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.
(iii) Pie Chart
A pie chart (or circle chart) is a circular graphical representation of statistical data that is segmented to demonstrate numerical proportion. In a pie chart, the arc length of each slice (and, by extension, its centre angle and area) is proportionate to the value it depicts. Although it is called for its similarity to a sliced pie, it can be served in a number of other ways. The corporate world and the mass media make extensive use of pie charts. For a better representation, the number of wedges in pie chart should be kept in limit. The categories of HUL shareholders are shown in
(iv) Map:
For displaying any type of location data, including postal codes, state abbreviations, country names, and custom geocoding, maps are a no-brainer. If the data is related with geographic information, maps are a simple and effective approach to illustrate the relationship.
There should be a correlation between location and the patterns in the data. Such as insurance claims by state and product export destinations by country, automobile accidents by postal code, and custom sales areas etc.
(v) Density map:
Density maps indicate patterns or relative concentrations that might otherwise be obscured by overlapping marks on a map, allowing to identify areas with a larger or lesser number of data points. Density maps are particularly useful when dealing with large data sets including several data points in a limited geographic region.
(vi) Scatter plots
Scatter plots are a useful tool for examining the connection between many variables, revealing whether one variable is a good predictor of another or whether they tend to vary independently. A scatter plot displays several unique data points on a single graph.
(vii) Gantt Chart
Gantt charts represent a project’s timeline or activity changes across time. A Gantt chart depicts tasks that must be accomplished before others may begin, as well as the allocation of resources. However, Gantt charts are not restricted to projects. This graphic can depict any data connected to a time series.
(viii) Bubble chart
Although bubbles are not exactly their own sort of visualisation, utilising them as a method enhances scatter plots and maps that illustrate the link between three or more variables. By varying the size and colour of circles, charts display enormous amounts of data in an aesthetically engaging manner.
(ix) Histogram
Histograms illustrate the distribution of the data among various groups. Histograms divide data into discrete categories (sometimes known as “bins”) and provide a bar proportionate to the number of entries inside each category. This chart type might be used to show data such as number of items.
4.3 Tables
Tables, often known as “crosstabs” or “matrices,” emphasise individual values above aesthetic formatting. They are one of the most prevalent methods for showing data and, thus, one of the most essential methods for analysing data. While their focus is not intrinsically visual, as reading numbers is a linguistic exercise, visual features may be added to tables to make them more effective and simpler to assimilate.
Tables are most frequently encountered on websites, as part of restaurant menus, and within Microsoft Excel. It is crucial to know how to interpret tables and make the most of the information they provide since they are ubiquitous. It is also crucial for analysts and knowledge workers to learn how to make information easier for their audience to comprehend.
Similar to most graphs, a table arranges data along one axis. The x-axis is the rows, while the y-axis is the columns. Because tables are read, it is customary to display categories along the x-axis. The y-axis depicts the values within each metric, with clearly labelled columns indicating their significance. In contrast to the majority of charts, tables may arrange qualitative data and show their linkages.
Analysts typically utilise tables to view specific values. They facilitate the identification of measurements or dimensions across a set of intervals (e.g., what was the company’s profit in November 2018) (Ex. How many sales did each person close in 2019). A summary table may also efficiently summarise a huge data collection by providing subtotals and grand totals for each interval or dimension. The problem with tables is that they scale poorly. More than ten to fifteen rows and five columns make the table difficult to read, comprehend, and get insight from. This is because a table engages the brain’s linguistic systems whereas data visualisation excites the brain’s visual systems.
Adding visual features to the table will allow users to obtain understanding from the data more quickly than with a simple table. Gradients of colour and size aid in identifying trends and outliers. Icons assist the observer in recognising a shift in proportions. Using different markings will highlight relationships more effectively than a table of raw data.
Tables and crosstabs are handy for doing comparative analysis between certain data points. They are simple to construct and may effectively convey a single essential message. Before including a crosstab into a data visualisation, one should assess whether it serves the project’s aims.
4.4 Report design using data Visualisation
After producing a report, the last thing one anticipates is for someone to actually read it. Whether conveying ideas or seeking help, the information must leave an impression. To do this, one must present the report in a style that is both attractive and simple to comprehend. This is especially accurate if your report layout includes numbers.
How to use data Visualisation in report design?
There are few strategic steps to include data Visualisation in report design, as mentioned below:
● Find a story in the data
Data-driven storytelling is a powerful tool. Finding a story that connects with the reader can help to create an effective report. It’s also not that hard as it looks. In order to locate the story, one must arrange the data, identify any missing numbers, and then check for outliers. One may then view the data and examine the link between factors.
● Create a narrative
When some individuals hear the term “data storytelling,” they believe that it consists of a few statistics and that the task is complete. This is a frequent misconception that is false. Strong data storytelling comprises an engaging narrative that takes the audience through the facts and aids in their comprehension. Moreover, an
explanation of the significance of these ideas is essential. To compose an excellent story, one must:
(i) Engage the viewer with a catchy title and subheadings.
(ii) Incorporate context into the data.
(iii) Create a consistent and logical flow.
(iv) Highlight significant discoveries and insights from the data.
● Choose the most suitable data Visualisation
Data Visualisation is not limited to the creation of charts and graphs. It involves presenting the facts in the most comprehensible chart possible. Applying basic design principles and utilising features like as form, size, colour, and labelling may have a significant impact on how people comprehend the data. For instance, deciding the optimal number of slices for a pie chart or the space between bars in a bar graph. Knowing these tips may greatly improve the data visualisations.
● Follow the visual language
The report design may be for internal or external consumption. Despite this, one should develop material consistent with the company’s style guide. It is essential to adhere to data visualisation principles in order to achieve both uniformity and comprehension. A strategic methodology assists in implementation.
● Publicize the report
Some reports are not intended for public consumption. However, since they include so much essential information, they may contain knowledge that is of interest to individuals or media outside of the business.
We will now examine some of the most successful data visualisation tools for data scientists and how they may boost their productivity. Here are four popular data visualisation tools that may assist data scientists in making more compelling presentations.
(i) Tableau
Tableau is a data visualisation application for creating interactive graphs, charts, and maps. It enables one to connect to many data sources and generate visualisations in minutes.
Tableau Desktop is the first product of its kind. It is designed to produce static visualisations that may be published on one or more web pages, but it is incapable of producing interactive maps.
Tableau Public is a free version of Tableau Desktop with some restrictions.
It takes time and effort to understand Tableau, but there are several tools available to assist doing it. As a data scientist, Tableau must be the most important tool one should understand and employ on a daily basis.
The application may be accessed through https://www.tableau.com/
(ii) Microsoft Power BI
Microsoft Power BI is a data visualisation tool for business intelligence data. Reporting, self-service analytics, and predictive analytics are supported. In addition, it provides a platform for end users to generate reports and share insights with others inside their business. It serves as a centralized repository for all of the business data, which all of the business users can access. Through such linkages, the prepared reports may be shared inside the organisation, making it a crucial tool forbusinesses seeking a consolidated data reporting system. The application may be accessed through https://powerbi.microsoft.com/en-au/
(iii) Microsoft Excel
Microsoft Excel is a data Visualisation tool with an intuitive interface, so it is not necessarily difficult to use.
Excel provides several options for viewing data, such as, scatter plot, bar chart, histogram, pie chart, line chart, and treemap etc. Using these techniques, one may illustrate the relationship between two or more datasets that is wished to compare. Also one may examine the relationships between variables to discover if they are connected or not.
Numerous data analysts utilize techniques in MS Excel to examine statistical, scientific, medical, and economic data for market research and financial planning, among other applications.
The application may be accessed through https://www.microsoft.com/en-in/microsoft-365/excel
(iv) QlikView
QlikView is a data discovery platform that enables users to make quicker, more informed choices by speeding analytics, uncovering new business insights, and enhancing the precision of outcomes.An easy software development kit that has been utilized by enterprises worldwide for many years. It may mix diverse data sources with color-coded tables, bar graphs, line graphs, pie charts, and sliders.
It has been designed using a “drag and drop” Visualisation interface, allowing users to input data from a variety of sources, including databases and spreadsheets, without having to write code. These properties also make it a reasonably easy-to-learn and -understand instrument. The application may be accessed through https://www.qlik.com/us/products/qlikview
Solved Case 1
Sutapa is working as an analyst with SN Company Limited. She is entrusted with the responsibility of making a presentation before the senior management. She knows that data Visualisation is an important tool for presentation, and a good data Visualisation can make her presentation more effective. However, she is not very sure about the data visualisation tools, that are available.
What are the important data Visualisation tools available that Sutapa may use for an effective and impressive presentation.
Solution :
A. Theoretical Questions:
1. Following is a widely used graph for data Visualisation
(a) Bar chart
(b) Pie chart
(c) Histogram
(d) All of the above
Answer:- d. All of the above
Choice "D" is correct as-
Widely used graph for data Visualisation :
2. Following are the objectives of data visualisation:
(a) Making a better data analysis
(b) Faster decision making
(c) Analysing complicated data
(d) All of the above
Answer:- d. All of the above
Visualizing the data enables decision-makers to interrelate the data to gain better insights and capitalises on the following objectives of data visualisation:
3. Following are the scope of DPA
(a) Defining significant meaning (relevant information) required by each audience member in every scenario.
(b) Obtaining the proper data (focus area, historic reach, extensiveness, level of detail, etc.)
(c) Determining the needed frequency of data refreshes (the currency of the data)
(d) All of the above
Answer:- d. All of the above
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
4. Maps may be used for displaying
(a) Pincode
(b) Country name
(c) State abbreviation
(d) All of the above
Answer:- d. All of the above
For displaying any type of location data, including postal codes, state abbreviations, country names, and custom geocoding, maps are a no-brainer. If the data is related with geographic information, maps are a simple and effective approach to illustrate the relationship.
There should be a correlation between location and the patterns in the data. Such as insurance claims by state and product export destinations by country, automobile accidents by postal code, and custom sales areas etc.
5. A scatter plot displays several unique data points:
(a) on a single graph.
(b) On two different graphs
(c) On four different graphs
(d) None of the above
Answer:- a. on a single graph.
Scatter plots are a useful tool for examining the connection between many variables, revealing whether one variable is a good predictor of another or whether they tend to vary independently. A scatter plot displays several unique data points on a single graph.
State True or False
1. Data visualisation enhances the effect of communications for the audiences and delivers the most convincing data analysis outcomes. Answer:- True
2. Visualisation allows to interpret large volumes of data more quickly and effectively at a glance. Answer:- True
3. 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. Answer:- True
4. Scatter plots are a useful tool for examining the connection between many variables, revealing whether one variable is a good predictor of another or whether they tend to vary independently. Answer:- True
5. Gantt charts represent a project’s timeline or activity changes across time. Answer:- True
Fill in the blanks
1. Data and insights available to decision-makers facilitate decision analysis.
2. Often confused with data Visualisation, data presentation architecture is a much broader skill set.
3. A pie chart (or circle chart) is a circular graphical representation of statistical data that is segmented to demonstrate numerical proportion.
4. If the data is related with geographic information maps are a simple and effective approach to illustrate the relationship.
5. Density maps indicate patterns or relative concentrations that might otherwise be obscured by overlapping marks on a map, allowing to identify areas with a larger or lesser number of data points.
Short essay type questions
1. State the objectives of Data presentation architecture (DPA).
Answer:-
Answer:-
3. Define the concept of data Visualisation dashboard.
Answer:-
4. Write a short note on bar chart
Answer:-
5. Write a short note on density map.
Answer:-
1. Discuss the ways in which the finance professionals may be helped by data Visualisation in analysing and reporting information.
Answer:-
2. Discuss the objectives of data Visualisation.
Answer:-
3. How to use data Visualisation in report design?
Answer:-
4. Discuss the different tools for Visualisation and Graphical Presentation
Answer:-
5. Discuss the objectives and scope of data presentation architecture.
Answer:-
Unsolved Case
1. Maitreyee works as a financial analyst with a bank. The departmental meeting with her managing director is going to happen very soon. Maitreyee is entrusted with the task of preparing a dashboard that will cover the performance of his department during the past quarter. She wants to prepare the dashboard in such a way, that it should not look cluttered, but at the same time, it covers all the available information in a visually pleasing manner.
Discuss the different approaches Maitreyee may adopt to meet her objective.
References:
● Davy Cielen, Arno D B Meysman, and Mohamed Ali. Introducing Data Science. Manning Publications Co USA
● Cathy O’Neil, Rachell Schutt. Doing data science. O’Reilley
● Joel Grus. Data science from scratch. O’Reilley
● https://go.oracle.com
● https://sfmagazine.com
● https://hbr.org/
● https://www.tableau.com
● http://country.eiu.com
● https://en.wikipedia.org
● https://vdl.sci.utah.edu
● https://towardsdatascience.com
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"Koncept perfectly justifies what it sounds, i.e, your concepts are meant to be cleared if you are a Konceptian. My experience with Koncept was amazing. The most striking experience that I went through was the the way Yash sir and Ruchika ma'am taught us in the lectures, making it very interesting and lucid. Another great feature of Koncept is that you get mentor calls which I think drives you to stay motivated and be disciplined. And of course it goes without saying that Yash sir has always been like a friend to me, giving me genuine guidance whenever I was in need. So once again I want to thank Koncept Education for all their efforts."
"Hello everyone, I am Kaushik Prajapati. I recently passed my CA Foundation Dec 23 exam in first attempt, That's possible only of proper guidance given by Yash sir and Ruchika ma'am. Koncept App provide me a video lectures, Notes and best thing about it is question bank. It contains PYP, RTP, MTP with soloution that help me easily score better marks in my exam. I really appericiate to Koncept team and I thankful to Koncept team."
"Hi. My name is Arka Das. I have cleared my CMA Foundation Exam. I cleared my 12th Board Exam from Bengali Medium and I had a very big language problem. Koncept Education has helped me a lot to overcome my language barrier. Their live sessions are really helpful. They have cleared my basic concepts. I think its a phenomenal app."
"I cleared my foundation examination in very first attempt with good marks in practical subject as well as theoretical subject this can be possible only because of koncept Education and the guidance that Yash sir has provide me, Thank you."
