDR concentrated all their efforts on maintaining the status quo, and the focus was on looking good, rather than being good. So, as the difference between the east and the west widened, and the propaganda in the east looked thinner and thinner, there was little substance in the DDR to hold things together when the Soviet Union changed direction, and internal dissent began to grow. So, like a house of cards, something that looked solid from the outside collapsed with such speed that it left you wondering how it had survived so long.

Many people imagine when they grab numbers, all emotion goes out the window, and the act of grabbing at numbers makes things objective, scientific. Well, it does not. Many people see what they want to see, and use numbers to justify this. I used to dabble in the stock market, and would occasionally lurk in the odd stock chat room. You would think with shares that it would all be about numbers. You would think people would look at things such as the price-to-earnings ratio, the cash flow, the assets, their market share, the potential growth and so forth. You would think that these things are objective facts, putting aside the odd cooked book, and that share purchase and sale decisions would be guided by these objective facts. But they are not. I have observed many people ride a stock down to nothing, becoming euphoric with each fleeting uptrend, and acting as cheerleaders as they rode the rollercoaster down to its inevitable bottom. No one can question their decision to hang onto that stock even in the face of overwhelming evidence. Anyone who questions the stock is booed off stage.

The fact is, buying shares is not a financial decision for many people, it is an emotional one. If they sell the share they are admitting that they made a wrong decision, and so many people’s egos are either too fragile, or so big, that they cannot accept the possibility that they made a mistake. Yet, to get rich on the stock market, and do this through skill rather than luck, you need to be dispassionate. You need to recognize that you will make bad decisions at times, recognize when you have done this, and change tack accordingly. But you cannot do this if you spend your whole time only collecting data that makes your share purchase decisions look good. If you focus on looking good, you will eventually lose all your money on the stock market, unless you have lucked on the right shares. If on the other hand you focus on being good, and collect data that can help you to be good, then you will have a fighting chance of success. Moreover, if you do succeed, you will automatically look good too. However, if you find yourself in that situation, looking good may no longer be so important.

In East Germany in 1989, the new people in power seemed to make a few last desperate efforts to focus on being good, rather than just looking good. But it is no good trying to change at one second to midnight, when all the forces that will bring about your demise are on an inescapable collision course. They left it all too late.

The library sector too is under threat, and everything that seems solid now can vanish in the blink of a historical eye. Libraries have centuries of history, and for centuries have played a critical role in distributing knowledge. This history does not guarantee a future. And, if you don’t focus your efforts on being good, but instead on looking good, then your library risks drifting faster toward hostile external changes; and in the process your library will forfeit influence for reaction.

If you want to be successful in the share market, you have to be dispassionate, you have to see things as they really are, and act accordingly. If you want to be successful in business you have to do the same. The feedback mechanisms for the library sector are slower than the share market. If you make a poor share purchase decision, the market will soon let you know. The flow of feedback in the public policy area is much slower, and more complicated. But it still flows. The lack of a rapid feedback mechanism does not mean you are insulated from change, it just means you have more time for more decisions. This economic feedback delay provides a buffer, and this buffer is a gift. If you are to use this buffer wisely, it will be to focus on how you can make your library be good.

To answer that question, how can I focus on being good, you have to come back to the core question: what value are we actually providing? There is no point attempting to collect any data aimed at being good, if you cannot succinctly and concretely describe your value proposition. Once you know what your value proposition actually is, and you can describe it in concrete words that actually mean something real; only then can you do things such as: measure how well you deliver against that value proposition; improve the efficiency with which you deliver that value proposition; and improve that value proposition itself. I think this is something that many library managers will struggle with in this changing world, and it is ultimately the reason why, when they are honest with themselves in the moments outside of the political spotlight, that they might be displeased with their performance and operational data.

I cannot tell you what your value proposition is; this is something you need to sort out for yourself. However, I can tell you that if you do develop a clear, sustainable and compelling value proposition for your services, then you are on the path to doing something beyond the ordinary with your data.

For example, if your key value proposition is to develop active communities of readers, then your focus will be on building, sustaining and growing social groups that are focused on reading. Your measures, therefore, might include more than just counts of participation; they might include qualitative data – such as stories about how these groups have changed individuals’ lives. You can use this data then, to focus on where you are getting the greatest impact, and replicating these success drivers across the program. You can also use this data to grow the program, as most people connect with stories, rather than numbers. But in doing all this, the question you always need to keep asking yourself, is how is our program providing something unique, where is this program succeeding, and where is the value added too thin to be sustainable. In other words, you need to be critical, not just cherry pick the good stories to use as marketing fodder for stakeholders and clients. To do this you will need numbers to keep you objective. The basic counts of participation will allow you to critically contextualize the qualitative feedback. If you have fantastic feedback, but when you drill into the participation you find that most of your social reading activities have fewer than three participants, then you will know that something is wrong, and you will not take the good news stories at face value. You can also use some more sophisticated quantitative data to assess the validity of the qualitative data. For example, your programs should build social networks. You could measure participant’s exposure to new social networks by mapping their movement through the different groups and programs you are supporting. If you find that it is mostly the same group of people attending most of your programs, then once again you will know something is not working, and that you need to look at the root cause of why the program is not attracting new people.

If you want to be good, you need data to inform critical thinking, and you need to listen dispassionately and act on any criticism the data is providing. This is why using data to be good is so much more difficult than using data to simply look good. If you only use data to look good, then you can just cherry pick the good stories. However, if you do this, and your program is only attracting a small cohort of faithful followers, then how long do you think such a program will be able to stand up against external scrutiny when funding becomes a critical issue, and the library is one of the biggest line items in your organization’s budget?

If you are still struggling with how data could be used to do more than the ordinary, consider the following. When it comes to teaching information literacy, or whatever the terminology for that area of learning is used, then the typical approach of most academic libraries I have seen is to count the number of classes, count the length of the class, and count the number of participants. Staff might also count the amount of time they spent on preparation, and they might provide more detailed data on the attendees, such as whether they were first year students, etc. Finally, they might ask for feedback via a survey. If you look at the data through the lens of what you currently do, rather than through the lens of what is possible, you will end up with “so what” data that at best only tells you how busy you are. So what is possible? This of course will vary from one library to the next. At the most basic level, it is possible to improve the academic skills of many more students than you currently teach. So, if this is your target, then at the very least you need to know what proportion of the cohort you are reaching, and how successful your targeted efforts have been. To achieve this you might integrate marketing with information literacy. Marketing needs data to have a reasonable chance of success. So you will need to know which students are attending (i.e., collect their student numbers), which students are not attending (i.e., any student number you have not collected), and then actually use this data. You would use the data to assess your current market share, to analyze variability, and identify any patterns of behavior that need to be taken into consideration. For example, are some staff consistently more successful than others at attracting student audiences, do some student cohorts have a greater proportion of attendees than others, does the time, day, and period in the semester have a significant influence on attendance, etc.? You would then use that information to target specific audiences with promotions, but you would not stop there. You would understand the level of variability in attendance, and you would identify a quantum that lies outside that level of variation as a clear indicator that the promotion had succeeded, and that any change that did occur was not just a random by product of the normal level of attendance variation for your current system. You would use the data you have collected on post promotion attendance to assess the promotion’s success, and you would all come together as a group to brainstorm the potential root causes for a promotion’s success or failure. You would then refine your promotions, and try again. You would always have measures in place to be able to determine whether something was successful, and in making that determination you would have a sound understanding of the normal variation in your data. There are many more things you could do with such an approach, but you should get the idea by now.

Whatever data you use to improve your performance and impact, the path to success is to take a more scientific approach to the use of data. What a lot of library data does at the moment is tell a “so what” story. This is not good enough, and you do not have to settle for this.

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