Unit 6: Data Reporting and Visualization
Analysis is useless if it is not reported in a way that helps management teams make decisions. Data reporting is the process of collecting and submitting data. Data visualization is putting data into a chart, graph, or other visual formats that help inform analysis and interpretation. Great definitions, but what does that truly mean? While we are becoming more connected by reporting the "numbers" of the world, how do we show that those numbers are assisting in our growth as humans? How we develop and utilize machines is where that power comes into play. The "seeing" or data visualization of the numbers allows more people to understand complex issues, participate, and contribute. To easily digest information, reports must be created that cover the right content and be formatted in a way that makes the results of the analysis clear. Data collection and exploitation is the "science" of analysis, and reporting and visualization are the "art".
Completing this unit should take you approximately 15 hours.
Upon successful completion of this unit, you will be able to:
- evaluate visualization products to determine how effectively they express meaning gleaned from data;
- explain the increasing ways in which human decision-making is converging with artificial intelligence to improve both; and
- describe the critical elements of reporting that clearly communicates analytic estimates to decision-makers.
6.1: Effective Data Visualization
There is a fine line between finding a data visualization tool and finding the "right" one. The database, process, and infrastructure must be in place before a data visualization tool is of any value. There must be a plan of action for the relevant data extraction to make an informed decision.- If you are selecting or building a tool, it is important to understand its strengths and weaknesses, the types of visual outputs you require, what it can produce, the users' skill level, and the analytics it can perform. This article will speak to understanding and evaluating those capabilities.
6.1.1: A Picture Really Is Worth a Thousand Words!
What does this phrase mean to you? While it can be asserted that complex thoughts and meaning can be construed with one picture in our world of data visualization, how you represent that meaning is vital. Know your data, know your message, and keep it simple. Do you convey this when you take a picture? What about when you are creating a graph?- Data can be daunting, especially big data, with dense sets of numbers that can swim before our eyes when we try to use raw data to look for patterns or anomalies. When we add visualization, we can easily tell the story of data so that even a child can understand it. Take note of the speaker's point about proportion. We must be careful about how we set our programs to visualize data. The kind of data we visualize and how we structure and organize it will have vastly different findings. How have you created or used data visualization in your work or studies? Have you been sure to look deeply into the numbers presented to be sure you are showing an accurate picture of what needs to be expressed? Have you ever experienced "the dataset changing your mindset?"
Utilizing VR, AR, and MR for Big Data Visualization has potential with some clear advantages and disadvantages. Graphs and tables developed in this method are powerful storytelling tools and can offer new critical data visualization components in business intelligence. What role do you think MR will play in the development of data visualization?
6.1.2: Interpreting and Evaluating Visualizations
Modern reporting and dashboard tools allow subject matter experts to quickly assemble compelling visualizations that take these insights and exploit our brain's capacity to rapidly assimilate visual information. How much of this skill set do you think can be automated, and how?This video highlights that business intelligence is evolving into analytics thanks to machine learning and visualization tooling advances. Analytics takes a more holistic and proactive view of a business by automating the process of extracting useful insights and looking to predict the future rather than affirm the past. Why is this key?
By classifying business intelligence appropriately, we allow ourselves to spot opportunities for investment and exploitation, increasing our ability to turn the data and insight we collect into profit. Business intelligence and its research can be divided into a taxonomy. This paper breaks that down. Even without data, are there areas that may contain similar opportunities?
Data visualization is both an art and a science, taking descriptive statistics and making them engaging and a jumping-off point for new questions. One of the leaders in the field, Edward Tufte, summarised the main principle of good data visualization as "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphics reveal data. Indeed graphics can be more precise and revealing than conventional statistical computations." Think about your favorite sport and how the graphics reveal more than just the score.
This paper discusses how graphics reveal data features that statistics and models may miss and why this is important. The paper also points out that we often accept graphics unquestioned as truth when sometimes they are incorrect or misrepresent the data by not giving the full perspective. Graphics raise questions that stimulate research and suggest ideas. After reading this paper, look at graphs used in news reports online and see if you can find at least one that fits each of the issues named.
6.1.3: Excel and Other Visualization Products
There are a host of products and tools available to visualize your data. As the technological innovation rate progresses, the need for flexible and intuitive tools will increase. What aspect of data visualization tools excites you?
- Clean data is essential for impactful and reliable analysis and visualization. Before building an analytics pipeline or a dashboard, you should take the following steps in this detailed tip guideline. Consider how you would clean raw data in your industry using the best practices outlined. How would you identify this data?
This extensive listing of tools should be explored in-depth to apply some practical skills for creating your own data visualization tool.
Data visualizations are different from infographics. However, both allow you to use the available data and transform it into a compelling presentation or powerful story. These products are highly shareable on social media; when well done, they can give more mileage to your content.
Most social impact data is in textual formats. This creates or magnifies problems with reporting on the progress of social change initiatives. Visualizations allow you to efficiently communicate complex situations to stakeholders, be transparent with your analysis, and build trust in your insights. Watch this video. What questions would you use to achieve insights for funding?
Good data visualization uses different visual characteristics (color, size, orientation, etc.) to encode information effectively at higher densities than in plain text. Testing your visualizations with real users during the development process is important. This testing should focus on measuring the expressiveness and effectiveness of the presentation. How might people with a visual impairment be included in the development process?
6.2: Humans, OLAP, and AI
Humans aren't going anywhere. According to current research, we will likely still be needed for approximately 80% of complex problem-solving work. And it's likely not until 2035 or beyond that AI will be undertaking over 50% of that work. The next stage in this integration of learning is Explainable AI, helping humans understand how AI came to a particular recommendation.
6.2.1: On-Line Analytical Processing (OLAP)
As machines become smarter, humans will too. Current research shows that young toddlers' motor, visual and other intuitive skills have changed due to their interaction with machines (mobile, tablet, Alexa, or other smart devices). So, will machines ever be smarter than humans?OLAP systems allow flexible and dynamic questions to be asked of big data. By combining OLAP with multicriteria decision-making techniques, we can allow business executives to incorporate insights from real-world data into the systematic evaluation of different business options. This improves the quality of complex decisions and leads to better business outcomes with the same resources.
OLAP allows complex, multidimensional queries over large datasets to be rapidly answered. An OLAP system models the world as facts representing quantitative or qualitative measurements of things of interest. This article defines the different types and their pros and cons.
6.2.2: How Can Machines and Humans Work Together?
For the full benefits of technological advances to be gained in society, a collaborative approach to machines and humans working together must continue to be paramount. For generations, humans and machines have worked together. Why would it stop now? While humans will continue to offer creativity, social skills, and qualitative aspects to the partnership. Machines will bring quantitative aspects, speed, and the ability to scale rapidly. So far, the nuances of language remain outside machines' grasp, while quantum computing is nearly impossible for humans. By combining forces, true innovation is bound to happen.
Anytime a new phrase of AI/ML innovation occurs, some people instantly claim that robots will replace us. One counterargument to this armageddon scenario is that it will not happen because of language nuance. However, GPT-3 is now on the scene. Read this explanation. Does it make you think we will enter some "brave new world"? What challenges and opportunities do you see this technology presenting?
This five-minute video gets to the core of explaining the newest AI technology in learning human language and characteristics. How long before GPT-3 is not recognizable as a computer?
This short video highlights some examples of careers that may disappear by being replaced by AI. How about parking garage attendants? Why do we need someone to hit a button to make the barrier arm go up once we pay, especially when we can only pay with a card? Although the friendly wave and smile and hearty wish for a nice day from that same attendant have not yet been replicated by computerized voices. What do you think about the concept of employment in the future?
6.2.3: What Happens to Humans When Machines Learn Faster?
Algorithms are based on rules, which could present areas of mischief for machines when they learn faster. If the algorithm is built on a false piece of data and the output grows exponentially in unexpected directions, what does that mean for humans? Are we done? Do we lose our place as the head honchos? Given the advancements of AI in recent years, going from beating us at Chess to Go, what do you envision?
"The use of OLAP data cube models for psychometrics opens the door to complex and dynamic uses of that data. This paper asserts that data cube modelling would allow larger, aligned, and integrated datasets to be constructed that could be used to build knowledge graphs or feed machine learning systems". Consider what this means and list some opportunities afforded by applying psychometric criteria to classifying content in e-learning systems that could improve your education.
To get to the crux of how the future workplace will be recast by robotics and AI, we need to consider how the next level of change will be more philosophical, sociological, and ethical. Considering what you know now about our present world situation, what do you consider the biggest issue when expanding the use of AI and robotics in the workplace?
- There are some thought-provoking points made in this video. What implications do you think the concept of "containers" holds for humans' intellectual growth? How do you think this idea will change your thoughts?
After reading and reflecting on the results of this trends survey, are you fearful or hopeful? While the expert participants offered their highly valued insights, do you agree or disagree? What areas do you believe should be added to their list of concerns and potential solutions?
As AI capabilities and ubiquity are extended, humans must learn how to work with it and ensure that its influence on human well-being is positive. We must ensure that human judgment maintains importance and that we are up to the task. There will be immense economic pressure to adopt AI, and we must train a new generation of data scientists and data science users who can guide this adoption for humankind's benefit. Educators should explicitly consider these pressures and a world where data science is more central when designing student curricula. A highly specialized US agency senior official recently stated that the humanities will need to be taught more than ever, even as the world pivots to STEM (Science, Technology, Engineering, and Mathematics) education. He says we will need historians and philosophers, psychologists and sociologists to ensure engineers do not just build technologies because they can. Someone needs to be sure that the should". How do you feel about the use of AI in early education? What should other kinds of higher education and thinking be encouraged to support this effort?
6.3: Producing Meaningful Reports
How your reports are written, including content, form, beautiful data visualization presentation, and utilizing a framework such as the SMART model to showcase your goal setting with robust data, will surely set you apart. Don't forget the other core responsibilities of your position and the characteristics of those you report to. They all play a role in how much of an impact your report has.6.3.1: How to Write a Report that Influences Managerial Decisions
Management information systems (MIS) provide the context and background knowledge to support executives in making complex but important decisions.The model described in this paper connects management information to positive changes in four categories of metrics for decision-making.
When leading a well-known organization, their leadership style is often studied and reviewed with every action and activity they undertake. People like Steve Jobs, Indra Nooyi, Ursula Burns, Warren Buffett, and Jack Welch have diverse skills that became significant at different levels and can be broken down into three general types: technical, human relations, and conceptual skills. Based on the responsibilities outlined and organizational structure, at what level is your managerial skill today? Which areas do you feel are your strongest and why? What are you lacking, and what methods can you deploy to upskill to aid your company and personal growth based on the critical thinking questions at the end of this paper?
Managers uphold the responsibility for decisions and actions made in a company. To achieve the most positive outcome, managerial performance is key to accountability. How managers leverage their ability to negotiate and motivate teams is just one area covered in this overview. The SMART model is a good framework (specific, measurable, achievable, realistic, and time-targeted) for goal setting. How many of a manager's roles have you already taken on? Which ones interest you, and which ones would you prefer to avoid? How can you adjust your management style within the confines of your organization to enjoy managing your teams and using your best skills most of the time?
6.3.2: The Art of the One-Page Memo
The ability to impart valuable knowledge with few words is not easily managed. It should be a solid skill for most people in our world of emails, social media messages of 240 characters, and less with infographics. As your average person's attention span diminishes, how will and do you succinctly convey information?
Writing effectively for different circumstances is key in business communication, which must be objective and avoid subjective preferences as these "words" may have legal standing. Look at Table 2.3, which provides a small chart checklist to aid your writing in the future. Try the exercises at the end, as well.
A degree of technical writing is needed to communicate a specific purpose within a company. This is what memos offer, as you can see from this breakdown of the format and example. To hone your skills, write a letter using this as a guide, being mindful of these tips.
Mass communication takes many forms in business. Memoranda and letters are two generally used in an official capacity. Watch this brief video to understand when they should be used, how, their format, key components, and their differences. Have you ever written one? Pick a topic and practice writing a one-page memo about a policy change in using the office break room to improve social distancing. Remember, it will be posted for all your colleagues to see.
6.3.3: BLUF (Bottom-Line Up Front) instead of BLAB (Bottom Line At Bottom)
Are your emails 150 words, or do they read like a novel? For effective communication in business and to save busy executives time, writing a conclusion first concisely provides the topline information without them having to wade through a novel to get to the point! If you need to include extra information, number the topics or use bullets to separate the important information. Alternatively, inundate their inbox with multiple emails with topic-specific subject lines. This strategy depends upon your manager's patience and organizational style.
BLUF is the acronym for Bottom Line Up Front, a method of placing conclusions at the beginning rather than the end. The alternative is BLAB (Bottom Line at Bottom); most managers hate this.
6.3.4: Evaluating and Expressing Confidence Levels
Just how confident are you? Population size, sample size, and percentage are three factors that determine the confidence interval size for a given confidence level. The larger the sample, the more likely it reflects the whole population.
This paper breaks down how to calculate the confidence interval, also offering an alternative approach with Error Bound. Take your time and work through the provided case studies, as that will aid your understanding of all the computations.
If you estimate some parameter, a confidence interval is a range of values of that parameter for a specified degree of confidence (called the 'confidence level'). If you're estimating the mean mass of an apple based on a basket of apples, what would a 95% confidence interval tell you about the range of masses?
Some newspapers and journals will use the phrase "margin of error". Other reports will not use that phrase but include a confidence interval as the point estimate plus or minus the margin of error. A confidence interval is another type of estimate, but instead of just one number, it is an interval of numbers. It is the range. These are two ways of expressing the same concept. Watch this video for a clear and concise explanation of the confidence interval. Take the short quiz at the end of the article to solidify your learning, although you won't be graded.
Study Guide: Unit 6
We recommend reviewing this Study Guide before taking the Unit 6 Assessment.
Unit 6 Assessment
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Take this assessment to see how well you understood this unit.
- This assessment does not count towards your grade. It is just for practice!
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