Monthly Archives: May 2021

The seven common biases connected to report adoption

In this article, I will summary seven common people biases that I observe when dealing with introducing a new reporting system.

Firstly, we need to understand why there is so much resistance in embracing a new? Most people are afraid that they don’t have adequate skills to use new tools, or they won’t easily understand the content of those tools. In some cases, the root of incomprehension is a lack of comprehensive information on why a new process is introduced.

Suppose we want to carry out a successful transition from the old process to a new one. In that case, it’s crucial to understand and address the specific fears and biases of our employees and manage those emotions. Change management strategies should be built around addressing people fears, untrust and incomprehension. Not without significance is a group of employees. Those strategies must differ when you approach data people and non-data people.

But before building strategies that help overcome those challenges, let’s name them.

The issue with data literacy

Data democratisation has enormous potential to change how we work and how we think. A great example is the work of Hans Rosling, a Swedish professor, who, thanks to exposing an audience to data, was able to influence their perspective about how positively the world change during the XX century. However, he didn’t leave the audience without assistance to understand and consume the data. When we give access to data for a wide range of people, we have to remember that among the employees’ population are individuals with various disabilities like dyslexia, dyscalculia, colour blindness or any other cognitive deficits. If we do not consider and address their limitation, our ambitious plan to empower each employee with data will fall.

The technology barriers

The biggest mistake is to assume that for other people, new technology is as intuitive and easy to assimilate as for us (it is a shared conviction among some people who already use some technology). However, the reality is quite different. People do not assimilate technology at the same pace. Some of them need more time and more assistance to become familiar and feel comfortable with new tools. Employees in our organisation have different educational background, age and skills that influence the understanding of any new technology.

The fear of an incomprehension

The result of neglecting issues with data literacy and technology barriers is that people will not use the new tool. They will not build a firm conviction that they can analyse data in a meaningful way and create business insights. For those who already work with data, BI platforms can be seen as data Eldorado. However, for non-data people, the same tool can turn into a nightmare. For data people, It is obvious that we live in the data ocean, and well-prepared data can enrich any process within an organisation or in private life. But not everyone has an analytical mind, and interpretation of data can be a challenge.

The fear of being redundant

Data people often see technology as a threat to current jobs. And this bias has strong evidence in factories and back-offices. If we automated the work of three people, who had done it for 80% of their time, they can feel anxious about their role in the organisation and be reluctant to use new tools. They can even present a negative attitude to a new device or process, explaining its unreliability. However, we need to remember that automation in the BI field is a blessing because it gives employees space to release their potential. In analytics, about 80% of the time is taken on data transformation jobs. By automating this part, people can have more time to explore data and create thoughtful insights and recommendations.

In here, we have reached another bias connected with someone’s skills. Even for people who have the potential, it can be hard to switch from one role to another in a short period. The organisations that appreciate human resources’ value are willing to offer upskilling training, which prepares for new positions, often more demanding. If well-designed and well-performed, this transition can be an excellent opportunity to grow, both for organisations and individual employees. More and more data requires more and more professionals who know how to take out the best insights and communicate them. In the future will be lower demand for data analyst but higher for data storytellers. That shift will be towards a better understanding of the business environment, business constraints, and connecting all those dots into one thoughtful piece of information.

The fear of being seen

BI platforms with updated on daily basis reports give enormous performance transparency in all fields. It enables monitoring people performance more outstanding than ever before. No one will hide. The dark side of this transparency can manifest itself in increasing stress experienced in the workplace by employees. People performance reports should be carefully designed to underpin organisational culture. Depending on the competitive culture or cooperative, the approach to data narration would differ. If you care to have a solid and effective team, your latest goal is to emphasise individual performance. Numbers in such case should reflect the team capabilities and contribution of individual members but not a comparison that can lead to competition within the team. Even in a competitive culture, data narration can have a positive or negative impact. When our objective is to attain goals in the longer time lag, we will desire to evoke positive employee motivation. Positive motivation is a reward for good performance, negative – pain avoidance. A good practice is to research before developing any reports to check what data visualisations bring what emotions and how they resonate with the audience. The red colour is a good example. For instance, at one company where I worked as a consultant, employees decided that red colour on reports negatively impacted their performance and well-being. The red colour was used to emphasise the sales budget realisation, which was under the threshold and shinned in red most of the time during the month. In such a case, another option is to highlight budget realisation and compare it with timeline, or simply change perspective and communicate current goal attainment (in green or blue).

The fear of responsibility

If you are thinking about creating an organisational culture based on individual employees responsibility and engagement, there is no better way than democratise data. The idea behind data democratisation is to democratise the decision-making process as well. However, on the other hand, as a side effect, we delegate more responsibility to the employees in lower positions. Some of them are already capable and just looking forward to that chance; some would be more reluctant. Nevertheless, the essential purpose of data democratisation is to equip employees with a tool that gives them authority to impact their performance significantly and, through this, on the entire company. Another aspect worth mention is reducing micromanagement that negatively influences employees’ efficiency, self-esteem and increases frustration. Having BI tools in place, employees are welcome to use them more frequently in self-management and drive their actions.

The issue with trust

Before we release a new BI platform, we need to be 100% sure of data accuracy. As an old proverb says, it is easy to lose credibility but extremely hard to regain. Data platforms are no exception in this case. They need to have a status of a single source of truth and be irrefutable. Otherwise, the audience won’t rely on provided data and go back to old, common practice. Establish a single source of truth and one dictionary for all measures is crucial. After the release of the BI platform, all other data resources need to be withdrawn for everyday use not to mislead users and not create a parallel reporting system with alternative truth. The massive challenge in this field is to convince people. In addition, shared access to guides and other materials which easily explain how the solution works, data is prepared, and measures are calculated can help people trust the new tool.

In the next article, I will go deeper into practical strategies that are available and easy to introduce in all types of organisations. So stay tuned.


Time orientation

Time orientation is crucial for the modern world to understand events and draw the correct conclusions.

The pre-industrial culture had not been so tided to time, and most often people perceived time in cycles as day-cycle or season-cycle. However, industrialization forced on us to create precise time systems and changed circularity to the linear phenomenon.

Currently, the majority of people live within time, and this time has for most of us one orientation from left to right and can not be reversible. It is one of human heuristics – mental shortcut, which helps us understand the world.

The example

Data visualizations best practices tell us to display time on the x-axis with left-right orientation (most of the culture except, e.g. Middle Eastern) and do not play with it especially when charts are going to be short displayed. In the end of August in Polish Public TV, a chart for unemployment rates was presented (see image below) with all possible misleading characteristics. I can not tell if it was intentional or not and politics are not the topic of this post, but let’s have a closer look at how this chart is designed and why it is designed wrong.

I have mentioned above that the human mind craves for mental shortcuts.  A quite possible scenario, in this case, can be that receiver reads only the first label for first bar from the left side on the x-axis and understands and remembers that on x-axis there are months of 2020 start from July (Lipiec 2020). The automated interpretation would be that two next bars represent data for two upcoming months, so August 2020 and September 2020. Of course, someone can raise a question in here “We don’t have data for September yet”, but my question is what a level of general data literacy and competency within society is? I am going even further and asking is it ethical to show data visualization for short time without a proper explanation of the graph? But it is a topic for another post. Going back to our example, the conclusion which can be seen is that the unemployment rate has decreased. Where is totally opposite.

However, let’s put ourselves in devil’s advocate shoes and consider, can we approach creatively presenting timeline or not? As I mentioned above, human eyes are used to interpret the timeline from left to right side. Due to that, it is good to keep that order. Sometimes we have a temptation to change it because for example, we would like to compare year over year change and we use last year data as a benchmark. However, that way of presenting data will not be intuitive for receivers. We must be very careful, when we are dealing with data associated with time.

How to fix it?

So how we can fix this visualisation?

First of all, let’s break years into two separate columns and give the time a proper order. Adding columns with years, we clearly indicate that we are dealing in here with two different time stamps. A title or a subtitle itself can help us emphasise that we are presenting a comparison between time points(July 2019 to July – June 2020), so don’t hesitate to include it. Also, I decluttered visualisation by removing background colour and 3d effects, which helps receivers focus only on data. To highlight the most current bar, I changed colour to orange.

All those changes enabled to present data story professionally and properly. Apart from all aesthetic aspects, data visualisation designers need to remember about ethics. The same as in other professions, data visualisation designers have their code of conduct.