Delivering bad news with data: How to turn frustration into motivation

What you’ll learn

This chapter shows what to do when data means bad news and why data can be a catalyst for improvement and progress. We offer practical communication strategies to convey bad data-based news so that you can avoid confusion, overcome resistance and turn frustration into motivation.

Data conversation

Alex looked at the report and instantly knew that it was bad news, very bad news. His team compiled an operations report and had discovered that there had been a continuously high reject rate in the production of an engine part. Alex knew that he had to inform the Executive Board including his boss, the Head of Operations, about this worrisome result. As he thought about how to present the findings, his hands became sweaty. Alex was a smart guy, with lots of experience in operations management. However, he had never considered himself a well-versed communicator and he often struggled to find the right words. At his last job, they had even told him that he had a ‘special talent’ to put things in an unintentionally rude and offensive manner. That had really struck him because he had never intended to be harsh to others or shame them with data. It was just that he had difficulties in communicating appropriately when delivering data-based insights.

As he sat there, staring at the wall of his office, he wondered how to deliver unpleasant data-based news, especially when it means criticising your own boss? Alex remembered a recommendation that he had recently read, saying that one should communicate in an authentic way. And this was exactly what he wanted to do. He asked his team to conduct further analyses on the dataset, then compiled a short PowerPoint presentation and went into the meeting without much further preparation.

‘Listen, I have to show you something’, Alex began. ‘My team found that we have continuous problems with the production of this engine part. For several weeks, there has been an unprecedented reject rate such that we have been wasting between 20% and 30% of the raw materials’, he continued. Alex then added: ‘We had a closer look at the data and noticed that the problems began after Matthew started running the operations unit. Moreover, the data reveal that most waste has been produced by Adriana’s and Lorenzo’s teams. Overall, the production needs to step up and do a better job.’

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Matthew, the Head of Operations, could not sit still anymore: ‘This analysis is crap and misleading. The high reject rate is simply due to the fact that we changed one of our suppliers and that we realised too late that the product quality does not meet our standards. This has absolutely nothing to do with me nor with Adriana’s or Lorenzo’s teams. If this is all you have to say, consider this meeting over’.

Alex stood there, looking puzzled. Why had Matthew reacted so thin-skinned? To better understand what happened, Alex decided to talk about this incident with his colleague, Dean. After hearing what had occurred, Dean shook his head and said: ‘Oh, boy, that was awkward. It is pretty obvious why Matthew became so furious’. Dean took a chair, sat down, and started to explain what went wrong . . .

Dean took a deep breath and described what went wrong when Alex had delivered the bad data news to the Executive Board.

‘Look, not only is it unpleasant to be confronted with bad news, also it is uncomfortable being blamed for negative outcomes’, he said. ‘You mentioned that the increased reject rates had been occurring since Matthew was in the position as Head of Operations and that most had been produced by Adriana’s and Lorenzo’s teams. You put the blame on them personally and made them the culprits. They lost face in front of all the other members of the Executive Board’, Dean argued. Alex frowned as he began to realise what he did. ‘If people feel that they lose face, they feel the urge to defend themselves. And as they concentrate on restoring their reputation, they are not open to rational and objective discussions anymore’, Dean added. ‘I see the problem, but what should I have said instead? I can’t sugarcoat the data’, Alex said in a desperate tone.

Dean reflected for a moment and then responded: ‘First of all, you should have been better prepared. It’s not enough to only deliver the bad news. You have to dig deeper and try to squeeze the reasons for the undesirable outcomes out of the data. What led to these problems? And second, make more informed choices regarding the level of analysis. The more fine-grained your analysis, the easier it might be to link the results to specific teams or people. Thus, always ask yourself in advance, whether your data allow you to identify groups or individuals. And if so, evaluate whether this is necessary and whether it provides any value added. And third, when criticising others, give recognition of their previous work and mention which other factors may have negatively influenced their performance. People are generally motivated to do a good job and they are sad or frustrated when things do not turn out as expected. So, acknowledge their effort when delivering bad news and shed some light on the context of the data’.

‘You’re right about these aspects. I think I have to apologise to Matthew, Adriana and Lorenzo for not being fair. I definitely have to work on my data communication skills’, Alex said.

After collecting and analysing data, you may have to present the results and conclusions to your colleagues and to your boss. But what do you do when you have bad news? Most people find it difficult to deliver bad news and thus avoid it as much as possible. This is intuitive and understandable because, generally, we do not like raining on other’s parade or being killjoys. Bad data-based news are any insights that are based on data and that are regarded as unpleasant or undesirable and that come with adverse consequences (see Figure 11.1). And what different kinds of bad data-based news are there? There are at least three common types of bad news from data: negative trends, goal failure and insufficient competitiveness.

Negative trends refer to all kinds of undesired or disadvantageous developments. Examples of negative trends include steady drops in satisfaction among your employees or decreasing demand for your products. Goal failure means that you failed to achieve your set objectives. For instance, you may have provided medical supplies to 5,000 refugees instead of 15,000 or you may have acquired 100 new clients instead of 500. Insufficient competitiveness occurs when you are lagging behind compared to others or when you are doing comparatively less well. An example is when your app is downloaded and used less frequently than the ones of your competitors.

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Although bad data-based news is something from which we might intuitively shy away, there is a great potential to it. When communicated in a clever and compelling way, bad data news can be a (positive!) game changer. Bad news means that things have to change. Such change can range from slight adaptations to fundamental transformations – from modifying existing processes to restructuring the entire business model. Change is a process. It is something that evolves. Decades of research on behaviour change have shown that people have to move through certain stages before they are ready to alter the status quo (Bünzli and Eppler, 2019; Prochaska et al., 2008; Prochaska and DiClemente, 1982). Guiding people through these stages requires resonating and well-planned communication. We will show you how to successfully lead difficult data dialogues for the better. So, don’t be afraid of delivering bad news when it’s backed by data. Harness its potential for improvement (Figure 11.2).

Let’s start with the three fundamental stages in people’s readiness to seize the potential of bad data-based news. The first stage, the comprehension stage, is about understanding the data and its negative implications. People need to see the problem, its root causes and the resulting implications crystal clear. They can only move on to the next stage if they thoroughly understand what kinds of data were collected, how the data were analysed and what the negative or worrisome findings are that derive from these data.

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The second stage, the acceptance stage, is about embracing the undesirable or unpleasant information that the data reveal. People have to accept what they have learned from the data analysis. The key is to make them understand that the situation is serious, but that the findings do not represent a personal threat. When people incorporate the need for change, they can progress to the next stage.

The third stage, the motivation stage, is about the willingness to act upon the data. The audience has to be motivated to use the bad news to generate new ideas and come up with improvements.

Every stage is associated with particular challenges that can be addressed and overcome with clever data communication strategies. The biggest threat in the comprehension stage is confusion (Table 11.1). Your communication efforts should be aimed at clarifying your data sources, analytical procedures and findings. Thus, take your audience by the hand and guide them through the process of how you got to your conclusions. Elucidate where your data came from, how you proceeded with the analysis and elaborate on what the results mean (remember that data do not speak for themselves, they need to be interpreted and contextualised).

When delivering bad news, presenters sometimes rush right into the unpleasant findings before properly building their credibility (and the needed context). This immediate deep dive could overwhelm the audience. Similarly, speak in plain language and avoid data or statistics jargon or acronyms as much as possible (as this may aggravate people’s resistance to the data even further). When people feel that you are speaking to them in riddles, they will not get the big picture of the bad news and fail to fully understand what you mean. Lastly, get to the point and be transparent about the extent and the severity of the bad news. This may be easier said than done because it involves telling your team or your boss straight away that things did not turn out as expected. It involves offering evidence in numbers and statistics that clearly demonstrate what is going wrong. Be aware that you do not resolve the problem by sugar-coating bad news (or tuning your findings to the expectations of the audience). It gives your audience a wrong impression and may lead them to not take the problem seriously enough.

Table 11.1 Key communicative challenge to the comprehension stage.

You may have successfully brought your audience to understand the bad data-based news. However, that does not necessarily mean that they will accept it. In fact, we often see that people exhibit severe resistance to bad news. When talking about resistance, we are referring to a key challenge of the acceptance stage. Overcoming resistance means getting people to accept the bad news that your data reveals.

There are several communication strategies that have shown to be beneficial to this endeavour (Table 11.2). First of all, pay attention to the way you frame your data interpretation. Often, we can express the same thing using a loss-framed perspective or a gain-framed perspective. A loss-framed perspective puts emphasis on negative aspects or disadvantages (e.g., ‘3 out of 10 customers would not recommend our product’), whereas a gain-framed perspective emphasises positive aspects or advantages (‘7 out of 10 customers would recommend our product’). Even when conveying bad news, try to weave in some gain-framed elements. The reason is simple: too much negativity is daunting and might give your audience the impression that there is no hope for improvement. This might create perceptions of not having an option and, as a result, might evoke resistance (Cho and Sands, 2011). Framing some findings in gain terms helps people see a silver lining on the horizon and makes it easier to accept that some things are not yet where they are supposed to be.

Second, mind your language when formulating recommendations. You spent a lot of time squeezing out insights from your data. It is legitimate that you are convinced of what you did and that you want to make sure everyone gets the implications that your data bear. However, as often in life, it is the tone that makes the music. Several decades of research have shown that controlling language triggers resistance, whereas autonomy-supporting language attenuates it (Rosenberg and Siegel, 2018; Steindl et al., 2015). What is meant by that? Controlling language includes terms such as ‘should’, ‘must’, ‘ought’. Autonomy-supporting language includes terms such as ‘consider’, ‘may’, ‘might’, ‘could’. Controlling language gives people the impression that they are being told what to do and that they cannot choose. This is counterproductive because it encourages (overt) rejection of your recommendation. This particularly applies to situations in which you confront people with bad news. No one likes to hear that they failed to meet the goals or that their project performed below expectations. This is even worse when you tell them in a dogmatic way how they should do things. Therefore, use autonomy-supporting language whenever you are presenting the implications of your data.

Table 11.2 Key communicative challenge to the acceptance stage.

Third, stay calm and professional. Most people feel uncomfortable when delivering bad news. To hide their unease, they sometimes make jokes about the things that went wrong. Using humour in such situations, however, often backfires. Imagine that someone tells you (in front of others) that the sales of your product crashed. The last thing you would want is that this person makes jokes about your problem. You would likely find it offensive, rude and unemphatic. When feeling offended or attacked, people frequently engage in counter-arguing. They question the reliability or validity of the data, the data analysis, or even the competence of the presenter. This is bad – not only for the presenter but also for the quality of the data discussion. Counter-arguing for the sake of defending oneself (or one’s ego) distracts attention from the actual problem and leads to unproductive and unsatisfying data discussions and eventually to bad decisions.

But what can you do if this does happen? Say, if someone questions what you did or what you know? Make clear that you are only the messenger and that you did not cause the problem (e.g., ‘John please consider that I am only the messenger . . . ’). You do not need to take the blame for things that are outside your responsibility. Nevertheless, be empathic and put yourself into the shoes of your audience. Being confronted with bad news can be overwhelming and can lead to people (occasionally) losing their composure. Think about how you would feel if you were given the same bad news. This might help you come up with the right words to calm down your counterpart and to reduce the emotions in the discussion. If someone is persistently criticising the data quality or the data analysis, it might be helpful to go back and give some details on your sampling and analysis again. To better prepare for criticism and counter-arguing, you may also want to write down a list with ‘nasty questions’ in advance of your presentation. Think of any kinds of tricky (or mean) questions that could be raised by the audience and develop answers to these questions. Common examples of nasty questions include: ‘Why didn’t you use approach X instead of Y to analyse the data?’ or ‘To what extent is the data biased and how might this affect the results’?

You may have brought people to accept your findings. However, that does not mean that they are willing to act upon the bad news. This brings us right to the key challenge of the action stage: that is, frustration. Overcoming frustration means to motivate people to take action and to improve things. In the following, we have synthesised several communication strategies that are helpful to battle inertia (Table 11.3). When talking about the next steps, give a proactive outlook instead of an avoidance-oriented outlook. A proactive, approach-oriented outlook focuses on possible positive outcomes (e.g., ‘Overall, adjusting our customer service will be key to retaining more customers’), whereas an avoidance-oriented outlook focuses on possible negative outcomes (e.g., ‘Overall, being vigilant of the customer service will be key to not lose more customers’). An approach-oriented outlook sparks motivation and optimism because it emphasises what people can gain by adjusting things. If available, use further data to underline your claim. Show your audience, for instance, how other units at your organisation managed to turn around a negative trend, how long it took them, or what measures were taken. Similarly, end on an optimistic note when finishing your presentation. Keep in mind that there is something called the ‘Recency Effect’, a cognitive bias where people tend to remember the most recently presented information best.¹ This means that things that you say at the end of your presentation will be the ones that stick in people’s minds. Thus, make sure that this is something positive and encouraging.

Table 11.3 Key communicative challenge to the motivation stage.

There is another powerful way to avoid frustration in your audience – something intuitive that is, however, often neglected: Give your audience the opportunity to ask questions, speak their minds and develop ideas. Overcoming frustration is not only a matter of preaching to the people and finding encouraging words, but also of giving them the space to become active and coming up with their own suggestions. Thus, allocate sufficient time for a Q&A session at the end or for an exchange of ideas.

Key take-aways

Most people feel stressed and uncomfortable when delivering bad news with data. However, this must not necessarily be the case as unpleasant data can be an important catalyst for improvement and progress. The art is to deliver such news in a way that the audience can understand the analysis, accepts its results, and feels motivated and empowered to take action. You may find the following questions helpful when preparing to deliver bad news with data.

1. 1. What kind of bad news do your data reveal: negative trends, goal failure or insufficient competitiveness?
2. 2.How do you ensure that your audience fully grasps the data? ➤ Avoid confusion (see page 205).
3. 3.What can you do to increase acceptance of your findings? ➤ Avoid resistance (see page 207).
4. 4.How do you motivate your audience to change things? ➤ Avoid frustration (see page 209).

Traps

Communication traps

Table 11.4 Dos and don’ts of communicating bad news with data.

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Further resources

To learn more about how to deliver bad news see:

https://www.forbes.com/sites/forbescommunicationscouncil/2019/04/03/13-ways-to-get-better-at-delivering-bad-news/?sh=29c7087865f0

https://www.youtube.com/watch?v=s76bX5ujl_4

Note

1. 1. https://dictionary.apa.org/recency-effect