Have you ever looked at your Library's key performance indicators and said to yourself "so what!"? Have you found yourself making decisions in a void due to the lack of useful and easily accessible operational data? Have you ever worried that you are being left behind with the emergence of data analytics? Do you feel there are important stories in your operational data that need to be told, but you have no idea how to find these stories? If you answered yes to any of these questions, then this book is for you. How Libraries Should Manage Data provides detailed instructions on how to transform your operational data from a fog of disconnected, unreliable, and inaccessible information - into an exemplar of best practice data management. Like the human brain, most people are only using a very small fraction of the true potential of Excel. Learn how to tap into a greater proportion of Excel's hidden power, and in the process transform your operational data into actionable business intelligence.
- Recognize and overcome the social barriers to creating useful operational data
- Understand the potential value and pitfalls of operational data
- Learn how to structure your data to obtain useful information quickly and easily
- Create your own desktop library cube with step-by-step instructions, including DAX formulas
Lifting the fog
Many libraries struggle to obtain real value from the data they have, and this can result in a vicious cycle where they collect more data in the hope that the additional data will yield previously unobtainable insights. This chapter shows how to break out of this cycle by recognizing the emotions that underpin the decisions to continue to collect useless data, and advocating for an objective and simple set of tests to determine whether you should keep, change, or stop collecting specific datasets. This chapter outlines the importance of managing this change as a project, outlining key issues, considerations, roles and tasks.
Decision making; emotional decisions; useless data; project management; objective tests; data selection criteria
Imagine your house is in shambles, clothes piled up in random containers tucked away in dark corners, shoe boxes collecting dust balancing precariously on the top of wardrobes, and you are sick of the state of mess. There is a sensible way to go about cleaning, and an irrational way. It is quite possible that the reason you have a mess is because you have more than you need. That dress may have looked great on you in your early twenties, but it is never going to fit again. And those pair of shoes you wore to your first job have gone out of style along with other relics that should stay in the past, like mullet haircuts. So, if you are serious about cleaning, this means letting go of some things. Easy said, not so easy to do.
The same applies to data. You might have some wonderful time series data that makes a pretty chart, or you might have some stats that staff have been collecting since the Stone Age, or you might have some statistics to which staff feel emotionally attached. Just because you collected it in the past does not mean you should have ever collected it, or even if it once was a legitimate collection from a business perspective, it does not mean that it is now. Just like the messy house, a bloated collection of irrelevant data, is counterproductive. At the very best irrelevance distracts from the data that is useful. At worst, the good data gets tainted by the bad data, with staff becoming cynical or disconnected with data. If the numerical literacy at your workplace is low, then chances are this will provide comfortable validation for those staff that want nothing to do with numbers.
When you are cleaning your house, the last thing you should do is rush off and buy more storage, and perhaps buy more clothes and shoes. This would only make the mess worse. The same applies to data. If you are not happy with the state of affairs with your data, don't rush off and create new spreadsheets, sign up to new data vendors, or collect more data. Useful things become useless if they are hidden in a sea of rubbish. Indeed, this is meant to be one of the key value propositions of the library - they are a gateway to quality resources. Unfortunately, many professions don't practice what they preach. However, if you are worried about the long term viability of your business model, then you will need good data; and to get good data you need to be disciplined and focused.
What is the first sensible thing to do when cleaning your house? You decide on criteria for determining whether to keep something or not, then assess whether the things you have meet those criteria. You would at the very least have three piles, one pile for stuff to keep, one to give away, another to chuck. Your criteria might be simple - it might be I will keep it if it fits me, and I will allow myself to keep five items for sentimental value.
When you are cleaning your data it is essential that you determine the criteria before you start. Cleaning data can be an emotional exercise, and if you don't determine the criteria first, chances are you will inadvertently allow emotion to make the decisions. Of course, emotions for data are quite different to clothes. The emotional response might be something like:
1. I really don't know why we ever collected this, but what happens if I chuck it and we need it later
2. I don't understand this data, and I don't want to admit that, so let's just hang onto it
3. No one understands what we do, and if we don't collect that data people will think we are not important, or not busy
4. I don't know what I need to collect because I have no idea how to use the data, so let's just keep collecting as much as possible and hope that the avalanche of statistics will somehow morph into something useful
5. It is all too hard, it is easier just to keep collecting it, besides, it does not take that long
None of the above are sound business reasons for keeping data, and therefore if you allow these types of reasons to unconsciously determine your choices, then chances are you will dispose of nothing. Now, if you have been lucky, and all your data is good data, then well done, go and buy a lottery ticket while the gods are smiling on you. However, chances are you have some bad data, which means anything you do to try to improve the good data will deliver slim value.
You might be asking what do you mean by good data. The sole purpose of data is to prove a point to an unconvinced audience, or help you to make a decision. If your stakeholders are worried about the value your library is delivering to clients, then you will need data to help put their minds at ease. If you want to create a new service, but are not sure whether there is the demand for such a service, then you need data to help identify the business case. Good data allows an organization to thrive, it can be used to build strong positive perceptions about your library, it can be used to drive continuous improvement, and occasionally it can be used to assess business cases for new ventures. Bad data is the fog that obscures the use-value of good data.
So what sort of criteria should you use to help you with your data de-cluttering? Broadly speaking, you should be collecting data to answer one of four questions:
1. How much effort am I putting into producing a given service/product
2. What is the demand for services/products I am producing
3. What is the perceived value of my efforts to clients
4. What is the outcome of my efforts for clients
If your data does not answer any of the above questions, or answers them very poorly, then why keep collecting this data? The only possible valid answer is because you are required to collect that specific dataset by law. There are peak bodies that require the collection of statistics that are not of much use locally. The question you will need to answer is whether the cost of providing that data to the peak body is worth the goodwill. Here the cost is not just the time spent collecting and reporting on the data, but the contribution it makes to the fog of irrelevant information.
Some people may think these four questions are arbitrary, and certainly they are from the perspective of the terminology I used. There are a lot of key performance indicators out there, and a lot of tools for organizing them, such as the balanced scorecard. But focusing too much on the terminology at this stage runs the real risk of driving the data renewal program irretrievably into a semantic bog.
When I first started facilitating planning sessions at an academic library, I was amazed by how much energy staff were putting into crafting the right words for strategic goals. Eventually, I became a bit tired of the exercise and said quite loudly, "stop polishing stones." My strange statement stunned a few people into silence, and when everyone turned to look at me I continued:
Your strength is also your weakness. Everyone here is great at crafting sentences, they are like finely polished stones. But before we start polishing stones we need to make sure that we have the right stones to begin with. If you have chosen the wrong course of action for the library's strategic direction, then no amount of word smithing is going to help. In fact, it will hinder progress, because you will have these bright shiny stones that no one is going to want to let go of.
The same logic applies to your criteria. Focus on choosing the right criteria first, not crafting words first. The meaning of course needs to be clear, but you are not writing the constitution for a newly formed nation state. Lofty words and lengthy criteria will make the de-cluttering exercise more difficult, and wherever difficulty exists, emotion can sneak into the decision making process.
On the subject of decision making, one of the first things you will need to do before you starting de-cluttering is to identify who is authorized to make the decisions. One option is to do the following:
If the data is never used outside the team, then the team leader takes authority for disposal
If the data is never used outside the division, then the division manager makes the decision
All other decisions are made by the Director/Librarian
This is a nice way to devolve responsibility; however, if you have a strong culture of data hoarding, then some people are quite likely to make emotional decisions with their data, despite your best efforts at communicating the criteria. In this situation a simple audit of everything might be required, with more central decision making. Otherwise, you risk...