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It takes a lot of data to lead to last-mile visualization. With effective data visualization examples, you can create visuals that influence decision-making.
Have you come across the phrase human beings are visual? Now, this is where data visualization comes in. Organizations are spending a lot of money on research but end up making losses at the last mile.
But who does data visualization benefit? Why does it matter? If you are looking for effective data visualization examples to use in 2022, here’s everything you need to know.
We will look at data transformation and how to use storytelling in data visualization. In addition, we will explain what it takes for data to lead to last-mile visualization. Let’s dive in!
Data is an essential part of the growth of an organization. But, raw data can be boring to read. No matter how much value it has to offer, people will always skim through to the end.
To counter this, you will need data visualization tools. Data visualization is about representing data using visuals such as tables, graphs, and charts.
The importance of visualization is that it helps to communicate trends and patterns that can quickly go unnoticed. For a researcher, for instance, it helps present your research clearly and professionally.
Data visualization is not as easy. Most people lose potential clients due to data transformation and representation. Remember, the end goal should trigger your audience to take action.
A good data visualization should involve storytelling, creating visuals, and understanding your audience.
As a data analyst or researcher, you do not want to miss any details when transforming data. Data transformation is the process of changing the format, structure, or value of the data.
For data analytics projects, for instance, data transformation can be in two stages. These are:
Data mapping: It is the process of matching fields and data from one source to a final destination. It involves assigning elements from the source or system towards the goal and capturing all the transformations in between.
The purpose of data mapping is to bridge the gap between several sources and the destination. One of the success factors in data mapping is quality.
However, mapping can become complex when dealing with complex transformations like many to one or one to many relationships.
Code generation: This stage is where the actual transformation begins. As a result, the data map specification is used to create an executable program that runs on computer systems.
In addition to the above stages, there is also visualization. The stage links data to its last mile. As earlier mentioned, data without visualization is bare, and therefore it needs to be engaging.
From the phrase last mile, you can already guess what it means. In data visualization, it is the final destination of any process you want to achieve a final goal after making an effort.
So, let us look at several examples to help you understand how the last mile works.
In a transportation network, the last mile describes how difficult it is for people to get from a transportation hub to their final destination. It can be from the airport or a train station.
Further, in sports, an athlete running in a marathon competition wants to realize his achievement by completing the last mile. Despite the emotional and physical exhaustion, the athlete is determined to finish the race.
As you can see from the examples above, the last mile is about the final destination after putting in an effort. It represents the final step, where perceptions translate into outcomes that help drive value.
Now, when it comes to data, this is where visualization applies. The human brain is quicker to process visual information than any other form of data. Remember, this is the main benefit of data visualization.
It helps to communicate information much faster. It also assists your audience to capture the crucial part of data. For instance, using a chart to summarize complex data makes it easier to understand relationships than using reports and spreadsheets.
Data visualization results in clear communication and helps business leaders to interpret and act on the information quickly.
Big data visualization tools provide real-time information, which makes it easier for stakeholders to evaluate enterprises. It helps to create a competitive advantage for industries in responding to market changes and identifying new opportunities.
Have you been wondering what it takes for data to lead to the last mile? Let’s start by defining what the last mile of analytics means.
As a business, organization, or data analyst, presenting information for final decision-making is very crucial. Quality is essential in data transformation.
The bridge between data and insights communication to the decision-makers tends to be missing or weak. Your investment and efforts can quickly go to waste without an effective delivery plan to the end-users.
Have you thought about your reporting interface? It is a narrow gap to rely on, especially for the last mile of business intelligence. The last mile emphasizes the need to visualize and communicate insights better.
It means that you can execute data that is intuitive, friendly, and simple. In addition, you need to sell it to your audience. By selling it does not mean that you ask people for money.
It means that you want to make it easy for people to access your work without putting too much effort. There is no need for you to make assumptions or rely on instincts. What you require is to convince your audience that your data product is worth giving their attention.
Here is an example. You have a client working with a global manufacturer who has an interest in becoming more data-driven. The client has invested in a data warehouse and has new dashboards.
You have to help the client create a comprehensive plan that ensures that the data has an impact. So, here is how to go about it:
If the data leadership team recognizes that technology and design are not the complete answer, they will have to change the culture and attitude of the organization.
Before we begin, let’s have a look at an example. Over the years, while watching sports on TV, you may have observed multiple races. Have you ever noticed how incredible the athletes keep pushing despite being physically, mentally, emotionally exhausted?
If you ask the athlete about the ‘last mile’ experience, they can confirm that it is the most exhausting part of the race. At the same time, it is the most stimulating and worthwhile.
It takes them months to plan, train, and get ready. Now, coming back to analytics, the strategy is similar.
After collecting the data, you must combine it and prepare it before carrying out an analysis or modeling it. Data collection activities are time-consuming.
An estimate shows that they can take up to 80% of the time and effort. With analytics, the last mile represents the final stage where insights translate into results that drive value.
If an organization is lucky to reach the final stage of the analytics process, failing to cross the lines means that all the previous work was a waste.
Most organizations topple and fall in the last stretch. As a result, they never realize their expected return on the data investment.
The last mile is the gap between data and the changed behavior associated with the answers. ChartExpo is a data visualization tool that helps to create insights from a spreadsheet.
ChartExpo has helped in coming up with visualization solutions to achieve insights. However, the challenge with most tools is that they cannot deliver effective visualization that the end-user can comprehend.
Lack of customer knowledge concerning the needs, expectations, and requirements on using data is the major contributor? But why is that?
Most of the successful products are as a result of discovering the customer persona and the customer journey. It has helped them gain exponential growth and a competitive edge in the market over their competitors.
User adoption can make or break your data initiatives’ success. For better results, ensure that your audience has an easy flow. Although it’s never that easy in the real sense, your solution should help people make better decisions.
If the end-user is not aware of your design approach, then you are not ready for the last mile. In addition to the technology and data considerations, you will have to factor in user experience.
The consideration elements are training, usability, visualization, workflow, incentives, and accountability. Think about the context to put in the analytics.
There is a lot to put in place to help you prepare for the last mile adoption. If you consider taking a holistic approach to the last mile, you are more likely to finish the race.
About 80-90% of failure in data-related projects is a result of the analytics race. While most of the data initiatives start strong, only a few make it to the end.
Instead of having the data initiatives fail in the last mile, consider starting with the end in mind. If you can identify the value-generating decisions and optimize them upfront, it can be easier to discover a clear success path.
Explore how the end-user will implement the data and how it integrates with existing workflow, applications, etc. The smoother the flow, the more your data initiatives get adopted.
Most big companies like Apple listen to their audience before creating intuitive solutions. Some take it further by adding features they never knew they needed, thereby creating an intuitive effect.
You can apply a similar theory in data by formulating a unique story that addresses the user’s needs. Storytelling relates to data visualization. However, there is a slight difference.
Storytelling considers the available data and appropriate logic to enhance the user experience.
The analytics distribution process is quite comprehensive. Here is how organizations define and plan the analytics:
A data owner is someone responsible for the data set. It’s the executive role that heads the department, team, or business unit owning the data asset.
It is the process of importing, transferring, loading, and processing data for later use. When it comes to a database, the process is referred to as ingestion.
Ingestion involves loading data from several sources. You need to alter, modify, and format it to fit large documents.
It is a central repository of business information that provides data management, protection, and data sharing functions through system connections using APIs.
There are other essential aspects of an enterprise storage system that have unlimited connectivity and multiple platform/ environment support.
Data analysis is the process of inspecting, transforming, and modeling data to discover useful information. The information can help with decision-making.
Data visualization is the representation of data using charts, graphs, and other visuals. It communicates the relationship of data using images.
It is crucial as it increases the visibility of patterns and trends. It’s at this stage that the data owners realize the return on investment on data distribution.
Problems arise after realizing that the insights delivered cannot be understood or the data can’t solve use case problems. You can only channel more investment into data analysis and visualization as you hope for better results.
You can customize the final results on a dashboard to provide a range of visualized data for the user. You can also create manual reports using PowerPoint to include data with a narrative to improve understanding.
The solution to a problem does not lie on the tool but rather the concepts needed to create a perfect data story that meets the users’ needs.
The first tip when working with analytics is to understand how to proceed with a particular scenario. It also helps to work with a given set of data.
Remember the definition of visualization? Last-mile visualization is the process of graphically representing data to determine its role in the data story.
Data storytelling is not a walk in the park. It requires you to invest time and resources. The first step is to determine the story, choose a platform or tool to act as the story’s vessel then conclude your analytics journey.
Last-mile visualization helps you to get results from data and come up with a final goal.
Here is a second tip on what it takes to Lead till Last mile Visualization. Data visualization helps to solve complex data. When you are in your last mile, you are already exhausted. Visualization helps you get better results.
Visualization helps to:
i. Emphasize data storytelling and visualization.
ii. Make it easy for the audience to understand your work.
iii. To increase accessibility even for non-technical people.
Now, let’s have a detailed look at each:
Data storytelling or building a narrative around a data set and visualizing it is very crucial. It is very critical when communicating the meaning of your work to your audience.
Whenever possible, make sure you include a visual representation of methods and results. Using informative graphics, flowcharts, and other visualization forms tends to be more engaging than text-based data.
The benefit of using data visualization is that it allows users to track connections and monitor business performance. Being able to find a correlation between business functions and market performance is essential for a competitive environment.
For instance, you want to keep track of your conversions. You can discover an element that is not performing well. It can be an ad description, landing page, ad heading, or bidding.
Therefore, you will have to perform an analysis and remove the hurdle ASAP.
In the online space, the audience wants something quick to skim through. The attention span is too short. Media publications understand this too well, and therefore, they make use of attractive headlines.
However, most of the data science works have not adopted the strategy. Even the most curious leader has a few minutes to decide if they are interested in your piece.
A data folio should be easy to skim through and convey essential data. The highlights section gives readers 10 seconds to decide whether to continue with the rest of the content.
More data science work is published and consumed by people who are not tech-savvy. The data folio helps to interpret the data in a format that is easy to understand for people with no technical knowledge.
Why is data storytelling important? It helps to:
One of the tricks to have in mind is that everyone is a novice in a new field. The end-user will be a novice when it comes to understanding data and using it.
With the learning curve in mind, it will prevent users from neglecting your content. It gives them time to explore more.
ChartExpo is a premium data visualization tool with a 7-day free trial period. It is a drag and drop tool that helps create different visualizations like data reports, maps, dashboards, etc.
ChartExpo allows users to save visuals as PNG and JPG, and you can attach them to Google Sheets and Excel. It is suitable for adding charts and maps to a spreadsheet.
After importing data into a spreadsheet, you can create data with a single click. Types of visualization include column, line, and bar charts, donuts, area charts, scatter plots, Sankey, NPS charts, symbol maps, sentiment charts, etc.
After visualization, you can import the visuals in different formats. You can use charts for data analytics.
Let’s say you are running a Google Ads campaign, and you want to monitor the performance in terms of days. You can disable the days that are not performing.
You can choose to save money by pausing an ad. You can copy the data from the Google Ads count and paste it into Google Sheets. However, it can be time-consuming.
To save you time, you can use the ChartExpo by PPCexpo. It helps you draw charts with no prior coding skills. ChartExpo has a variety of charts.
Let’s take an example of an adjacent bar chart. If you have data of keywords with different CTR and CPC, ChartExpo provides a very good visualization to present that data.
If you need to see the results in the form flow then Sankey is one of the good visualizations that ChartExpo has. E.g. data of impression shares of keyword match types on different devices could be a good option to represent with this visualization.
You can create a multi-axis line chart like the one below if you have data of impressions, clicks & conversions over different dates.
A Radar chart is also the best option to visualize results for any metrics e.g in the below image Avg. CPC and Clicks are showing with respect to days of the week.
So every visualization has its own way of presenting the data and giving you an idea about the insights. For example in the radar chart above, you can easily find which day of the week remained best for you with respect to clicks and which day of the week remained good for the average CPC.
You are not bound to stick with these visualizations, as there is plenty of data being generated on daily basis in different domains ChartExpo has a list of different chart types which can help you to visualize your data for your own analysis as well to present to different stakeholders.
Organizations are rethinking the last-mile analytics based on several factors like intense competition, urbanization and service failures. Developments using a data visualization tool helps to curb these challenges.
To visualize the data, you need to have powerful visualization. It helps you visualize crucial data creatively and have the confidence to implement a solution based on the analysis.
ChartExpo is a data visualization tool for creating interactive charts for spreadsheets. It works with spreadsheet data, and the output is in the form of PNG and JPG. The data sources are Google Sheets and Excel.
There are several chart types, like Treemap, Pie, Pareto, Sankey, Likert, and Scatter chart. You can always customize them to meet your goals.
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