How can data visualization improve decision-making in any domain?

In my experience, data visualization is an indispensable aspect of data analysis. Without it, the analysis process feels incomplete. I've found that tailoring the presentation of insights to the specific audience is key. For certain stakeholders, a high-level overview suffices, while others require a more detailed breakdown. Even as we strive for simplicity in our visualizations, there are instances where drilling down into the data becomes necessary. Personally, I've witnessed how data visualization can truly engage and persuade the audience to grasp and believe in the analysis. The visual appeal and aesthetics of data visualization play a crucial role in effectively conveying information.

In the realm of data, "A picture is worth a thousand words" probably translates to a "Visualization worth a million rows in of a spreadsheet". This reflects how a great visualization compresses data and uncovers many more complex dimensions of it simultaneously.

With data visualization, we can gain a more in-depth understanding of complex data sets, identify patterns and trends for making more informed decisions. Few ways data visualization can improve decision-making: -help identify patterns and trends that might be difficult to spot. -help in understanding the relationships between different variables. -help spot outliers and anomalies that may be important to consider, making it easier to spot correlations and causal relationships, which can be useful in making predictions and planning for the future. Complex information can be easily communicated to others such as stakeholders or colleagues, in a clear and concise way, which then enables better collaboration and decision-making across teams.

In my experience data visualisation is essential for portraying a clear message or story to your audience. Clean and clear visuals allow team members to interpret data in a way that is recognisable to the larger workforce as opposed to numerical formats making it a more interpretable method of showing insights.

I think data visualization provides greater insights on analysed data stored in the data warehouse systems. Data visualization provides make over of the raw data in the DW systems.

Proper labeling is critical for data visualizations as these are often standalone and the audience will have to be able to interpret them quickly and independently without being able to ask clarifying questions or reading additional text like they would in a report. Can the audience identify the accurate meaning (and unit) of each bar, line etc.? Does the audience know what the chart is about, in general? Additional visual effects can be added to point out interesting trends, differences or relationships in the visualization to clarify it even further.

Time! For every dataviz I've made that's electrified an audience, I've created 20-50 absolute pieces of crap. But it's through that process you can start to imagine, see, and then finally realize the findings in your data *and* how best to share them with people who your specific audience. You need to build time into your teams and orgs for -- as well as respect for -- failures in all of our processes, or we never really succeed.

To sum up, these 4 points are the key challenges: 1. Dealing with internal and external aspects 2. Evading the common risks and issues 3. Proofing the feasibility and readability of the data visualizations. In addition, here's the fourth, which I suggested: 4. Availability of the manpower, resources and tools to develop the data visualizations

Data visualization can also support decision making by using auto insights, storytelling, and advocacy by presenting data in a compelling and engaging way using both AI/ML and Predictive analysis.By providing easy-to-understand visual representations of data including pattern and authentication with secure digital tool, it helps employees make more informed decisions based on that data and analysis.

Briefly: 1) Being able to anticipate the level of familiarity with the data 2) Avoid reading ambiguity, since most companies do not have mature data governance 3) Being able to identify and manage political sensitivity 4) Adapt visualization according to each person's style, as there are people who are more deductive, others who are more intuitive 5) Select data according to context, as depending on the position or situation, people maybe more focused on the next steps than on understanding the context

I was quite surprised not to see Python here, which I believe is the most widely used platform for visualizing data in the daily data science workflow. From general visualization libraries like Matplotlib to Seaborn and Plotly to a series of specialized libraries, like NetworkX for graphs, Kepler.GL for geospatial data or Datashader for large-scale data sets.

To identify which tool is suitable and best fit to be used for data visualizations, the following elements should be considered: 1. Capacity and capability of the staff to use the tool 2. Data source to be applied to the tool 3. Difficulty and complexity of the tool 4. Features and customization effect of the tool 5. Licencing and subscription of the tool - applicable for enterprise and/ or company

The programming tools for data visualisation includes matplotlib, seaborn, plotly, bokeh, D3.js, datashader, etc. The non-programmatic tools includes Tableau, Power BI, etc.

As mentioned in the text there are some example tools like Tableau, Power BI, Excel, R, Python, D3.js, and Google Charts, besides the above mentioned tools. There are other tools like Qlik, Chart.js, Good Data Studio and other data visualisation tools available on AWS and other cloud platforms.

Power Bi tends to be the tool with the greatest adoption, given that the vast majority of people already have some familiarity with Excel. But for data scientists using Python and R provides a greater range of options and time gain

In addition, the following resources can be searched to inspire you on examples of data visualizations: 1. Repository 2. Consultancy 3. Industry/ Business 4. Online platform 5. Newsletters/ Magazine/ Conference

Presenting data in the form of charts or any other pictorial format like pie chart, stack bar chart etc helps in better decision making in any research. A study must include a qualitative approach/ question along with quantitative which can further be presented in attractive word clouds with positive or negative sentimental/ emotional attributes behind it. With so much advancement in AI, we can get a lot more out of data and conclude on the final results easily and concisely. Instead of focusing on gathering too much information from sample, researcher should aim on setting up small surveys etc that will provide us even more fruitful information and and that too in a budget. Thanks Rahul Mittal

Finance industries like Investment companies, Banks, Insurance, and Asset management companies can benefit with data visualization. These companies deal with vast amounts of financial data and they are a good example of data visualisation.

As a marketing analyst, here are some examples of data visualizations that can demonstrate the power of data visualization in our field: ?? Marketing Funnel: Visualize the customer journey. ?? Website Traffic Trends: Uncover user behavior. ?? Customer Segmentation: Target effectively. ?? Competitive Radar: Measure against rivals. ?? ROI Dashboard: Allocate resources wisely. ??? Customer Journey Mapping: Personalize touchpoints. ?? A/B Test Results: Enhance campaigns. ?? Market Research Infographic: Simplify complex data. These visuals empower to make informed decisions, optimize strategies, and communicate results effectively. #DataVisualization #MarketingAnalysis

Agreed with all 6 points above, summarised into 2-3 words below for each point: 1. Goals and persona 2. Visual selection 3. Design principles 4. Contexts and formatting 5. Recieving comments 6. Join data challenges

Don't get caught up on fancy or highly technical methods for data visualization. Ask yourself: what will be the simplest or most engaging way to communicate an insight? A data visualization is a story and should be told briefly.

When designing a visualisation, one of your guides should be accessibility considerations for the audience as you might not be aware of accessbility challenges your stakeholder you are building for might have.

Understand stakes Understand data Understand requirement Understand source Understand 5 y's Understand Ability Understand intent

Understand audience is key: 1) Identify the level of familiarity your audience has with the data 2) Make sure that your metrics, groupings and segmentations meet the mindset of your audience and the context studied 3) Understand which approach is most likely to be successful: deductive, inductive or chronological 4) Use gestalt principles in your graphs and spreadsheets to help with visualization

Nowadays what you are producing is not important as how you are producing.. with the help of data visualisation tools it's much easier than before..

One of the biggest things that has impacted my work is to what extent leadership is interested in data use and visualizations.....

It is necessary to conceptualize before conceiving, thereofore to know the intent behind the data is key to successful design