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Make more impact by empowering your one-to-one meetings with data.

COVID-19 was a game changer regarding our ways of working. Many companies were forced to change a typical on-site work style for remote work. That shift had pros and cons like everything does. On the pros surveys reveal better work-life balance, commuting time savings, or performance increase. On the other hand, managers notice risks in a higher rate of employees leaving. One of the biggest cons of remote work is that employees lose attachment with the company. The main reason is a lack of social interaction with peers and involvement in creating specific organisational culture. But the second is weak identification with the brand that often relates to the office and office events. Managers are brainstorming how to bring back people again to the offices and gain their loyalty, but in the post-pandemic world, it is not so obvious how to achieve it. Rules changed and nothing remains the same as it was.

I believe that everyone needs to feel purpose in life and feel that his work is meaningful. For me understanding how I contribute to the overall strategy, goals or company’s vision is essential. Many times, we lack those connections because of poor communication from the management side and a vague understanding of our role in the entire organization. Fortunately, we have a bunch of communication tools that can be used to improve mutual understanding and keep employees satisfied with their positions.

One of those I find helpful is one-to-one conversations. The one-to-one conversation has great potential in tracking performance, and most of the time they are only used for that. But what is much more important is having a deep and honest discussion with employees about their thoughts, sentiments, and aspirations. That knowledge gives managers the opportunity to react fast when a loss in interest is observed. However, be honest with yourself, how often do you have a feeling that your one-to-ones are not effective as they could be? What are they still missing?

From my long record, I rarely recall that those discussions were supported by some good information. In the majority, discussions were driven by opinions rather than facts. Wouldn’t be great to have evidence for our gut feelings? That precious time is too often wasted simply because companies don’t provide adequate tools to make those meetings more valuable and beneficial for organisations, managers, and employees. Writing “tools”, means collecting, analysing, transforming, and presenting relevant data to make sure that people are talking about facts, and not opinions. And yes, nothing stands in the way to use data for one-to-one meetings.

Of course, the selection of data and KPIs will differ across industries, businesses, and roles. However, some of those remain the same. The biggest challenge is asking the right questions and finding data that respond to them. The great starting point in the journey of creating KPIs that give you meaningful data-based one-to-one conversations are:

  1. Company strategy & goals,
  2. And the job description.

Company strategy &goals

As I wrote above, people like to feel purpose and connection. Why not use a narrative from the big picture down to the bottom and show employees how does he or she participate in the company’s growth? The more tangible connections between the employee’s daily work and the company’s performance you can find, the higher satisfaction the employee can have. Most organizations cascade down their goals. Thanks to that, we can simply provide proper KPIs and data visualizations to present departments, teams, or individuals’ contributions.

So, before the next one-to-one, if you do not do it already, would be good to talk with the business intelligence team, the sales team, or the finance team to get some shareable data about the business growth and current progress toward goals and the contribution share of your team.

Job description

The job description includes all expectations toward a specific role that can be converted into questions tracked by data. Typically, the job description has two parts that we can use for our purpose. First are responsibilities, second qualifications and skills. Responsibilities can shape our questions about current performance toward goals, finding challenges and their proper solutions or give us a clue on how to prioritise hot issues. Qualifications and skills are a great introduction to talk about employee directions of development, their ambitions and future career paths.

Business case

As a business case, I’ll use the Product Owner role. Depending on the industry’s and an organization’s characteristics main responsibilities, qualifications and skills can differ. However, for the purpose of this post, I’m picking those:

  • Develops, owns, and executes product roadmap.
  • Prioritizes and maintains the sprint backlog for assigned products, balancing the requirements of stakeholders.
  • Translates product roadmap features into well-defined product requirements including features, user stories, and acceptance test criteria.

Expectations reflected in data

The product roadmap is one of the key drivers of success in delivering products. Without a strong and clear vision of what the product is and which characteristics and functionalities it has, it would be hard to develop anything. As a Product Owner, you should often review and update the roadmap to make sure that the vision of the product still reflects the market demands. On the other hand, the product roadmap is a base for the product backlog that consists of features or /and user stories that workload estimation gives the Product Owner a feeling about timely delivery. So, what kind of KPIs should we track to make sure that the roadmap is still valid?

Do all milestones are on the product roadmap?

The product roadmap usually includes milestones or bigger chunks that are broken down into smaller pieces like features and user stories. Tracking something that is not visible is a complicated task. Having one big picture of what is planned gives you the opportunity for proactive conversation. Having the possibility to see all relevant tasks for each milestone makes you ensure that you didn’t forget anything highly important.

Does the product backlog cover the product roadmap?

The first measure that could be interesting to track is the number of tasks under each milestone. The alert could be set up for those milestones without any created tasks. If you have the possibility to track the progress of the task, it gives you a feeling that pace of work is aligned with assumptions or is it faster or slower. You can then discuss options.

Do we have enough resources to deliver the agreed functionalities on time?

Time and money are always tied together. Looking at the roadmap we need to guess somehow the amount of work that is needed for development. For that, we can use story points, or man-days, or any other measure that allows us to compare team capabilities with the required workload. As a result, we can have a positive or negative gap. We wouldn’t trouble ourselves too much as long as we had a positive gap, but the questions would arise with a negative one. Should we narrow the scope or maybe find other people to help us?

Do features/user stories well-prepared for developers?

This question can reveal if tasks for developers are ready for development, or if some issues must be clarified still. We can use here RAG (Red for not ready, Amber for those in progress, Green for those that are ready) approach that gives us the status of tasks’ readiness. This status review opens a discussion about issues and challenges on a very low level that in the end can have a tremendous effect on the entire product development. To create RAG status, think about the most important entries, or fields on your feature/user story template. Then you can use a simple sum or a weighted one to calculate the indicator. Add conditions to differentiate between red, amber, and green (or not ready, in progress, ready). Now you have KPI to see which task needs more of your attention or has some issues to address.

To track these data, you do not even need fancy tools. The Excel spreadsheet will work perfectly. Of course, if you have the possibility to use more advanced business intelligence tools, please do not hesitate 😊

Addressing aspirations and ambitions

Most people I have known have their own aspirations and desires regarding professional and private life. Most of them if they cannot fulfil them in the current workplace are starting to look around for more favourable conditions. That is why the manager should remember to leave enough space for one-to-one conversations for discussing topics regarding employee growth. But again, the discussion is an exchange of opinions. Can we find some data to visualise how much time and effort is spent on learning and mastering skills activities?

More and more companies offer their employees learning platforms just to name a few Udemy, Coursera, and EDX. They are perceived as tremendous benefits by employees but only when they are allowed to allocate some time for learning. In the interest of any organization should be staff development. It has so many positive aspects for both sides, the employer, and the employees. I have an experience among organizations which had entirely different approaches to peoples’ growth. Some of them didn’t care at all about these needs, some of them gave the opportunity to learn but after working hours, some of them understood it as an investment and some of them required upskilling but without providing any courses or giving room for learning. But it totally different topic.

My point is that if you have such platforms in your organizations, maybe you can leverage them for:

  • Verify together with your subordinate which courses would be relevant for mastering skills required in her/his position,
  • Prepare together learning path,
  • Agree on timelines,
  • Allocate time per day/week/month for learning.

Most learning platforms share data or even provide built-in reports about users’ activity like a list of chosen training, amount of time spent in the application and on training, or progress on lecturer or practical activities. Isn’t it a great mine of information? Armed with such knowledge we can bring to the table tangible insights and have a proper conversation about employee growth. What we can definitely review in the first place is whether a person has the opportunity to use the dedicated time for learning or is snowed under with daily tasks. Or the exact opposite if you are sure that a person is not overloaded with work why she or he doesn’t take classes as is agreed? Another point for discussion can be reviewing new learnings and figuring out how this fresh knowledge can be applied to business, or if the subject is still relevant or should be changed. As you can see having those data we can start even think more strategically about the development of teams, departments, and entire organizations.

The above examples are only a small sample of enriching one of the processes within the organization. The huge challenge in making organizations data-driven is to design relative key performance indicators and create a habit of using them unconsciously by people. The main strategy to achieve that is simply to weave data into almost every process. The result can be that employees won’t think about data as something separately but as an integral step for achieving their goals. Establishing that common culture in the organization will support gaining market advantage like never before.

Featured post

Four Levels of Data Storytelling – Where Do You Perform?

In this post, you will find out the four crucial skills to become a data storytelling master and why you should improve them.

Firstly, I have good news for you. Everyone can be a data storyteller and possess the required skills on a “good enough” level. Of course, each of us starts from a different position, and the time necessary to reach a “good enough” level will differ. There is scientific proof that you need about 10 000 hrs to be professional in any picked field, but only 20 hrs to have a basic knowledge of a subject. There are four fundamental skills that made you a data storyteller.:

  1. Analytical skills
  2. Data visualization skills
  3. Communication skills
  4. Subject comprehension

Analytical skills

This skill is a basic of basics. Without understanding numbers and reading them, you cannot adequately prepare a story about them. Even when you are not a “data person” – someone who already has had the skill to transform and interpret massive data sets, you still can learn it.

Our brain is divided into two halves – left and right. The left half is responsible for analytical, logical and sequential thinking. In this part of the brain, the centre of language is located. The right half gives us the ability to perceive in a non-verbal way: see objects in space, compare similarities, have intuition, have a holistic view of something. Most people experience domination of one of the halves. However, it does not determine that you are an artist or an accountant. If all humans have both halves, all of us can analyse and interpret data. Naturally, some of us are more gifted than others, but I would be far away from the opinion that you cannot learn analytics unless you suffer from solid dyscalculia.

But where to start your journey with data analytics if you do not have previous experience?

Foremost, understanding descriptive statistics is a game-changer. Descriptive statistics are methods for organizing and summarizing information. Having those statistics in place, we can start asking the right questions that help us reveal some insights. Mostly, we do analytics to see some trends, picks and falls, a contribution of factors or distribution of one of the characteristics within the population.

Data Visualization skills

Ok. We gathered all required data, organized and summarized them using descriptive statistics. But how make them readable for others?

In lots of companies still, a primary tool for performance reporting is MS Excel. And still, in those companies, the primary manner to present numbers is an excel table. There is nothing wrong with using tables, and sometimes they are even the best way to communicate results. However, we have much more tools to select to communicate numbers effectively. There is quite an impressive range of available data visualizations in any common software like Excel, Power BI or Tableau, just to name a few. Visualizing numbers is a skill like any other. You can learn it and master it.

Nowadays, this skill is more important than ever when we are submerged in the data ocean. Data visualizations are often the only way to make sense of data, find patterns and understand the surrounding world. Data visualization utilize human perception to communicate and receive data. If we do it without proper diligence and mindfulness, we can mislead our recipients, and as consequences, they will draw wrong conclusions. The worst-case scenario would be misleading the audience on purpose. Regardless of designer intentions, it is an ignorance of using and presenting data in an unethical way. There is plenty of sources that provide rules and best practices on how to use data visualizations correctly. So do not miss this opportunity and earn credibility.

Communication skills

So, two first steps in a process have been already done. You found interesting patterns and insights in the data and prepared their visual representation to make it visible to others. However, how to convey the message?

As a species, we are designed to communicate complex ideas and theories because we have speech apparatus, unlike any other animal. And vocal communication is a basic one for us. Thanks to our ability to pass complex ideas and theories, we have built an advanced civilization. But even when we speak the same language, we often cannot efficiently articulate our thoughts, and the receiver can misinterpret our message.

From a data storytelling perspective, there are two crucial components of communication. The first one is to use language adjusted to the audience. It is easy to overwhelm the audience with technical jargon, lose their attention and, in the end, lose their interest in the subject. The second one is the ability to make simple explanations. There is good exercise, at least when you have kids. When you explain your thoughts in a way that a seven-year-old kid can understand, you are the master of communication. To achieve it, try to use as many as possible comparisons, examples and metaphors from your audience experiences.

Subject comprehension

On top of the three essential skills, there is one more. I have already emphasized several times how crucial subject knowledge is. As Steven Covey said, “firstly understand to be understood”. You will not be a convincing storyteller without knowledge of what answers your audience is looking for. There is a simple rule: people always are interested in their business and problems, not yours. So, when you want to persuade them, you must present benefits or threats for them. From my experience data analysts are overloaded with ETL jobs and do not have enough room to talk with business people about business pain points and challenges. Those conversations would significantly enhance provided information. Data without context and understanding what is behind the scenes are useless.

Apart from mastering analytical, data visualisation, and communication skills, try to become a true partner for the business that you support. Build strong relationships with your internal or external customers and listen to them actively. Most people are willing to talk if you are pleased to listen. There is no better source of knowledge than subject matter experts. With those competencies, your ability to have a real impact within the organisation and building your personal brand will grow.

From Beginner to Master of Storytelling

In each discipline, there are levels of mastery. There is no difference with data storytelling. Check out where are you right now and what your aspirations are.

Beginner

This is entry-level. You even do not know that there is something like data storytelling and you can learn it to enhance your data analysis. I often meet beginners as young people who have just started their career. Their heads are plenty of theory, but they lack practice. In the first place, Beginners should improve business knowledge to prepare better and relevant data analysis. In most organizations, there is plenty of internal training and materials that bring closer inner business processes, rules and characteristics.

Recruit

I would say that those are mostly data analysts with excellent analytical skills and data visualization skills who do beautiful data visualizations that often are totally useless. The pitfall here is that when you are experienced in one very narrow specialization, you can have the delusion that you know better than others what they need and how to present it. However, they produce products for their customers and should listen to their voice. Maybe your customer does not know how to analyse data but for sure know what questions are interesting and show you in which direction data analyses should go. Recruits are typical data people, and they put too much focus on technical aspects, and they too often use very technical language when they communicate with non-technical people. They should focus more on the business side of analysis and less on the analysis itself.

Leader

As you can see on the matrix above, I valuate communication skills and business acumen more than data visualization skills in data storytelling. People who already know what the pain points of business are and can draw the audience attention do not need to know advanced data visualisation techniques to impact. However, there is a potential risk to easily mislead the audience if someone uses data without proper knowledge about basic best practices of data visualization. As I have mentioned earlier, data visualization techniques are based on human perception, which is a very fragile cognitive apparatus. Leaders have a special mission in spreading data culture across organizations because they feel comfortable with data, know how to use and present them, and, thanks to their position, can make or influence data-driven decisions.

Master

Masters have proficiency in all four skills. What is more, thanks to the linkage of business knowledge and analytical skills they are true advisors, who can set directions of future growth.

The doom of pre-defined dashboards. True or false?

A few days ago, I read an article1 about trends for 2022 in data analytics. One of the opinions paid my attention more than the rest. The thesis was that in 2022 we can observe “the death of predefined dashboards” which sounds odd to me.

Maybe it is only some kind of over-interpretation of what is happening in the industries and an attempt to call it controversially. Nevertheless, decision-makers can take it for granted and start a revolution in organisations harming analytical processes, workflows and widely understood data culture.

Let me touch more deeply on why I bare such an opinion.

The case with data literacy

I would love to see legions of employees who are able to read, interpret and work with data fluently at every level of the company’s hierarchy. But we are not there yet, as all surveys of all consulting companies show us.

For years we have been observing how companies have been putting a large focus on data democratization. The main evidence of that is an evolution towards a data-as-a-service direction by using cloud-based solutions to empower different users in data analytics. However, most of that significant potential can be easily lost just because of the immaturity of the organization’s data culture and the data literacy level of each, single employee.

Frankly speaking, too much focus is on the technology side and too less on people. Companies still mainly invest in training improving technical skills or ability to use specific tools.  Training which teaches how to use data for a specific purpose is in minority, even on the market is hard to find such offers. We must remember that employees have different backgrounds and different skills. Some of them would always need assistance in data analytics, just because their core skills are allocated somewhere else and there, they bring business value. We shouldn’t require them to waste their time learning how to work with data, while they should master other skills.

Challenge with an approach data as a product

The next point to cover is how those organisations are advanced in digital transformation. Before introducing a new strategy, some basics must be prepared. Many companies would like to be data-driven, however still suffer from a lack of integrated, automated, and accessible databases that provide high-quality data. And it is not a completed wish list.

Efficient and business valuable data sets serve specific business areas. In most cases, it means that different business areas have data prepared differently including data aggregation, hierarchy, and perspectives. The huge challenge for organizations is to provide an environment, structure, and infrastructure to approach data as a product. It requires investment in hiring an adequate number of professionals and changes in existing processes and technology. Apart from that, DaaP is still a fresh concept and companies need time to get familiar with it and step in on this journey.

Underdevelopment of tech-savvy

I’m writing above about too much focusing on tech training. However, some companies don’t have any vision of how to support their employees in their tech-savvy journey while still expecting results.

I was the victim of such an approach gaining access to the tools without any training and vision of employee development and setting a clear learning path. Worse, I was required to figure out how to upskill myself. That was a horrible experience, both for the employee and the organization that ends up in frustration and lack of results.

Mature organizations employ professionals who take care of the technological development of employees in accordance with the company’s long-term strategy and vision. They make sure that the skill set of employees can shift the company from point A to point B. Without them or similar roles, no major changes can take place.

The hell of multi-sources of truth

If you are a fan of Marvel like me, you know what chaos can be brought by having multi universes. The same risk can be a case when we allow separate business units to use databases without supervision. Business units may report the same metrics differently only because they understand or define them differently. From the inside, we can observe that data retrieval is processed in a different manner.

This generates a bunch of problems. Especially in proofing whose numbers are correct ones, and this requires additional time and resources that could be spent on more valuable tasks. Not to mention ruining trust and mutual relations between departments and employees.

As a key conclusion, I would say that giving employees the freedom to create their own dashboard places a huge responsibility on their shoulders and requires them to have various sets of technical skills. Such a strategy may be similar to throwing the baby out with the bathwater if companies do not invest time and money in ensuring that their employees acquire the skills they need, are equipped with the right tools and data sets can be used without worrying about the disinformation.

  1. https://www.geeksforgeeks.org/top-10-data-analytics-trends-for-2022/

How to present numbers involved in people tragedies?

Every day, we have been bombarded with news about people cruelty toward other people or animals and natural and unnatural disasters that result in many deaths. It is now even doubled because of COVID-19 and its death toll. You could say that there is nothing spectacular in it. From the first time man set foot on Earth: Famine, Plague and War are our inseparable companions, and in the era when we plan to conquer the Universe, they are still not defeated.

However, most of these terrifying scenes are somewhere long distance from our safe and cosy homes. In addition, we are overwhelmed by violence presented in mass media. That gives us the impression that those situations are unrealistic and abstract. We hardly attach them to real people, victims and it is going to be even worse as we learnt from the latest studies about decreasing empathy.

For instance, I experience the same feeling of indifference when looking at COVID-19 statistics. These are ONLY numbers. Dehumanized numbers like production series or kilometres run in your tracking app. And that scares me a lot.

A situation when people (or any other living creature) are presented as a sequence of numbers scares me. When we use some abstract forms to identify persons, there is a danger that we will perceive them as objects and not as subjects. I witnessed behaviours involving the use of employee numbers in internal communication and it was a part of the culture. For me, this approach detached living people from their formal functions and roles. Roles become impersonal. There are no people, there are only cogs in the machine or resources to use and to get rid of when used.

So how to present numbers and communicate real people tragedies?

Language

Another thing is the language used to describe victims. Many times, the word “case”, “deaths” or “fatal accident” replaces words “wounded people”, “died people”, “victims”. Especially in medical statistics like presented in Figure 1 number of people who died and recovered from COVID-19 (statistics for a particular point in time).

Using abstract forms do not help in building vivid pictures in the mind of our audience of happy people, who recovered from the awful disease and went back to their families or plunged in grief over the loss of their loved ones. And this is what we would like to achieve – move their imagination to evoke their feelings.

Which of those subtitles in Figure 1 are more dramatic?

Figure 1

Numbers

People, in general, have problems with understanding big numbers, statistics and abstract visual forms presenting the information. The numbers in Figure 1 are so enormous that is hard to imagine them. To convey information effectively we must downsize it and chunk to the well-known, familiar, and easy to interpret elements.

In Figure 2 we can see the percentage of how many people died vs recovered from COVID -19. I used the abstract visual form to present information – pie chart and impersonal, medical description – death rate, closed cases. Nothing about victims.

Figure 2

How can we interpret this picture? If we are good at maths and understand the concept behind percentages, we can have the impression that 2% is a quite low chance to die of COVID-19 and there is no big deal (I won’t vaccinate myself! It’s a mystification to implant a chip on me!). And again, using the word doesn’t help us understand a real, current threat. “Death” for most of us is a metaphysical conception that lies somewhere in the far distant future.

Iconographic

To downsize information and present it in a more readable format, we can use graphical representation, small objects that symbolize humans. This approach lets an audience understand the range of coronavirus death toll because the big number was chunked into small pieces (1 out of 50). Number 50 is much closer to our imagination than 5 613 594. Using human symbols I emphasized that numbers are related to people.

Do you feel now more or less certain that COVID-19 is not a big deal?

Figure 3

Time

We can use the time to strengthen our message significantly when we embedded our audience into the present moment and convert statistics into occurrences. With this tactic, we can easily emphasise how human life is fragile because when you are reading this text every nine-second someone passes away because of the corona virus (again I used a 2% death rate). You can use animated gifs to be more dramatic.

How do you feel now with this knowledge?

Figure 4

I do not say that standard data visualisations are bad, and we should not use numbers or statistics. I just want to challenge anyone who communicates information to a wide audience to tailor better channels to make sure that a message gets properly understood, and people will start looking again at those who suffer… with appropriate respect.

Map your maps.

During the holidays season, I’m having more time to catch up watching movies. On that long list a film “Another round” can be found. In a nutshell, the plot is about four friends and their unexpected alcohol experiment. Everything is done in the spirit of science, of course. In truth, this dark comedy-drama touches on a very sensitive social problem that affects many people around the world.

I’m wondering how Poland looks compared to other European countries and if Poles on average drink more or less in comparison to Danes? According to WHO (World Health Organization) data from 2018 average Pole drinks 11.71 pure alcohol and Dane 10.26 (15+ years). The difference is 1.45. Is Poland near or far from Denmark? Depending on the colour palette and applied scale we can perceive it differently, and consequently, convey different stories or draw misleading conclusions.

5 stepped colour

I used Tableau Public to visualize data. This visualization is automatically chosen by Tableau. According to the visualization, Poles are not in the lead for European countries and Danes are somewhere in the middle of the scale.

3 stepped colour

But wait a minute. What a shame! Poles are heavy drinkers. Now I can see it clearly.

7 stepped colour

OMG… how much beer average Czech had to drink to win this competition? When it comes to Poland, it is not so bad. Poland is near the middle of the range.

Reversed 3 stepped colour

Hm… I’m a little bit confused. I have the impression that Poles don’t avoid occasions to celebrate the fragility of life, but now I can see is opposite. (Who would check legend description? Waist of time, data visualizations are intuitive!)

Attention: Remember in our culture stronger colour saturation means increased occurrence of the phenomenon.

As we can see, each of the four above examples depicts the same information differently, and that difference can be significant.

Maps are commonly used in public media and people like them. The same is in the business world. However, knowing it from experience, it is very easy to manipulate information presented on maps. Before you publish or share your map ask yourself:

  • Does scale represent the statistic bins,
  • Are colours adjusted to the topic,
  • Is reverse scale justified?

Data source: https://data.worldbank.org/indicator/SH.ALC.PCAP.MA.LI?view=map

Develop these four skills to be more successful in any domain.

It is a chilly morning. I stand in the middle of the kitchen and look at my lovely daughter after our regular morning battle to get her ready for school. Apart from all rage that she carries right now inside, she is like a delicate flower torn by the wind. I ask myself where is the point to force her to get up so early and expose her to all these frustrations that will come for sure today when she tries to remember all useless knowledge. The Polish education system sucks.

My daughter, as the next generation of humans, will face many new challenges in the near future. Climate crises, energy crises, increasing inequality, overpopulation, the collapse of democratic rules … just to name a few. The current education system does not prepare our children for any of the challenges of the 21st century.

Experts agree that for our kids to be able to adapt to the new environment and face what the future will bring, they must master four basic human skills. They are called 4C’s for the 21st century: Critical Thinking, Collaboration, Communication, Creativity. And what is more! According to the experts’, 4C’s are the cornerstone skills learners of all ages need to be successful in life[1].

What the hell, do these 4C’s have in common with data storytelling?! You would ask. Well, I got an idea for this post asking myself how can I support my daughter in developing 4C’s. Then I asked myself if I was using 4C’s and how beneficial it would be.

4C’s for Future, 4C’s for Today

If you’re wondering where the future starts, the simple answer is today. It doesn’t matter how old are you and what challenges you face in your daily life; these four skills definitely help you achieve more in less time.

Critical Thinking – foremostly

In the past century, people have struggled with collecting and obtaining data for their studies. We are now reaching the point where anyone with access to the web has access to a large amount of data and can do their own analysis. Data democratization, like everything else, has two sides of a coin. Unfortunately, the dark side of the common usage of data is to mislead people and create fake insights.

I love the TV series “Ancient Aliens” but the level at which they treat and interpret scientific facts is very innovative – gently speaking. For me, it is a piece of good entertainment, but we can imagine how that trivial approach to science and what is worse mass-broadcasting this approach, can implicate damage in some people understanding of ancient history without questioning that “revealed truth”.

Critical thinking has its roots in curiosity. Before you judge or draw a conclusion based on information, you should dig deeper to make sure that your conclusion is not skewed by shallow analysis or dubious data. Similarly, to “Ancient Aliens” you can create the most breath-taking story about your discoveries, but where is a meaningful value from this fairy tale?

Critical thinking is a habit of questioning others and yourself and the good news is that everyone can learn it. To develop this habit:

1.Ask the right questions and validate your own logic.

“There are no stupid questions!”. I hope that you’ve heard that many times. If you haven’t – change organization! Asking questions is the simplest and the best way to verify your or others reasoning. Use the below questions to warm up your critical thinking:
“Where data came from? Do I trust data sources?”
“What is data quality? Are there any missing entries?”
“Does the data sample is big enough? Does it present only a small part of the bigger picture?”
“Do all factors are included in the analysis?”
“What business questions does this analysis cover?”
“Do I not overcomplicate things?”

2. Deal with your (or others) biases. Remember we too often look for evidence that supports our prior beliefs.

All of us have some kind of the burden of biases. It strongly affects how our brain interprets information and draws conclusions. Studies show that we have a natural tendency for ensuring that we already believe. That tendency can be very harmful to the recommendations which we provide. To understand better how our biases play tricks on us read the book “Mindware. Tools for smart thinking” Richard E. Nisbett.

3. Take time to evaluate the topic from different sides and seek diversity.

Most of the time we are in rush and that hurt our reasoning and the quality of work we deliver. So, hold your horses and invest time in finding out other people opinions. One question about “What causes revenue decline” can have multiple answers depending on the point of view. These points of view can be very valuable and let you create a story with a wider spectrum.

Collaboration

The self-made man is a myth. No one is one hundred percent accountable for his/her success or failure. We are the result of many factors like genetics heritage, family relations, culture constraints, environmental influences, and life experiences. All together constantly have a huge impact on how we perceive ourselves and make sense of what surrounds us.

Have you read a biography of Bill Gates? Bill Gates maybe wouldn’t be so successful in his field without a few coincidences like exposure to the computers in the earlies ’70s as a teenager (what kid had that opportunity!) and mother who served in IBM board and helped in securing his first big deal with IBM. Of course, he used those opportunities very well, but would he have been the same Bill Gates without those chances?

We as humans operate in tribes. Without other members, we wouldn’t survive. If you want to be successful in your life collaborate with other people and leverage their skills and knowledge, especially because domain specialization is so deep that it is hard to be a Leonardo da Vinci in the 21st century.

Some people find it easier to collaborate with others, others find it harder. And again, self-discipline and practice can help you develop habits:

1. Invite subject matter experts to discuss and review your data, analysis outcomes, recommendations. They can bring a new fresh outlook to the table and create together with you more valuable insights.

2. Ask other analysts how they would approach the analysis of particular datasets. Maybe they did something similar in the past and you can save plenty of time.

3. Gather as much information as possible from stakeholders to focus on what matters for them instead of waist time on general questions and findings.

Communication

No other animal has developed communication skills like humans. We wouldn’t be able to conquest the whole planet without that one unique skill. Due to that skill, we can build strong relationships inside our tribe and with other tribes, convey abstract ideas and pass on incredible stories about faraway lands.

Good communication starts with a good strategy. How many times have you failed to convince others even though you have done an excellent analysis and prepared actionable recommendations? Your message didn’t get through because it wasn’t appealing to them. Consider the below points and tailor your message to be more impactful with your audience:

  1. Ask yourself what are the main pain points for your audience?
  2. Are they data literal and how advanced?
  3. Are they subject matter experts or do they need more introduction?
  4. What can they expect from you? Raw analysis with insights or clear guidelines and scenarios with recommendations?

Creativity

I’m not a fan of getting too creative in the visual representation of data. Data visualization is already an abstract form and making it more complicated by adding non-intuitive graphic shapes does not make it better.
However, using creativity to look at a problem from a new perspective and consider new possibilities is a direction every data storyteller should take. Most of the time we stay within our standard thoughts or typical suspects. This leads us in the long run to

However, using creativity to look at a problem from a new perspective and consider new possibilities is a direction every data storyteller should take. Most of the time we stay within our standard thoughts or typical suspects. This leads us in the long run to intellectual castration, which has several serious consequences, such as missed opportunities for the organization, unrecognized in time threats, and a retreat in development.
Creativity is again a skill that can be acquired and mastered. Experts recommend the following exercises to strengthen it:

1. Learn from others and surround with inspiration
The more you collaborate with others, read a lot, and learn new things, the more creative you are. You need to have enough information gathered to connect the dots and then new ideas start appearing.

2. Enjoy what you do
Doing things with passion produces unexpected outcomes. You need to be truly dedicated to your work to be able to find new solutions or patterns. If you do not like what you do, you are not involved and interested, do not expect from yourself outstanding performance. Maybe it is high time to change profession?

3. Find time to do nothing
Give your brain a break. My best ideas show up mostly when I do something different like taking shower, doing exercises, drawing, or reading. When you feel overwhelmed, simply switch your activity, and focus on something else. Your brain anyway still processing that idea in the background and doing the magic.

4. Walk
Stanford study has shown that walking improves creativity. So, when you have a problem, simply take a dog for a walk. Many CEOs already have introduced walking meetings within their organizations to increase people ability to think out of the box.

5. Hypothesize
One of my co-workers taught me a great technique. It is a simple question to ask, “What would have to happen to achieve XYZ”. That simple technique removes any barriers from our brains and shifts from concentrating on constraints, what we naturally do, to focusing on possibilities.

[1]Partnership for 21st Century Skills. http://www.p21.org/




Mind the Gap! – How visualizing missing data influences people’s trust in data quality and affects decision-making processes.

The ethical approach to data visualization has many faces. One of them is dealing with missing data and the way of communicating them to the audience. In the real world, we face situations that our databases are incomplete.  This is a common case of many reasons. Some are technical errors that can occur during ETL processes, others appear when data is collected manually, especially as a result of surveys, as people often fail to answer all questions.

Statistical procedures often eliminate entire records when only one variable is missing. This leads to a dramatic shortage of statistical samples. However, many times, even though our data is leaky like Swiss cheese, we have to present them and what is even worse, draw conclusions, because having 100% of data is in many cases ineffective and unrealistic in terms of costs and time.

Statistical approach

To stay honest with our audience and to present the observations or phenomenon to them in the most transparent way, we have only two options: to present gaps in the data or imputed data in place of missing data. There are several imputation methods widely used in statistics and statistic data modelling. The most common ones are:

  • Case deletion – omitting cases with incomplete data and not take them to analysis.
  • Zero-filling – imputation of value 0 for all missing data.
  • Linear interpolation – replacing missing data with estimated values.
  • Marginal means – the mean value of variable is used instead of missing one.

More explanations of the specific methods you can find here.

Nevertheless, what method we are going to use, we need to communicate to the audience about which data comes from observations and which ones are imputed. This communication should be given in voice and visual form to strengthen the message leave no room for presumptions.

Dilemma – show gaps or imputed data?

Many strategic decisions are data-driven and missing data impacts the overall understanding, interpretation and reasoning of a phenomenon if not properly addressed.

Recently I found interesting research by Hayeong Song and Danielle Albers Szafir that shed some light on how we visually communicate missing data, which has a significant influence on data quality perception and on confidence in drawing conclusions. Research emphasizes that visualizations that highlight missing data but do not break visual continuity are perceived by responders as those with higher data quality. The general conclusion is that imputation methods are better graphical choices than simply removal of information as they do not decrease perceived data quality as much that have consequences in the decision-making process. However, the very important aspect is to highlight imputed data by different shapes or colours. Another interesting graphical decision is to present imputed data as error bars. It gives our audience additional information about the likely range of values.

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The research results in Figure 5 (b) clearly show that linear interpolation has the greatest positive impact on the perceived quality and accuracy of the data, and the visualization with data absent (Figure 4 (a)) is the lowest.

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The research was carried out for two commonly known visualization: a line chart and a bar chart. Both graphical choices gave similar outcomes.

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Conclusion

I have several books which are like a shining star that guides me through the darkness. One of them is “The Little Prince” Antoine de Saint-Exupery and quote from that book: “You become responsible, forever, for what you have tamed”. I believe we should have exactly the same approach to our analyses and their graphical representations as data analysts or data storytellers.

My personal attitude towards data – ethics in data storytelling.

On September 26, 1983, in the middle of the Cold war, Russian lieutenant Stanislav Petrov was on duty at the command centre of the nuclear early-warning system. The system reported that six missiles were fired from the US toward the ZSSR. Petrov based on provided information had to decide whether the alarm was true or false and to obey or not obey orders. After countless minutes that seemed to be an eternity, Petrov judged that it was a false alarm and saved the world against third war – the nuclear for sure. Later, the investigation revealed that the system malfunctioned.

But what kind of the world could we live in now if Petrov had not considered other options of the system’s response? Having that historical event in mind, can we trust any information without a doubt?

As data analysts or data storytellers, we are like a nuclear early-warning system. We provide people with the information they need to make critical decisions and shape the future. It is a very responsible role.

Why is so hard not to lie with data?

Does it sound controversial?  I believe so. Does it sound realistic? For sure. Why do I think so? Are you confident that you know all aspects of a subject that you want to present to others? Have you considered all possible options and looked at them from all involved stakeholders’ perspectives? Are you sure that the data set time period is long enough, and data quality is high? There are more questions than answers. So, tell me which version of the truth you are holding in your visualizations?

I do not accuse anybody to mislead people on purpose. Most of the time when we prepare data analysis and data visualizations to communicate information, we have pure intentions. The case is that we hold some biases and believes, and our brain uses previous experiences, and constantly makes unconscious assumptions. All that influences our thoughts and perception.

Harmful data visualization

Let’s do the mental exercise and think together about how harmful data visualization can be. Currently, I’m reading an exciting book by one of the most recognizable authors of the information visualization domain Alberto Cairo “How charts lie”. In one of the chapters, there is a story about nationalist Dylann Roof, who killed several Afro-Americans by being influenced by some charts that presented a number of crimes vs ethnic roots. That shocked me and opened my eyes to the potential consequences of distributing misleading visual representations of data.

That warning is more for data journalists and other people who juggle with data publicly. Often to get more votes or support or to influence some kind of the audience line of thinking. However, even in the business environment, we must be cautious not to make the same mistakes, because results can be catastrophic and have a real impact on people. Nevertheless, all of us should remember that when we share any data on social media or on other web pages.  

The potential negative impact of wrongly done analysis and poor data visualizations:

  • Hundreds of people can lose their job,
  • Profitable business sector can be shut down,
  • Launch of a new product can miss the target,
  • Thousands or billions of people can be at threat because of the release of the new drug.

This vulnerability is real because people who make decisions make history. There is always a human factor in any success or failure.

Do you feel like an influencer?

Some time ago I had a lot of fun preparing and sharing data visualization. But currently, I’m not so eager to do that. I didn’t have enough confidence in the data that are available, and I don’t have enough time to dive into and understand the specific subject, make analyses and investigations.

In upcoming posts, I’ll focus on ethics from a data visualizations point of view. The first one is data range.

Data range

Insights could differ very much in case of changing data scope. Anyone who has some shares on the stock market knows that depending on the selected time range he or she can observe positive or negative trends. The same cognitive dissonance we can have presenting data within our organization. Maybe in the last two years, we achieved tremendous revenue growth, but looking at revenue from a longer perspective, it can turn out that we even got closer to the results from the financial crisis (pick your favourite one as an example, they come and go periodically).

Figure 1 depicts what kind of understanding and feeling the investor can have to look at the same data but from different ranges. The left chart can indicate that results are declining, but when we look at the right one, we can see that in the longer perspective trend is positive.

Figure 1

Of course, our narration can be built around the latest two years of growth, but we shouldn’t hide information from the bigger picture. The approach in such a case should be to display the bigger picture first – a longer period of data is displayed and then zoom in on the last two years to present factors of recent revenue growth.

Another example, which is notoriously used to present voting results, is presenting people support for particular parties but having only people who voted as the full population. When I listen to the news in the mass media, often people refer to the election results without considering the voter turnout. That narration skews reality. Let’s see the below example. Figure 2 shows the result of the latest presidential elections in Poland. What will most people remember from the chart? That Duda won and had more than 50% of public support.

Figure 2

But this is not true! The real public support for Duda was 34.49% if we consider the voter turnout. The voter turnout in this election was 68.18%. It means that 31.82% of Poles didn’t go on the election. I would love to see in the mass media charts which present the entire election results, including those who didn’t vote. Then we would have the complete picture of people’s political preferences. However, I still see truncated data scope.

Figure 3

By manipulating data range as a timeline or included/excluded categories, we tell different stories about data and evoke different understandings and feelings in our audience about the subject. Let’s remember that to not lose in translation the most objective view possible.

Why you need Change Management in successful BI products adoption. Effective implementation strategies.

I promised to prepare a post about strategies of BI product successful adoption. It is a really hard work to achieve this. Not because your company CEO is a miser, and he or she doesn’t want to give any penny more for technology or on hiring a new workforce or outside company that works for you. The true challenge is to change the way people think … and behave.

Is Excel still the main data processing tool in your company? Do people still value working with this tool, because of its simplicity? If your answers are yes, you should already feel that changing their work habits is not a piece of cake.

BI solutions are new tools that need to be adapted in your organizational structures with proper care. Introducing a new tool goes hand in hand with introducing a new process. Introducing a new process involves managing change. And that is exactly what adoption is – the change management case.

Many organizations have in their structures Change Management department that can support BI projects in better and faster implementation by leverage knowledge of change management processes and techniques. Human Resources department can be very useful as well when it comes to redesigning some people habits and behaviours. I highly recommend asking them for support in any initiatives involving the introducing any new solutions.

CHANGE MANAGEMENT

Before we delve into the subject, let me briefly explain what change management is. It is a structured approach to prepare and support the entire organization and individuals in making organizational change.

For me, the term “change journey” is more appealing than “change management”. I associate change with the human factor more than with processes because without people’s willingness any change will take place. There are several methods or frameworks to lead successful change, however, for BI products adoption I found ADKAR model appropriate.

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A like AWARNESS of the need for change

From my experience, it is very important to start communicating about the change a long time before it happens. There is a psychological explanation behind this: people don’t like changes. They must get familiar with it, so preparation is key.

There are many channels that can be used for that purpose such as intranet, emails, workshops, and face to face meetings. The message should focus on answering why change is needed and on the benefits for each individual and the entire organization. It is important is to address any concerns or biases related to the change. (I wrote more about it here).

Ask HR department for support in this sensitive case. Involve top management as the voice of change.

D like DESIRE to participate and support the change

Although all efforts go into Awareness phase, it doesn’t mean that the results will be spectacular. The reason is that each person must make their own inner decision whether to support the change or not. Many practitioners point out that win hearts and minds is the most difficult part.

The main challenge here is how to get people to care about something they don’t care right now?

As unfaithful Tomas, most of us have to see to believe. Data platform projects are relatively long-term and for most of the time, end-users do not see results. Fortunately, we often create PoCs (proof of concepts) or prototypes to test certain assumptions. These small pieces of work can be shared to prove major concepts of a new approach. If this prototype is prepared to address one of the main company’s pain points, it would be easier to promote the new approach in the organization because of its undoubted value, which shows how this change can work for them.

K as KNOWLEDGE on how to change

This phase is associated with learning new tools and new skills. Many organizations use Excel to communicate data. Most of the time they prepare reports and send pdf files by email. Introducing a new tool like Power BI or Tableau forcing breaking old habits and behaviors and building a new one. This transition must be supported by delivering inhouse training that will bridge the gap between current knowledge and skills and desired one. In addition, all training must follow with creating an internal space where people have access to information about this new tool and have a place where they can share their experience and find answers to their questions.

Too often I observe a common scenario, that a new tool is introduced, however, staff training is not budgeted. This gives rise to a lot of frustration when people are required to provide valuable analysis, but they lack skills.

A as ABILITY to implement desired skills and behaviors

Having knowledge doesn’t mean that you know how to put it in practice. It takes time for people to develop a strong conviction that they are capable to use new tools for expected results. They won’t do it without support from the company side. Bringing in trainers or field experts who will work with them for a while can speed up learning process and smooth transition from the old to new approach. The main slogan here is practice, practice and even more practice.

R as Reinforcement to sustain the change

Have you heard about the “JoJo effect” when it comes to weight loss? It often happens that people who put a great effort into losing a few kilograms and spent several weeks or months on exhausting diet and psychical activities, very quickly regain their original weight. The reason is that they didn’t change their habits but only suspended for a while. There is even scientific proof that our brain reverts to safe, comfort and well-known practices. Therefore, maintaining the change is very demanding.

Before we are going to introduce a new approach, we must find out how the current processes are like and what people think and feel about it. Most of cases in organizations there are two or even more ways people do certain things. The first one is official procedure which can be found in organizational documents or regulations. The second one is the informal way people really work. This informal approach manifests their habits, behaviors and beliefs and is significant for us. Without revealing true processes, the new change won’t be successfully implemented due to lack of knowledge of how to implement it in such a way that people would be open to accept it.  

LESSONS LEARNED

Quick wins

You don’t have to start big. Start small.

When working with the client, we usually choose only one business area to improve. This could be sales performance, for example. Then we makeover reports, or we design them from scratch, develop and make them available as the reporting platform. This short cycle has many benefits. First of all, we can quickly verify technical aspects of the proposed solution, check with the stakeholders whether the product meets all the requirements, and what is most valuable if the product can be release to wider audience and prove its usefulness to them.

Leverage old tools

Instead of introducing rapid change as revolution, sometimes we can achieve better results by doing it in slower pace like evolution. If your employees are used to using Excel, don’t take it away from them. Most of the BI products have possibility to extract data into an Excel file. Focus in the first phase on process automation and ensuring a single source of truth. Anyway, they have to use the BI product to retrieve some data. Over time, as they trust and become familiar with the tool, they will start using it instead of extracting data from it.

Top management involvement

Recognition and a pat on the shoulder is not enough. Every change (as well as every initiative) requires fully committed top-level managers.

Several years ago, at one of my previous employers, I was involved in designing and implementing a new business intelligence tool. The goal was to provide a large number of reports covering all business aspects. The task wasn’t easy due to its complexity and data accesses challenges. Most of data were stored with IT department which didn’t want to share accesses. The first release took us almost a year (it was long before I heard about Scrum 😊). As you can imagine tremendous effort and time has been invested in delivering this tool.

This project was under company digitalization umbrella and aiming to improve the availability of information at every level of organizational hierarchy. However, most senior managers didn’t use this new platform, where they had all important information at their fingertips. They preferred the old-fashion style to send tones of emails asking for these essentials.

As you get the impression the adoption wasn’t spectacular, I would say that we missed the momentum.

There is a proverb that “the example comes from above”. I believe that if senior managers presented themselves as hard users of the platform, it would have enormous impact on the platform usage.

Ambassadors of the new approach on each level of organizational hierarchy

Apart from Top management, you need army of true believers, who will be a voice of change. These people should come from different departments and from different levels of company’s hierarchy. They should be a role model for their colleagues.

There is no better option to involve people by giving them the chance to become fathers and mothers of the initiative. Parents love their children selflessly.

You can follow the tactic of one of my clients. They formed working teams with people from different departments, who were involved in the design of a brand-new reporting platform. These people talk about their new project in the halls, canteens, and during cigarette breaks. This is a perfect example of viral marketing!

Support, support and once again support

How would you perform driving a car without hours of training and a good teacher? Likewise, your people need teachers and resources to learn and master their skills. You can leverage whatever works: on-demand or instructor-led training, online resources, community groups or newsletters with examples how to use and read data from the new BI product.

One of my clients constantly uses emails to send out extensive examples presenting usefulness of the BI product. They provide screenshots and guide others on how to use a tool, but more importantly how to analyze with the tool and create insights.

Start with day one

The last good practice that I want to present is to combine BI products into internal processes. This tactic forces people to use this tool and cut any discussion, whether they deem it relevant or not.

That tactic is for companies that really have ambitions to become a data-driven companies quickly. In such case all teams have to start workday by checking the latest data and on that basis and making decisions what they will do today to improve the performance, for example.

The great example is Daily Scrum – meeting (one of Scrum time boxes). During this event, a team relies on yesterday’s activities planning today’s activities. They use Kanban board to track data about the progress of current work.

Likewise, dashboards or reports should be used as a mandatory tool for daily stand-ups to discuss ongoing performance and set the next directions.

How to better design dashboards and reports. Data Storytelling in BI products design.

Not everyone has an opportunity to be on the first line and present data in front of the audience. Many are silent data heroes at the back of the stage. They constantly work with data to make sense of them and pass it on to others.

I know from my experience that in many organizations people work in silos, and it can be a tangible barrier in delivering well-designed, actionable dashboards. The best option to overcome this phenomenon is to make an effort and find end-users to gather their requirements and tailor reports for their specific needs. Only in this way you can find out what the true story should be built around a particular data set. The rest is a piece of cake.

Nevertheless, if you are one of that data heroes, to be honest, you are the true master here. You decide which data sets will be distributed within your organization and to what extent.  So, you may not be presenting the results in front of the audience, but they are likely seeing them with your eyes.

However, it is a double-edged sword. Having great influence results in having huge responsibility. It is a challenge for every communicator, and you are a kind of communicator because you prepare and hand down information.

I will just present only a few which I find very useful, and I often use them in my work. These technics are easy to remember and easy to implement, so everyone can benefit from them. They have similar usage as linguistic construction which can influence you to buy or do something.

We will go through:

  • Colour
  • Size
  • Shape
  • Common region
  • Position

COLOR

Humans see colours, maybe not in such spectacular range like other animals (check this article about hummingbirds), but still it is one of the most important senses that helps us understand the world and allows us to run away from wild animals in the jungle.

When it comes to designing dashboards, use colours to lead the audience from point to point. It is important to use just several ones. There is a good rule of five. Take five colours, assign to them meaning as for example white – the main colour for background, grey – major of data in data visualization, dark blue – numbers, black – text and icons, and orange – focal points. You can extend orange to orange and green if you want to differentiate positive and negative results.

In such way, you use colours on purpose and teach the audience their role in conveying the message.

To illustrate that we can compare these two pictures. Both charts present the same information – sales of regions. But the chart on the left side doesn’t promote any region. We can see all of them equally. It just aggregates information and presents them on the graph. However, the chart on the right side emphasises one of the regions (yes, that chart is created for the north region manager) by making it orange ( the darkest colour) and the rest regions greyish and tells a story about this specific region performance. The rest of the regions give context to the story.

Due to that simple change, you draw attention to one region and force others to look at it closely with avoiding special interest in other regions.

SHAPE / SIZE

What else you can use to push some information in front of another? Humans can see easily changes in sizes or shapes, so why not to use it for our purpose? Especially when we remember about people who have some colour seeing difficulties. Size and shape are another visual channel which can be used to spotlight some data. Make it bigger, make it stronger.

When we change solid line of North to dashed one and thicken it, our brain processes information even faster than before, because we use three visual channels to code this information: colour, shape, and size.

Even when we take out colour and leave visualization black and white (which sometimes serves the best for better contrast), we can still achieve the same result.

Size cannot be introduced in all visualizations. Would be hard to do it with bar chart. But regarding shape it is much easier. You can use pattern to fill in North bar.

Size is essential for presenting numbers. Differing numbers sizes, we control which of them play the first fiddle and which ones are providing additional information. Shape can be manifested in font type or its boldness. But we must remember here about the parent rule of readability. There is a general rule that on dashboards we use sans serif fonts because they are without any additional decorations and work better for displaying on screens.

Unexpectedly, font types can evoke some emotions or can reflect word meaning in their look. It is especially handy when you are about to design infographics.  See examples.

COMMON REGION

Do you know that people tend to group and interpret objects which are in the close or shared areas? This principle has even its own name as the Law of Common Region and was devised by Gestalt group in 1920s.

I’m a hard user of that techniques when it comes to design dashboards. A single piece of information itself has no impact, however, when you connect a few dots together, the message can be powerful. To make it happen, it is important to create a common area for these elements. We can do this by adding background or border and create visual boundaries.

POSITION

Studies regarding how people view websites, commonly known as Eyetracking, are consistent in results. The area with the greatest attention is the top-left corner of the page follows by the top-right corner, then the down-left and the last one is the down-right corner (see image below).

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Of course, that we can use it to support data storytelling! Just divide a dashboard area into four quadrants and follow these two simple rules:

  • In 1&2 place information which you want to highlight as KPIs, the crucial changes in trends, threats and opportunities, and components which are essential to navigate on the dashboard.  Do not forget about the title. Use the best practices of designing UX (check this link about best practices in UX and find out what we have in common with goldfish).
  • In 3 & 4 are additional information that broadens perspectives or sheds another light on the already presenting data. At the bottom is the great place to place information about last data refresh, or report confidentiality.

Data storytelling is a mix of knowledge about data visual presentation, design and people perception. Having these components in place you are armed with a very powerful tool, which makes the audience listening to your voice…, even when this voice is behind dashboards that you deliver.

Data storytelling for busy people – strategies which always work

Do you need to know how to tell stories with data?

Ask yourself how often do you use data in your daily job? Or maybe how many times do you use data to convince others to your ideas? If your answers range from rarely to often, then this post is for you.

One scene from the movie “Silver linings playbook” stuck in my memory. The main character after having an explosion caused by hearing his wedding song, is sitting in the therapist’s office, and complaining that it would not have happened if that song had not been played in the therapist’s office. The response of the therapist was clear and brief “You need to build your own strategy how not to be afraid of that song”.

Building strategies helps us to be more productive and perform better, whether it is in our work environment or our private life. Our brain just loves mental shortcuts, and strategies are those shortcuts.  Especially when we are in a hurry and need simple solutions which always work.

 Let’s see what strategies we can prepare to make data communication more effective and efficient.

Comparisons

Comparisons are always a good choice when we want to present the progress of initiatives, outcomes of introducing new processes, or showcase sales performance in different markets. People compare things in their brains all the time, so any story based on comparison will be easy to understand. But it needs to be well-crafted.

Before and After

This strategy works well when delivering outcomes of recently introduced new initiatives or processes. Old state data is the best background to emphasize big change or the success of a new approach. You can present benefits or results in several dimensions: process, employees’ satisfaction, increase in a number of clients etc. Anything you deem valuable for your business.

As an example, we can put together two dimensions: employee’s satisfaction and a number of human errors. In Picture 1, it is easy to see that changes have improved the employees’ satisfaction and resulted in a decrease in human errors. Simple column charts displayed side by side will suffice to represent this data. Adding lines connecting columns makes visualizations more suggestive.

Picture 1

Us vs. All

Every good manager should brag about her or his team and highlight what a great job they do for the organization. If your team, the product, sales, or converted leads are the best, show how they stand out from the rest of the company.

To draw attention to your data, you can change its colour. This simple trick will distinguish your data from the others and push it to the foreground. See Picture 2.

Picture 2

Where are my stars?

When analysing revenue growth, we consider what is pushing it forward and what is holding it back. A very popular concept is to present leaders and laggers. The popularity of this concept stems from human nature. We admire and envy the best, but love the worst because they are worse off than we are!

C-Levels managers like to see contributors of the growth on the waterfall chart because this visualization shows at a glance which contributors have made money for the company and which have lost. For our revenue growth example, we can use two different colours to indicate leaders and laggers.

Picture 3

Changes over time

Changes over time are the next group of strategies which use the familiar comparative idea with a whole story set in time.  We can present how something develops over time and what is more appealing for our audience how it might be in the future. For such stories, we use line charts.

Show me the bright future!

Who would not want to know the future? Well, I do not… But, when it comes to the business environment the answer is always: everyone. When I work with clients, the trend of any phenomenon is a must. Many decisions within an organization are based on current trends and an estimation of future outcomes.

However, every data scientist will warn against relying too much on historic data. There is a strong tendency to predict future business performance behaviour based on past results. To temper expectations, we can provide several scenarios based on the same dataset. This approach will add value to our analysis if we introduce factor parameters to each scenario. Typically, three scenarios are provided: optimistic, realistic, and pessimistic.

To illustrate the technique, I will use an example with revenue growth (every CEO cares about revenue growth). The main factor in the example could be the launch of product A in a new market. As we all know, launching a product on a new market can be a huge success, but on the other hand, it can also be a spectacular failure.  Using sales of product A as a parameter, we can create three separate revenue scenarios for the upcoming fiscal year.

Picture 4

Factors of success or failure.

Another story which is attractive for the audience is about factors which influenced the results of the phenomenon. This narration is based on our natural tendency to look for cause and effect relationships. Maybe if we knew what had triggered results in the past, we could use it in the future to prevent bad impact or use identified factors to achieve better outcomes?  This strategy is great when you want to convince senior managers to spend money on the next marketing campaign. Simply show them the periods with and without marketing campaigns on the line chart, where they can easily observe the ups and downs of the line representing sales. Do not forget to add some call outs to strengthen a message. See picture 5.

Picture 5

Connecting dots

The last strategy which I want to bring closer to you is about presenting the most crucial business metrics on the one-pager. This strategy is a master level, because whoever prepares it must be aware of connections between separate metrics and the overall influence which they have on the business health. This is very practical when trying to understand which processes drive others. The one-pager can show usual suspects, threats, and opportunities. For instance, if your core business as a company partner is selling services to the specific hardware, you can expect a drop in sales if hardware sales fall down.

Picture 6