Data Psychology

Is better data about data mining or people minding? As much as some people would like to believe that it is merely a technology problem - better data is, without a doubt, about much more than just a choice and implementation of hardware and software.

Better data is about quality, right? Well... what kind of quality?

Structural quality you say? well-formed? complete? concise? Interoperable? Well... does it matter? who cares? Oh yes - people care. Regulators, shareholders and let's not forget customers.

But hold on, this is true, but isn't data quality an operational thing? You know: timeliness, uniqueness, validity? True, but even if you ignore the structural quality people - what about the operators? People design, configure and maintain these technologies.

Ok, and what about functional quality of data? Things like fitness for use? availability? relevance? Those look at the applicability to business processes which people own and operate.

So yes, you can master your data, and you can interrogate the data to its tiniest bits, and you can draw beautiful models and flows. But, without spending a considerable amount of effort in understanding the psychology of data, in other words how the people around the data can and will behave around it - you are doomed from the start.

Let's take an analogy: You have been invited for the end of the year party. What will you wear?

Firstly you want to make a certain impression on the other attendees. You have an emotion attached to the event and this will influence your decision and behavior with regards to your dress code. This might be a more formal type of event where you want to impress people, or it might be casual, seeing old friends, where you want and intent to relax and enjoy quality time with these people. Or perhaps you are forced to be with people you do not really enjoy spending time with.

Secondly, the party may have a theme, or the crowd might have specific ethnic or religious views, which you need to consider. Perhaps you need to dress conservatively, or emphasize a certain identity? This type of environment specification will also influence your choice of clothing.

Finally, we get to the third and last factor. Resources. How much time do you have to find something to wear? Do you have money to buy something new? These constraints will ultimately influence your behavior further in terms of getting yourself ready for the end of the year party.

Going back to data management, people's behavior to come to the party (i.e. participate in a data strategy), will be influenced by the same exact factors. No, I don't mean what they will wear to your data management meetings, but rather the level and ways in which they might commit their participation.

People will always consider first their personal perspective in terms of their job, their personal and professional style and the impact of your data management efforts on their own world. Only then will they consider what actions would be acceptable, given their operating environment, for example how their business model, customers and partners might respond to any changes. Then lastly they will consider the resources they have available and the level of commitment they are able to provide you.

A defect in a record detail might frustrate or annoy the user, but the provider might have no interest in improving the data. A technician running a system may have a limited perspective and prescriptive operating constraints with no flexibility (or awareness) to measure or mitigate information risks. There maybe shortage of resources, under skilled workers or simply unmotivated employees - the scenarios are endless...

So what does this mean? I have given you a few examples and specific dimensions to consider in your approach to data management. You need to consider the technical aspects of system design and integration, but also take a closer look at the human dynamics which are a real success factor on your data management strategy.

Ask yourself this question: Is your team and strategy sensitive enough to consider the psychology of the data you are trying to manage? ... It might seem common sense, but I think in most cases - the answer is no.

The Theory of Data Mind

People have different metadata for their lives. Not only from a fundamental semantic perspective (I.e. language), but also at a more complex levels ranging from domain specific formally obtained (I.e. education) to personal semantics built over time (I.e. life experiences).

Now when you were a child, your caregivers thought you the theory of mind (hopefully). This is the ability to understand that other people's perspective could be, and actually is, in most situations - different from yours. In professional terms we call the application of this theory - stakeholder management. To succeed in your tasks you need to satisfy the requests of the people who will judge its completion. It might be yourself, your manager, your client and/or other authorities. In effect, to be successful you need to convince your stakeholders that you have completed the task to their satisfaction.

In data management we talk a lot about context. The semantics, quality, responsibilities and impact relating to data all mean different things to different people. This is because they have different responsibilities, life experiences and education. Learning to deal with these differences is an essential to the success of implementing data management.

Interestingly enough, the practice of data architecture, which forms part of the discipline of data management, is in itself entangled by semantics and theory of mind issues. Too often people rely on a limited set of theories and a limited set of stakeholders’ perspectives to both define and implement data management.

What is the difference between data and information architecture? Who is responsible to the data profiling? What is the best reference framework to use in implementing data management? Most likely, everyone reading this post has their own opinion. As long as we continue to support independent organization – there is nothing wrong with adopting different sets of tools and practices. However, it does make it more challenging (and costly) to create a sustainable data strategy. Imagine the rotation of responsibilities between existing employees and new employees. The more exceptional your approach – the more difficult it would be to introduce it to people and to integrate it to existing processes.

The reality of it all is that in practical terms, companies will continue to implement different strategies towards data management. Some of the variations will originate from the different characteristics and requirements of each organization, while other differences will be due to the lack of consensus on a single “golden” reference framework to implement data management. Companies will only align their approach when there is a real business need to do so.

So what all of this mean to you, as someone concerned with the well-being of the data in your organization? When implementing data management limit your scope (but keep a strategic mind).  Keep theoretical conversations outside the formal data management conversations. The leaner and meaner (precise and useful) your strategy is – the more likely you are to succeed.

Just be warned: you will get nowhere if you forget to apply the theory mind to the application of your data strategy.