Tag Archives: data analytics

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.

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/

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

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.

source

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.

source

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

source
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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.

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.

source

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.

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

Circle Charts – when design meets data

  • Circle charts are better to use for entertainment or information purpose. They are not the best choice for a business environment.
  • Circle charts are attractive for receivers and can pull them into your story.
  • Using multilayers demands providing a well-defined legend.

Humans always have had a special attitude to the sun. In prehistoric and ancient ages, in some cultures sun had the status of God. Without any scientific theories, they just knew that the sun is unique and has a crucial role for our planet and any living creature. Even in cultures where ancient humans did not worship the sun, the sun motif was commonly used to decorating buildings, everyday items, or apparel.

Nowadays, we still willingly use the image of the sun, especially in art and architecture. Something is appealing in this figure. Centric circle shape with rays around them somehow reminds me of the wheel of life with rays as special moments.

Maybe that is why the pie chart and all variations of pie charts are so popular and like among people. The father of the most known data visualizations is William Playfair. He invented a pie chart in 1801, and it is still commonly used to depict data.

My personal relationship with a pie chart is …. complicated. I do not use them often in a business environment. It is hard to present accurate data on a pie chart, especially with a good number of categories. When it comes to present information for making decisions it is better to go for more readable visualizations like bar charts (check my post: “PIES ARE FOR EATING NOT FOR DISPLAYING DATA”).

However, a different story is with data journalism, when the purpose is to entertain, or inform the audience. In such case, I would give green light to anybody, who would like to present any complicated data on any variation of a circle chart like a sunburst, radial chart, or spiral chart.

Those charts give you an opportunity to present complex hierarchical information on one chart, so even though there are maybe not idealistically readable, they are still concentrated within one visualization, which is an advantage for the audience. Do not forget that data journalism has a different purpose. The main goal is to pull readers into the story. Surprisingly, more complex visualizations with a huge number of details, colors and shapes can be a better agent than simple one to achieve that mission. It is because readers must spend more time decoding that visualization and retrieve information from it. Another aspect that increases the involvement of readers is chart interactivity. Of course, that case can be applied only on website media.

EXAMPLES

Below infographics are good examples of the complexity vs. the reader engagement. It is hard to understand them at glance. You need to hang your eyes for a longer time and go deeper to acknowledge these images.

The huge advantage is adding other layers or rings to the image. Thanks to that technique additional data are introduced into a chart and we can interpret or read information from different angles or levels. Looking on the same image with several layers of information helps us to find interesting patterns and observation. Would be much harder to achieve that effect when having separate charts.

Global statistics

Our Mother Earth is round at it has a connotation with a round object like a circle. Why not use it to strengthen the message. The chart is combined with several charts placed on circle x-axis: life expectancy and average hours of sunshine is a bar chart. Life satisfaction is a heatmap.

https://www.designboom.com/design/sunshine-and-happiness-infographic/
https://www.visualcapitalist.com/visualizing-all-of-earths-satellites/

Time

The time in western culture is perceived as linear from years perspective. When we present years the line chart or bar chart would be our first choice. However, when it comes to the elements of one year, we perceive them as a cycle. What I definitely admire in circle charts is the possibility to present any periodical phenomenon connected with time:

  • Seasons: Summer, Autumn, Winter, Spring
  • Months
  • Weeks
  • Hours
  • Minutes
https://www.digitalartsonline.co.uk/features/graphic-design/award-winning-infographic-designer-nadieh-bremer-on-how-create-powerful-data-visualisations/

Hierarchical information

Presenting hierarchical data is challenging. However, sunburst charts can handle that. Sunburst charts consist of rings that represent a separate level of hierarchy. This visualization gives us an opportunity to present very complex information in one view.

Note that hierarchical information can be presented as qualitative or quantitative.

The below example presents types of cheese categorize by type of milk and their hardness. This information is qualitative. Another type of visualization that we could use would be a treemap. However, a treemap does not look such good as a circle chart.

https://stackoverflow.com/questions/17069436/hierarchical-multilevel-pie-chart

DOS & DONTS

  • Use colours to catch the attention but remember to choose them in accordance with best practices for colour blindness disabilities. Studies show that around 10% of people population have some disabilities in colours distinguish.
  • Always provide the legend. The legend should explain the meaning of colours, shape, sizes and even positions of objects on your visualization.
  • Add short text on visualisation. If there are points that should be emphasis place additional text with an explanation nearby them. The well-balanced text provides context for a particular point.
  • Plan the objects’ size with available space in mind and readability aspect.
  • Do not use too small fonts.
  • Do not use decorative fonts as they are not readable.
  • Remember about the title and short description of the data visualization.
  • Leave whitespace around the visualization to not clutter the page.

How to speed up information decoding by simple data visualization tricks – the story of one chart.

How many times have you struggled to quickly understand what a chart is presenting? It is something that I often experience in media when reading articles or watch some statistics on TV. Sometimes is extremely hard for me to make sense of what I see, just because I am not the subject matter expert and those data at a glance do not seem familiar. And let face it, I am a data person. What must feel ordinary people, who do not work with data on a daily basis and are not highly data literate?

This post is inspired by data visualisations in the article that I have read recently about the employment situation in the UK. You can find the link to the paper at the bottom of the post.

We are going to focus on three easy to introduce improvements to make any chart more readable, impactful, and thoughtful:

  • Additional Axis labelling
  • Annotation
  • Preattentive Attributes                

As an example, we will improve the below chart that presents changes over years of staff availability index.

Additional Axis Labelling

I am not familiar with the staff availability index. From the title and footer of the chart, I understand that the higher, the better. However, that information could be served on the plate. Based on my experience, I can see an easy fix for such a case that speed up the cognitive work of my brain. Most of the time, when some charts are presented, they present some changes over time or comparisons between two or more phenomena. 

In this case, adding small arrows to the Y-axis and additional words describing axis directions give much more sense to the chart and improve the audience experience. Now the chart presents not only changes over time but informs the expected direction of change.

Annotation

There is a common myth that “Data speaks for itself”. No data can speak because it does not have a tongue. The responsibility of proper understanding of the message lies on the messenger side.  Another quick win is adding more text to the chart itself. Additional description or insight help people to process information more effectively and, thanks to visual presentation, make it easier to remember. 

I have added a sentence from the article next to the point that I have wanted to emphasise. The rich text pays attention to the audience eyes, and the soft grey line directs to the specific point on the chart.

Preattentive attributes

Each object on Earth has properties like shape, colour, size, position. This is what we notice without using conscious effort, and because we do not involve too much conscious effort, we must take advantage of it to decoding information faster. Thanks to them, we can guide the audience eyes through our data visualisation and point them exactly where we want.

Introducing a small red dot is a true game-changer for presenting information on the below chart. We can get this effect by taking off the line chart colour and add to the chart another object with a different shape (circle), size (the circle is significantly thicker than the line), and by adding contrasting colour (the red one). At the final stage, let us analyse our eyes movement. First of all, our eyes start looking at the chart with the title (that is why do not forget about titles! Never!). Then they go straight to the red dot. Just next to the red dot is an insight that explains that point.  Next, they track the line chart and finally look at additional Y-axis labelling. Now, our brain, after collecting all this information, can process them and make sense of those data.

I would recommend those three easy to remember and use tricks to uplift any data visualisation that will improve your audience experience.

The link to the article:

https://www.theguardian.com/business/2021/jul/08/uk-employers-struggle-with-worst-labour-shortage-since-1997?CMP=Share_AndroidApp_Other

Use the force of tables, but choose wisely.

“You must unlearn what you have learned”, said Master Yoda. Tables are not visuals! Truth? Have you ever heard that?

Nothing more wrong. Tables are a very powerful tool for visualizing data if you use them wisely. The main advantage of tables is the ability to present several measures for the same category in one row. This allows your audience to make quicker decisions because all important information is “on the table”.

However, the human brain READ the table. There are plenty back and forward iterations which it does to understand table content. So to make understanding easier, some additional elements should be introduced into tables. In the end, we don’t want to overload the lazy brains of our audience. Let’s see how we can improve tables to make them more accessible for people.

What makes the bottom table better than this at the top? There are several bullet points, which I’m going to address. You should have already noticed titles. Titles, itself, are introducing a huge difference.

Flat table

This table is simply flat. All information is at the same level, which means that they equally attract your attention. Nothing is highlighted, except for the second rows… which is unnecessary. Well, it’s hard to read, right? There are more sins: small fonts, cluttering elements such as lines, grey backgrounds, no formats of values.

Meaningful table

In the table, I’ve introduced information hierarchy by using different font colour. Rows and columns headers are in the background. Values have the darker, bold font. What is more, visual elements are added. Bars differentiate revenue volume, RAG icons simply convey the message about target realization, arrows indicate the direction of the year over year change. Columns headers well describe a column content and columns order leads through information importance.

Spaghetti Monster. Visualising multicategories.

When I say “multicategories”, I mean more than 4 categories. Sometimes, a challenge of visualizing multicategories is like an old polish proverb “eat a cookie and have a cookie”, which is hard to put into practice. I often observe how data analysts try to approach this challenge. Common scenarios are for products, countries, businesses, departments, teams, agents or cost centres. For all these data, they try to find out meaningful insights by depict patterns and highlight interesting points… mostly on one chart. That visual decision creates a beautiful piece of abstract art riched in colours, shapes, different sizes of objects, patterns or crossing lines.

Could you imagine how someone must be determined and persistent to look at and try to understand the column bar chart with 15 categories presented on 5 years horizon? Which category has upward and downward trend? Which is a leading one? And foremost which one is which?

When I think about “multicategories”, my first association popping into my head is “clutter”. The clutter is one of the greatest factors of cognitive overload. To understand clutter impact imagine that, you try to talk to your friend in a crowded space like a bar. You are all ears to follow her or his, and even then, you are not successful. The same effort your brain does, when it is exposed to the flashy visualization.

So how to overcome this challenge?

Both visualisation present the same information:

  • trends over time,
  • product comparison,
  • the best and the worst-selling products.
Spaghetti Monster
Clear view

Doesn’t it look like Spaghetti Monster? You rummage with a fork to find a juicy bit of meat. Similar is with decoding some information from this visualisation (line chart), it costs a lot of effort and time. Our brain decoding one information eg. line colours, then stores it in memory, then compares lines position on the chart, then look for trends for each of lines goes back and forward through the chart to make a sense of it.

On the second approach, I’m showing the different strategy to present the same data set. Splitting information into two visualisations gives clarity and ability to draw a conclusion. The left chart enables the receiver to compare sales amount between products and memorise them easily. The right chart provides information about particular product trend in comparison to average sales. In this approach, we guide the audience through data. We pay their attention to important points. We don’t leave them alone having hope that they draw a conclusion on their own.

We are the data storytellers.