We deal with data every day, especially at work. It can fuel our decisions and change the way we work. At the same time, if we’re surrounded by a huge amount of data, we may not find it easy to arrive at an optimal decision. This is where data visualization comes in.
Data visualization refers to the graphical representation of the data. It makes large amounts of information easier to understand and helps identify patterns and trends. People can easily comprehend information and make conclusions through data visualization.
“Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space,” wrote American statistician Edward R. Tuffe, author of the book “The Visual Display of Quantitative Information.”
Understanding how to approach data visualization allows people to equip themselves with the right tools, approach, and strategies as they gather data and present them visually. This is important to businesses who want to understand consumer behavior patterns or governments seeking data-backed insights on a crisis.
Data visualization may be considered a science because it is a process and represents data methodically and accurately. Data visualization begins with volumes of information, undergoes an intensive cleaning, classification, statistical and mathematical modeling, analysis, and design process, and ends with a visualization.
On the other hand, many argue that data visualization is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams are not inherent and must be learned.
Data visualization helps to communicate analytics results in pictures. In simple words, data visualization is the language of images. That is on par with the language of words both written and spoken and with the language of numbers and statistics.
Merging science and language
Science and language do not have to invalidate each other. Their elements can go hand in hand in data visualization.
In data visualization, the challenge is how to make more people take interest in scientifically processed data. Presenting appropriate and relevant information in an engaging format through design is what makes data visualization successful. Science processes and provides information based on certain objectives while design is a form of communication shaped by visual elements.
Combined, scientific data and design can generate meaning out of raw data. The end result of data visualization is almost always a story. In storytelling, the plot (design) won’t be able to progress without the characters (scientific data) and vice versa.
Ensuring that graphs and charts present meaningful results is important now more than ever. In MicroStrategy’s “2018 Global State of Enterprise Analytics,” 63% of data-driven organizations said that implementing analytics initiatives led to high efficiency and productivity while 57% said they became more effective in decision making.
With this, the challenge for organizations is to know how to structure, format, and present their graphical data that will allow them to make faster business decisions. Sign up for The KPI Institute’s Certified Data Visualization Professional course to learn the fundamentals of creating visual representations, the most effective layouts, channel selection, and reporting best practices.
High quality data can play a huge role in increasing efficiency and improving performance and can help managers in the decision-making process. Sometimes, it is acceptable to make decisions based on instincts and gut-feelings, but the majority of them should be backed up by numbers and facts.
Data-driven decision-making is a process of collecting measurable data, based on organizational goals, extracting, and formatting data, analyzing the insights extracted from it, and using them to develop new initiatives. Nowadays, advanced software is available to help with data gathering, processing, reporting, and visualizing, to support managers.
The main steps of the decision-making process
The first step to build a well-functioning, data-driven decision-making process is to clearly define organizational goals, and to identify the questions to which the answers we find can help reach these goals. For example, if our company’s revenue goal is to increase its portion of the market share by 20% until the end of the year, a good question would be: what are the most important factors which have influence on market share?
The next step is to identify data sources and to find custodians. The source of the data highly depends on its type. There are qualitative data, which cannot be expressed by numbers, and quantitative data, which can be measured by numbers. We can collect data from primary and secondary sources. Primary sources can be observations, interviews and surveys, whilst secondary data can be collected from external documents, third-party surveys and reports.
The third main step is to clean the gathered data. During the data cleaning process, raw data is prepared for analysis by correcting incorrect, irrelevant or incomplete data. There are six data quality dimensions which should be kept in mind, during this process: Accuracy (indicates the extent to which data reflects the real world object), Completeness (refers to whether all available data is present), Consistency (refers to providing the same data, for the same object, even if this data appears in different reports), Conformity (consists in ensuring that data follows a standard format, such as YYYY/MM/DD), Timeliness (indicates whether the data was submitted in due time, respecting the data gathering deadline) and Uniqueness (points out that there should be no data duplicates reported).
Only now, the data analysis process can start. Statistical models should be used to test data and find answers to the business questions identified beforehand. Descriptive statistics can help to quantitatively describe and summarize features of data and to describe, show or summarize data in a meaningful way. For example, monthly sales or changes in employee competency levels can easily be presented in a visual manner.
Interferential statistics can help find correlations between different variables and predict future outcomes. For example, by using regression analysis, we can make a prediction on how growth, experienced in the employee competency level, can positively affect the sales volume.
Even if the data gathered is cleaned and correct, and the data analysis process has respected all the recommendations above, if the data is not presented in a meaningful way, it will not be of much use. Well-presented information and the outcomes of the analysis can help in interpreting data, thus supporting the decision-making process.
From time to time, data should be updated and re-evaluated, to make the best decisions in today’s continuously changing business environment.
The advanced analysis techniques and software, which are available nowadays for the majority of organizations, make it possible to build up a data-driven decision-making culture, which leads to more prudent business decisions. These tools generate more thoughtful decisions that help performance improvement, which ultimately lead to organizational growth.
Working in a team can create synergy, since a good team will likely produce better results than individuals working separately. However, measuring team performance is even more challenging than measuring the performance of each employee separately, since you have to take into consideration each and every member’s performance, in relation to the others’, as well as the overall team’s.
In general, employees are members of departments. A department is a subdivision of an organization and an individual, generally, can only be part of one department. That being said, nowadays, teams are more flexible in how they are formed and how they operate: a team can be a temporary group formed to work on a specific task or project. Therefore, employees can be members of only one department, but several teams.
The first step is to link the team results to the organization’s goals, by cascading the objectives and KPIs from the organizational level to the team level. It is not very productive to have a well-performing team whose work does not help the organization reach higher performance goals.
Key aspects of team performance measurement
There are many indicators and measurements that can be useful when considering measuring your team’s results. In what follows, we’ve put together a list of the most widely employed benchmarks, so that you may get a general feel for what is considered useful to keep track of.
Employee attendance: Employee attendance is an important aspect of team performance since absenteeism incurs excess costs and will have an unwanted effect on team productivity & employee morale.
Moreover, late employees can be the source of annoyance or frustration, which will reduce team cohesion and further reduce a working unit’s effectiveness. Therefore, attendance related KPIs should be the first ones to track, when we talk about team performance:
% Absenteeism: Indicates the percentage of employees within the team who are repeatedly and/or unexpectedly absent, out of the total team members.
$ Lost time accounting: Measures the potential revenue lost because of idle workers or wasted hours within the team.
# Time lost by starting work late: Measures the volume of time lost due to employees starting their working hours late.
Client satisfaction: Every team has an internal/external customer, which is why satisfaction can be a good measurement unit. Improving customer satisfaction will eventually result in a more efficient production process, better service and ultimately, lead to more satisfied external customers. The most important KPI to measure in this regard is the following:
% Customer satisfaction: Measures the level of satisfaction exhibited by the team’s customers (current employees, distributors, vendors, departments, or external clients), towards the inter-functional services provided, be it communication, productivity and/or responsiveness.
Employee retention within the team: A low retention level or a high turnover level is usually connected with low levels of efficiency and productivity, which in the end can lead to a negative impact on an organization’s overall results.
This aspect can be influenced not just by the team performance, but also by the HR department’s performance, the working environment and work policies, the supervisor, as well as the promotion and professional development opportunities for the future. However, high level of employee turnover within a specific team could indicate team-related problems.
The most important employee retention KPIs to measure are the following:
% Employee turnover: Measures the rate at which employees leave the team in a given time period (e.g., month, quarter, year).
% Employee retention rate: Measures the total number of employees retained at the end of the reporting period, expressed as a percentage from the total number of employees that were in the team at the start.
Employee satisfaction: Studies suggest a direct correlation between employee satisfaction, employee engagement and increased performance. Employee engagement can be increased through various company efforts, such as facilitating the development of skills for its employees, giving them a sense of trust and integrity, and clarifying their opportunities for future career development. The most important indicators to take into consideration, when looking to improve or maintain employee satisfaction, are the following:
% Employee satisfaction: Measures the employees’ satisfaction and motivation level, with aspects regarding their job and working environment: job responsibilities, team and management, workplace, and professional development.
# Employee Engagement Index: Measures the engagement level of employees in their work activities and responsibilities, in terms of enthusiasm, commitment and discretionary effort.
Productivity of individuals: Productivity of individuals is a key element of team performance. The following KPIs help measure a team’s contribution to the organizational goals, and the contribution of its members to the general team results:
$ Profit per employee: Measures the team’s contribution to the overall profit pool. It is a particularly important ratio in customer-focused businesses, such as those in the service sector.
$ Sales per employee: Measures a team member’s productivity and efficiency in generating sales.
% Human Capital Return on Investment (ROI): Measures the return on investing in a team’s human capital, after adjusting for the cost of financial capital.
$ Human capital value added: Measures the value added through productive activities, by a team’s members. Reflects the adjusted operating profitability figure, calculated by subtracting all expenses except for labor expenses, from revenue, and dividing the adjusted profit figure by the total headcount.
OKRs or KPIs?
In some specific cases, where the productivity of a team is not directly linked to the organizational revenue or profit (ex. support teams), it is more advisable to use OKRs (Objectives and Key Results), instead of KPIs (Key performance indicators), to measure productivity.
OKRs contain a well-defined objective and one or more key results. OKRs help define how to achieve a goal through concrete, measurable actions. So, in case of the support teams, these results should be measured to track team performance, as they will be able to paint a more accurate picture of their efforts.
It is a complex process to measure team performance; therefore, it should be analyzed from numerous angles, according to each team’s specialization and workload. It should be noted that the aforementioned indicators are not the only ones which can portray a group’s results. However, if you are looking for a quick introduction into this topic, these KPIs will serve as a sustainable foundation on which you can build your employee management system.
When all you have is a hammer, everything looks like a nail.
That English proverb suggests that if someone has only a limited number of tools, instruments, or skills to resolve problems, they may be used in situations where they are not meant to be used.
If a business is facing a problem, they have to correctly identify it before they come up with innovative ideas as their solution. To make sure that the solution can address the issue and bring in results, a company that operates like a tool factory should streamline its innovation process.
For instance, if a customer wants to use a screw to fix a picture on the wall, and the current tool which they own, the hammer, is not suitable for this. With this, the may recommend and develop a better version of their already existing product.
The output could be a bigger hammer that would allow the customer to get the screw into the wall. This kind of innovation is called incremental innovation, which occurs when a company’s existing products or services have been upgraded to meet customer needs or further compete in the market.
For example, Apple Inc. originally created a touchscreen tablet, but now it combines the functions of an iPod, a cell phone, and an internet communication device.
If the tool factory wants to build an innovation culture within the organization, invests in R&D and in intellectual resources and then comes up with something new. This kind of innovation is called radical innovation, which refers to replacing existing products or services with new ones that have never been done before.
Amazon.com can be considered as a radical innovator since it managed to revolutionize book selling and introduced the portable wireless electronic reading device now popularly known as Kindle.
If the product innovation is successful and the customer buys the screwdriver and is able to fix their problem, this means that the tool factory is able to:
grow as they offer solutions for two types of issues
remain profitable since the customers who already have a hammer can now buy a screwdriver too
differentiate themselves from the other factories because they are offering something that competitors don’t.
Why innovation fails
However, a product innovation like the screwdriver could fail for many reasons.
One reason is innovation does not solve a customer’s problem all the time. For example, if the initial research was not correctly done, it could be possible that the customer needed only duct tape to put the picture on the wall. It may have nothing to do with the screwdriver because the customer does not have a screw.
Moreover, innovation may take too long to be launched in the market so the customer may look for other possible solutions. For instance, if the customer does not have the necessary tool to put the picture on the wall, they might buy a photo album and use it for all their other pictures in the future.
Another reason why ideas may fail is they are underfunded or poorly launched. If the company does not have the right marketing strategy for the screwdriver, people won’t hear about it and therefore, won’t be able to use it.
The execution of ideas requires time and resources too. The manufacturing machines used to develop the screwdriver are expensive. It takes a lot of time for employees to learn the manufacturing process and how to use the machines.
An iconic example of innovation failure was the Galaxy Foldable Phone by Samsung, which meant to offer large screens on small space to the customers. However, when the device is folded, customers don’t find it comfortable to carry. It is also deemed too fragile. Because of this, the production of the device lasted for only a few months and then it was halted.
Making innovation work
If businesses want to make product innovation work, they have to do their research properly and understand what customers actually need. Businesses must also allocate enough resources to develop new types of instruments.
If there is a new product, businesses must develop and test product prototypes first and identify all the possible problems that may come with it. It is also important to invest in introducing a new product to customers and educating them on how to use it. The most important thing is not to screw up, but to nail it!
If you would like to learn more about the best practices for an innovation-based business culture and establish an efficient organizational innovation system, check out TKI’s Certified Innovation Performance Professional course.
No matter the size or type of your business, reports probably get created for it regularly. Whether you’re a public agency or a private organization, data clarity & accuracy are two must-haves nowadays.