Data Presentation, Visualisation and Graphical Presentation - Financial Management and Business Data Analytics | CMA Inter Syllabus
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There is a saying ‘A picture speaks a thousand words’. Numerous sources of in-depth data are now available to management teams, allowing them to better track and anticipate organisational performance. However, obtaining data and presenting it are two distinct and equally essential activities.
Data visualisation comes into play at this point. Recent studies reveal that top-performing finance directors are more likely than their peers to emphasise data visualisation abilities.
The capacity to explain complicated ideas, identify informational linkages, and provide captivating narratives resulting from data not only elevates finance’s position in strategic decision making, but also democratises data throughout the business.
Why data Visualisation is important?
Scott Berinato, senior editor and data visualisation specialist for Harvard Business Review, writes in a recent post that data visualisation was once a talent largely reserved for design- and data-minded managers. Today, he deems it indispensable for managers who wish to comprehend and communicate the significance of the data flood we are all experiencing.
This is particularly true for finance, which is becoming the data hub of the majority of progressive enterprises. David A.J. Axson of Accenture highlights in his paper “Finance 2020: Death by Digital” that finance is transitioning from “an expenditure control, spreadsheet-driven accounting and reporting centre” to “a predictive analytics powerhouse that generates business value.”
Finance is able to communicate these analytic findings to the entire business through the use of data visualisation. Several studies indicate that sixty five percent of individuals are visual learners. Giving decision makers an opportunity to have visual representations of facts improves comprehension and can eventually lead to better judgments.
In addition, the technique of developing data visualisations may aid finance in identifying more patterns and gaining deeper insights, particularly when many data sources or interactive elements are utilised. For example, contemporary finance professionals frequently monitor both financial and non-financial KPIs. Data visualisation may assist in correlating these variables, revealing relationships, and elucidating the actions required to enhance performance.
Doing data Visualisation in the right way
All data visualisation isn’t created equally engaging. When properly executed, it simplifies difficult topics. However, if data visualisations are executed improperly, they might mislead the audience or misrepresent the data.Finance professionals who are investigating how data visualisation might help their analytics efforts and communication should keep the following in mind:
● Know the objective: Before the development of great images, one must first grasp the objectives. HBR’s Berinato suggests, first establishment of the information if it’s conceptual or data-driven (i.e. does it rely on qualitative or quantitative data) is required. Then specify if the objective is exploratory or declarative. For instance, if the objective is to display the income from the prior quarter, the goal is declarative. If, on the other hand, one is curious as to whether the income increase correlates with the social media spending, the objective is exploratory. According to Berinato, determining the answers would assist in determining the tools and formats required.
● Always keep the audience in mind: Who views the data visualisations will determine the degree of detail required. For instance, finance data presentations for the C-suite require high-level, highly relevant information to aid in strategic decision-making. However, if one is delivering a presentation to ‘line of business’ executives, delving into the deeper details might offer them with knowledge that influences their daily operations.
● Invest in the best technology: There are a multitude of technological tools that make it simple to produce engaging visualisations in the current digital age. The firm should first implement an ERP that removes data silos and develops a centralised information repository. Then, look for tools that allows to instantly display data by dragging and dropping assets, charts, and graphs; offer search options and guided navigation to assist in answering queries; and enable any member of the financial team to generate graphics.
● Improve the team’s ability to visualise data: Most of the agile finance directors rank their team’s data visualisation abilities as good, compared to only twenty four percent of their counterparts, according to an AICPA survey. While everyone on the finance team can understand the fundamentals of data visualisation, training and a shift in hiring priorities may advance the team’s data visualisation skills. Find ways to incorporate user training on data visualisation tools, so that the staff is aware of the options that the technology affords. Additionally, when making new recruits, look out individuals with proficiency in data analytics and extensive data visualisation experience.
The amount of data analysed by financial teams has grown dramatically. Data visualisations may help the team convey its strategic findings more effectively throughout the enterprise.
The absence of data visualisation would make it difficult for organisations to immediately recognise data patterns. The graphical depiction of data sets enables analysts to visualise new concepts and patterns. With the daily increase in data volume, it is hard to make sense of the quintillion bytes of data without data proliferation, which includes data visualisation.
Every company may benefit from a better knowledge of their data, hence data visualisation is expanding into all industries where data exists. Information is the most crucial asset for every organisation. Through the use of visuals, one may effectively communicate their ideas and make use of the information.
Dashboards, graphs, infographics, maps, charts, videos, and slides may all be used to visualise and comprehend data. Visualizing the data enables decision-makers to interrelate the data to gain better insights and capitalises on the following objectives of data visualisation:
● Making a better data analysis:
Analysing reports assists company stakeholders’ in focusing their attention on the areas that require it. The visual mediums aid analysts in comprehending the essential business issues. Whether it is a sales report or a marketing plan, a visual representation of data assists businesses in increasing their profits through improved analysis and business choices.
● Faster decision making:
Visuals are easier for humans to process than tiresome tabular forms or reports. If the data is effectively communicated, decision-makers may move swiftly on the basis of fresh data insights, increasing both decision-making and corporate growth.
● Analysing complicated data:
Data visualisation enables business users to obtain comprehension of their large quantities of data. It is advantageous for them to identify new data trends and faults. Understanding these patterns enables users to focus on regions that suggest red flags or progress. In turn, this process propels the firm forward
The objective of data visualisation is rather obvious. It is to interpret the data and apply the information for the advantage of the organisation. Its value increases as it is displayed. Without visualisation, it is difficult to rapidly explain data discoveries, recognise trends to extract insights, and engage with data fluidly.
Without visualisation, data scientists won’t be able to see trends or flaws. Nonetheless, it is essential to effectively explain data discoveries and extract vital information from them. And interactive data visualisation tools make all
the difference in this regard.
The continuing epidemic is a current example that is both topical and recent. However, data visualisation assists specialists in remaining informed and composed despite the volume of data.
(i) Data visualisation enhances the effect of communications for the audiences and delivers the most convincing data analysis outcomes. It unites the organisation’s communications systems across all organisations and fields.
(ii) Visualisation allows to interpret large volumes of data more quickly and effectively at a glance. It facilitates a better understanding of the data for measuring its impact on the business and graphically communicates the knowledge to internal and external audiences.
(iii) One cannot make decisions in a vacuum. Data and insights available to decision-makers facilitate decision analysis. Unbiased data devoid of mistakes enables access to the appropriate information and visualisation to convey and maintain the relevance of that information.
According to an article published by Harvard Business Review (HBR), the most common errors made by analysts that makes a data visualisation unsuccessful are:
● Understanding the audience:
As mentioned earlier, before incorporating the data into visualisation, the objective should be fixed, which is to present large volumes of information in a way that decision-makers can readily ingest. A great visualisation relies on the designer comprehending the intended audience and executing on three essential points:
(i) Who will read and understand the material and how will they do so? Can it be presumed that it understands the words and ideas employed, or if there is a need to provide it with visual cues (e.g., a green arrow indicating that good is ascending)? A specialist audience will have different expectations than the broader public.
(ii) What are the expectations of the audience, and what information is most beneficial to them?
(iii) What is the functional role of the visualisation, and how may users take action based on it? A visualisation that is exploratory should leave viewers with questions to investigate, but visualisations that are instructional or confirmatory should not.
● Setting up a clear framework
The designer must guarantee that all viewers have the same understanding of what the visualisation represents. To do this, the designer must establish a framework consisting of the semantics and syntax within which the data information is intended to be understood. The semantics pertain to the meaning of the words and images employed, whereas the syntax is concerned with the form of the communication. For instance, when utilising an icon, the element should resemble the object it symbolises, with size, colour, and placement all conveying significance to the viewer.
Lines and bars are basic, schematic geometric forms that are important to several types of visualisations; lines join, implying a relationship. On the other hand, bars confine and divide. In experiments, when participants were asked to analyse an unlabeled line or bar graph, they viewed lines as trends and bars as discrete relations, even when these interpretations were inconsistent with the nature of the underlying data.
There is one more component to the framework: Ensure that the data is clean and that the analyst understands its peculiarities before doing anything else. Does the data set have outliers? How is it allocated? Where does the data contain holes? Are there any assumptions regarding the data? Real-world data is frequently complicated, of varied sorts and origins, and not necessarily dependable. Understanding the data can assist the analyst in selecting and employing an effective framework.
● Telling a story
In its instructional or positive role, visualisation is a dynamic type of persuasion. There are few kinds of communication as convincing as a good story. To do this, the visualisation must give the viewer a story. Stories bundle information into a framework that is readily recalled, which is crucial in many collaborative circumstances in which the analyst is not the same person as the decision-maker or just has to share knowledge with peers. Data visualisation lends itself nicely to becoming a narrative medium, particularly when the tale comprises a large amount of data.
Storytelling assists the audience in gaining understanding from facts. Information visualisation is a technique that turns data and knowledge into a form that is perceivable by the human visual system. The objective is to enable the audience to see, comprehend, and interpret the information. Design strategies that favour specific interpretations in visuals that “tell a narrative” can have a substantial impact on the interpretation of the end user.
In order to comprehend the data and connect with the Visualisation’s audience, creators of visualisations must delve deeply into the information. Good designers understand not only how to select the appropriate graph and data range, but also how to create an engaging story through the visualisation.
Data presentation architecture (DPA) is a set of skills that aims to identify, find, modify, format, and present data in a manner that ideally conveys meaning and provides insight. According to Kelly Lautt, “data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data Visualisation, communications, organisational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and Visualisations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organisational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data Visualisation, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen (which is data Visualisation). Data Visualisation skills are one element of DPA.”
Objectives
There are following objectives of DPA:
(i) Utilize data to impart information in the most efficient method feasible (provide pertinent, timely and comprehensive data to each audience participant in a clear and reasonable manner that conveys important meaning, is actionable and can affect understanding, behaviour and decisions).
(ii) To utilise data to deliver information as effectively as feasible (minimise noise, complexity, and unneeded data or detail based on the demands and tasks of each audience).
Scope of DPA
In the light of abovementioned objectives, the scope of DPA may be defined as:
(i) Defining significant meaning (relevant information) required by each audience member in every scenario.
(ii) Obtaining the proper data (focus area, historic reach, extensiveness, level of detail, etc.)
(iii) Determining the needed frequency of data refreshes (the currency of the data)
(iv) determining the optimal presentation moment (the frequency of the user needs to view the data)
(v) Using suitable analysis, categorization, visualisation, and other display styles
(vi) Developing appropriate delivery techniques for each audience member based on their job, duties, locations, and technological access.
Data visualisation is the visual depiction of data and information. Through the use of visual elements like dashboards, charts, graphs, and maps etc, data visualisation tools facilitate the identification and comprehension of trends, outliers, and patterns in data
4.1 Dashboard
A data visualisation dashboard (Figure 10.3) is an interactive dashboard that enables to manage important metrics across numerous financial channels, visualise the data points, and generate reports for customers that summarise the results.
Creating reports for your audience is one of the most effective means of establishing a strong working relationship with them. Using an interactive data dashboard, the audience would be able to view the performance of their company at a glance.
On addition to having all the data in a single dashboard, a data visualisation dashboard helps to explain what the company is doing and why, also fosters client relationships, and gives a data set to guide decision-making.
There are numerous levels of dashboards, ranging from those that represent metrics vital to the firm as a whole to those that measure values vital to teams inside an organisation. For a dashboard to be helpful, it must be automatically or routinely updated to reflect the present condition of affairs.
4.2 Graph, Diagram and Charts
Henry D. Hubbard, Creator of the Periodic Table of Elements once said, “There is magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.” Few important and widely used graphs are mentioned below:
(i) Bar Chart:
Bar graphs are one of the most used types of data visualisation. It may be used to easily compare data across categories, highlight discrepancies, demonstrate trends and outliers, and illustrate historical highs and lows. Bar graphs are very useful when the data can be divided into distinct categories. For instance, the revenue earned in different years, the number of car model produced in a year by an automobile
company, change in economic value added over the years
To add a zing, the bars can be made colourful. Using stacked and side-by-side bar charts, one may further dissect the data for a more in-depth examination.
(ii) Line chart:
The line chart or line graph joins various data points, displaying them as a continuous progression. Utilize line charts to observe trends in data, often over time (such as stock price fluctuations over five years or monthly website page visits). The outcome is a basic, simple method for representing changes in one value relative to another.
For a better visual impact, the area under the line may be shaded. Also if feasible, the line graph may be presented combining with bar chart.
