What Happens When Data Lacks Context?

On the surface, data is just a collection of numbers and oftentimes, complex information. Only when data is provided in context does it become relevant. Take, for example, an online search in the U.S. for the word “Pie.” If we look at the results, we see that this search spikes considerably during the month of November every year. At this point, you may be thinking to yourself, “obviously, searches for pie will surge around Thanksgiving.” However, this is where context comes into play. If you grew up in the United States, you probably know that Thanksgiving occurs in November and that pie is frequently served as part of the celebration. Therefore the increase in pie searches is directly related to individuals planning their Thanksgiving festivities. However, if you were raised outside the United States, you may lack the cultural reference to understand why pie searches in the United States surge around November and reach an entirely different conclusion (and decision) on why that spike happens.

With the proper context, choices may be made based on the data and what the data means. In our pie example, the CMO of a fictional pie producer would be armed with data on traffic behaviors (data) and topical interests connected to those habits (context). Taken as a whole, this data can support an ad campaign in which data dictates when and how to contact customers and context informs what they may find relevant and intriguing.

Visualize Data With the Big Picture

When it comes to business choices, the best and most accurate decisions result from having the correct data, which should be contextualized.

Text is instrumental to context, from naming your axes and giving color keys to using data point labels and annotations on the visualization or adding explanatory paragraphs in an article. Without these, data visualizations might be challenging to understand at best or misleading at worst.

Context allows you to accurately interpret the facts, flesh out the broader picture, emphasize key points, direct the reader to the correct inferences, and allow accurate comparisons. They can also anticipate the reader’s interest in an outlier or trend.

So, the next time you want to offer up an effective data visualization, stop and consider the following:

• Can I clarify the information I’m presenting by emphasizing or repeating a point?
• Do I need to further explain less obvious details?
• Is there an anomaly or a pattern that external data can explain?
• Most importantly, can the data help stakeholders make vital business decisions?

Context is critical because it distinguishes between a superficial presentation and genuine data understanding that motivates readers to action.

Conclusion

Visualization is a significant part of any data presentation, but any data presented without context loses most of its meaning. Data is more than simply a collection of numbers. People may draw insights from several sections of a data set or incorporate information from various sources to form an action plan. However, without the capacity to understand the rationale behind data, stakeholders could find it difficult to make informed insights.  

If your business is looking to better understand what your data is saying, schedule a call or start your free trial of Gemini Explore today! We can help you connect the dots, tell data stories better, and get to valuable (and actionable) insights faster. 

The Current Problem

Gartner predicts that total analytics use will increase from 35% to 50% by 2023. However, in a study by McKinsey & Co. they found that only approximately a third of decisions were both timely and of good quality. To make the most of the vast amounts of data being collected, businesses must make more decisions based on data and be confident that they’re using good data to reach the desired results.

Unfortunately, many businesses struggle to make sound decisions based on their data due to a lack of time, energy, and resources to do so. The data that is being collected today is exponentially larger and more complex than ever before, and many analytics tools have not kept up. As a result, many organizations rely on humans to do the lion’s share of collection, analysis, and contextualization of their data. This can result in errors, inaccurate data, and a huge lag in getting projects completed. And that time lag could result in a project ultimately being useless or slow, inaccurate decision making. That could have negative consequences, from a decline in sales to ineffective marketing strategies, and more.

The Solution

What businesses need are new tools that can augment and support their human resources to achieve quick and actionable decision making – a platform that allows them to see the big picture in one place. 

The key to bridging the gap between data collection and meaningful insights is data contextualization – a way to make sense of what vast volumes of data are saying. At its simplest, data contextualization means adding related information to any data to make it more actionable. Doing so allows trends, patterns, and correlations to stand out against a background of context, getting you a lot more value from your data. 

Oftentimes, complicated dashboards and other tools pull data from multiple, disparate sources and the end results require a data scientist (or a whole team) to explain it in meetings, calls, and emails. This can lead to that dreaded time lag as more time is spent explaining data than acting on it. However, data contextualization combined with the simplicity of using one platform, can eliminate the need for hours of human explanation and clear a path for automated, easy to understand insights. Data contextualization can also help teams save time, money, and effort as a team of data scientists is not required to understand the results and it makes complex data relationships easy to understand. 

Conclusion

Gone are the days of stakeholders making decisions based on intuition, historical frameworks, or guesswork. The future of decision making is now, and it’s data-driven. But businesses have to be sure they’re making the leap from data analysis to actionable insights, and doing so with accuracy. Organizations must take on new tools, such as data contextualization platforms to cut down on lags and human error. At a time when decisions need to be made faster than ever, businesses can’t afford to miss out on what their data is telling them. 

If your team is looking to close the gap between analysis and action, reach out to Gemini Data today. We’ve eliminated the complexity of data operation down to a single platform that supports it all, and gives you the data when you need it, at the time you need it. With our platform, you can effectively reduce the cost of data ownership and get your insights quickly and easily.

Data has grown in significance over the years. So much so that just about every aspect of our lives is influenced by how we analyze and understand data. Indeed, big data plays a role in just about every industry and can guide strategic decision making for business. Because data has become so critical, it’s equally important to understand and gain insights from data. And that’s where data storytelling comes into play. Through data storytelling, data can be more accessible and understood by the average person even if they lack data fluency and knowledge.

Because of how popular data storytelling has become, misinformation and misconceptions have proliferated, making it confusing to understand how best to leverage and utilize data storytelling. Since data can drive decision making and determine the course of a business, it’s critical to understand the limitations and myths of data storytelling. In this article we debunk three of the most common misconceptions around data storytelling, helping you better understand data as a whole.

Misconception #1: Every Form of Data Visualization Tells a Story

One common misconception around data storytelling is that all data visualizations tell a story, which can oversimplify data visualization as a whole. While it’s helpful to convert data into more understandable mediums such as graphs and charts, you also have to make sure that you’re communicating everything effectively and accurately. Charts or graphs that are littered with multiple data points can be confusing and may end up hurting your data storytelling efforts. Just because something is visual doesn’t mean it’s telling a story. For example, a pie chart showing sales numbers from the last year isn’t meaningful on its own, as it requires the viewer to parse out data points and reach their own conclusion. This can be just as difficult to understand as showing someone raw data. It’s important to pay close attention to how your data visualization tells a story, and that you provide context. 

Misconception #2: Data Storytelling Is Solely About Visualization

While people understandably gravitate toward data visualization when telling a data story, there are other equally important elements. Alongside strong visualizations, you also need to seamlessly integrate narrative elements that will supplement the graphs and charts that you’ll be using. These elements can add related information to make it more useful, including helping a viewer identify patterns, trends, or relationships. To use the sales pie chart example again, giving your audience more information like the timeframe, previous benchmarks, and anomalies can all tell a more powerful story than just visualizations alone. This can be helpful in deciphering the data that’s being presented and give your audience more opportunities to engage with the data.

Misconception #3: Data are the Characters of the Story

Lastly, data should not be the main characters of the story. This doesn’t mean that data isn’t important but it’s only one part of the equation when it comes to data storytelling. It’s important to remember that data isn’t always the whole truth, it’s often a reflection of our socially constructed world. The reality is that data is capturing human activity or the results of this activity which means all the biases and assumptions of humans are part of the data. For example, the perspective of a historically marginalized group of people such as the homeless or people of color, may not be reflected in data collection. This doesn’t mean you can’t use data, it just means you have to be aware of and account for the ways in which data may not show the whole truth or reality. 

Everything from data collection (decisions about “whose voice is heard”) to presentation (choices about what kind of chart to use, what colors to use, etc.) are made up of human choices that could influence someone’s understanding of what you’ve done. Every decision about collecting or presenting information makes it subjective and therefore, open to different interpretations. The total sum of the parts – not just data – is what makes up data storytelling and the insights gleaned from it.

Conclusion

When it comes to data storytelling, understanding what it isn’t is just as useful as understanding what it is. Thinking all visualizations tell a story, focusing only on data visualization, and thinking that data are the absolute truth, are just a few common misconceptions. While data can be invaluable in many ways, it’s important to keep these myths in mind to ensure you can leverage data storytelling in an accurate, practical, and helpful way. 

If you want to understand what your data is telling you, schedule a call or start your free trial of Gemini Explore today! We can help solve your biggest data challenges, enabling you to understand and share data stories that drive your business forward. By connecting the dots between data from disparate sources, Gemini Data helps organizations effectively transform data into stories. 

Over the past few decades, unit charts have proven to be a critical tool that businesses can use to leverage data for better decision-making. For businesses of all sizes, unit charts are valuable resources that use basic analytics to guide even the most complicated decisions. However, many people don’t understand how they can start using this tool to their advantage and can get overwhelmed learning how to use them.

We’ve laid out a few tips on how to get started using unit charts to simplify the concept and help your business implement them successfully.

The Basics of Unit Charts

Unit charts—otherwise known as pictorial charts, pictogram charts, or picture graphs—are charts that use icons to represent a specific quantity. In the average chart, a single symbol is used to represent a specific quantity of a certain item. A symbol can be denoted as one unit, ten units, fifty units, or any reasonable amount, and it can be used as a point of reference or documentation.

When assigning particular symbols, any geometric shapes, icons, or images can be used as long as they are distinguishable from one another. This freedom to choose can be seen in the most common examples today, such as ISOTYPE charts, waffle charts, and other charts with small symbols.

The Importance of Unit Charts

The logic behind using a unit chart is rather simple. Pictogram charts use icons to provide a more engaging view of small sets of discrete data in totality, making it easier to assess a situation with a bird’s eye view. Through the use of icons, even uninitiated viewers can easily understand it and the data it’s presenting, regardless of how niche the topic is.

Compared to other types of charts, unit charts are great for businesses because simple icons can help immensely with overcoming differences in language, culture, and education. And on top of the widely understood nature of these tools, icons also provide a more representative view of the data so that they have a more engaging impact.

Dos and Don’ts of Unit Charts

If you’re looking to start using unit charts in your business, there are a few dos and don’ts that you’ll want to be aware of. To best understand the parameters when using a unit chart, here’s a rundown of critical factors to account for:

DO: Use Symbols Your Readers Will Understand

Using a unit chart successfully means being aware that simple, common symbols are king. They make it easier to understand an otherwise overwhelming amount of data. By using shapes or figures that are simplified, less detailed, and distinguishable, it will be easier to convey vital data to your readers and ensure they understand it.

DON’T: Use Them for Large Data Sets

When you have extensive data sets that are too complicated to distill into simple charts, it’s best to use another type of chart instead. Don’t try to cram a large amount of information into simple symbols—this will make everything even harder to process. Acknowledge the limits of unit charts and opt for other tools instead.

DO: Include a Legend

If you want to make your unit chart easier to understand on the first read, then you’ll want to include a legend. It should define what your symbols mean so that your readers have a clear understanding of what the data points represent.

Conclusion

If you’re looking to present data in a clear and easy-to-understand manner, keep unit charts in mind. With this guide, you’ll be able to start leveraging this critical tool to improve your data storytelling and help you make smarter decisions.

If you’re looking to leverage the power of data and storytelling for your business, contact Gemini today. We transform data and analytics by enabling you to easily and intuitively interact with data using storytelling visualization.

Data storytelling needs both creativity and structure to succeed. You need to skillfully combine emotion and complex data into a piece that communicates insights that your company can use. Today, more and more businesses are using data storytelling in content for clients and employees alike. If you’re thinking of using data insight solutions to improve your business, here are things to keep in mind.

Why Data Storytelling Works

Experts project that by the end of 2021, there will be 74 zettabytes of data online. That’s 74 trillion gigabytes, a number so large it is practically inconceivable. There is so much content online today (for example, there are over 70 million blogs on WordPress alone). To stand out in that sea of information, you’ll need to do more than recite facts and figures.

Data storytelling succeeds at getting people’s attention because they elicit emotional responses. People feel stories, and brands that can shape data into stories will always have an audience. You can cut through the noise and reach people easily with data they remember.

How to Use Data to Tell a Story

There is so much data available to marketers, analysts, and communicators alike that makes storytelling much easier. If you know how to take information and create a narrative out of it, you can speak to your audience on a personal level. Here are tips that can help you use data in storytelling.1

• Study Your Audience

Most companies already have a sense of how they want potential customers to see their brand. However, you don’t want to base your branding strategy on impression alone, whether you’re a new company or a well-established one. You’ll need to study your market well so you can create stories that will connect with them and compel them to take a desired action. Big data consulting companies can help you study your market and identify the defining features and behaviors of your audience.

• Create a Memorable Brand

The more relatable and memorable your brand is, the better it is for your bottom line. Humans crave connection and relationships, so if your brand feels human, it will create a stronger connection with your audience. You can use data from consumers regarding what aspects of your brand personality resonate with them, such as tone, graphic and design elements, and messaging.

• Make Your Messages Concise

In  a world filled with an overwhelming amount of content, you only have a small window of time to engage with your customers, so you need to be strategic about your words. Study how to make the most impact in the smallest space possible. You can also use audience data here—for example, some programs can show you how long a person spends looking at an advertisement or a post. Posts that sit at the intersection of interesting and brief will almost always get repeat views.

• Make Your Message Shareable

Despite everything being digital, word-of-mouth is still a powerful marketing tool. It is arguably more popular now since you can accelerate storytelling exponentially through social media. Data from your customers’ social media activity can help you determine what they see as worth sharing. You can study these posts and ads and shape your content to replicate what your audience is currently posting and sharing.

Conclusion

Whether they’re aware of it or not, customers want to feel like they are buying into a brand story. They buy a product or subscribe to a service because they believe that the brand’s values align with their own or because the brand promises something they want. Data storytelling will help you create coherent, relatable, and engaging content that makes your brand memorable.

Gemini helps you tell powerful and engaging stories through data. We provide advanced analytics solutions that change the way people and organizations make decisions. Contact us today to learn more.

For risk managers, data analytics has become one of the most critical activities to carry out. With analytics, decision-making becomes even more reliable and accurate, and can help with the long-term success that minimizes losses while maximizing gains. However, there are plenty of challenges to be faced when collecting and using data to make decisions and inform strategies.

Some of these challenges may be tougher than others, but they all impede the risk manager’s ability to analyze data. Fortunately, there are solutions for all of these roadblocks that can make a risk manager’s job easier and more effective.

Keep reading to learn more!

Challenge 1: Handling Overwhelming Amounts of Data

With essentially everything being data-driven, it’s easy to get overwhelmed with enormous amounts of data. For instance, an organization may decide to track every single activity that happens under its name, but this can lead to both relevant and irrelevant activities being recorded, leaving risk managers with too much data to make sense of.

Utilizing a data system that automatically records and organizes information is a must-have to deal with this issue. This system can handle the lion’s share of culling, sorting, and organizing data, while carrying this out manually would take too much time, and leave no room for an employee to focus on important tasks. But, with the help of software to automate this process, an employee can instead focus on making decisions based on data rather than trying to process it.

Challenge 2: Visually Representing Data

To make data easily understandable, it’s important to present it in a visual manner that makes the information more accessible and actionable. Things like graphs and charts are usually the best way to do this, and while data visualization makes it much easier to understand complex sets of data, creating these visuals can be time-consuming and difficult, especially if there is a large amount of data to visualize.

To tackle this challenge, utilizing software that can easily summarize data into a graphic is vital. This way, risk managers and other individuals can quickly make better decisions based on accessible visual representations while not spending too much time creating them.

Challenge 3: Ensuring Data Is Easily Accessible

When moving data around, there’s the risk of accidentally storing data somewhere that is hard to access, or worse, where it could get lost. This hinders the data analysis process, mainly because risk managers waste time trying to retrieve data when it should be easily accessible in the first place.

To facilitate easy data access, using a database that allows authorized users to access information quickly is essential. Such a system should support viewing and editing data from anywhere, allowing for quick decision-making no matter where the decision-maker is.

Conclusion

These are just some of the many challenges decision-makers face when trying to work with data analytics. Other challenges also include a limiting budget, shortage of skills, the need to collect data in real-time, maintaining accurate data, and more. Fortunately, no matter the challenge being faced, there’s a solution to fix them.

You may also want to consider partnering with a firm of data experts to help you come up with solutions to help eliminate these challenges and make data analytics more useful, valuable, and efficient.

Gemini Data are experts at transforming data and analytics to help businesses grow and achieve their goals. If you are looking for a partner to help with your data analysis and storytelling, contact us today to learn more.

With the 24-hour information cycle and social media constantly bombarding us, many people have become desensitized to it all. Companies have to keep innovating new ways to catch potential customers’ attention and cut through all of the noise.  As a result, many businesses are focusing more on creative promotional campaigns and relying on things like data analytics to better understand their audience.

So what are the potential benefits to your business of using data analytics? Keep reading to learn more about how your business – big or small – can use data analytics to reach your goals.

1. Better customer retention

With the help of data, many companies can better understand what potential customers are looking for much more accurately. This means your business can produce tailor-made offerings that you know will meet the needs of your customers. For instance, analyzing and interpreting customer data allows companies to notice any patterns and trends to incorporate into new products or services.

2. Focused campaigns

Previously, many campaigns were created based on limited data and the assumption of what the audience may like. However, with data analytics, these campaigns can be much more relevant and highly focused.

The frustration of creating and fine-tuning a campaign that goes nowhere or falls short of your goals is challenging and can also waste precious company resources. But, with the information available from customer data, companies have everything they need to understand exactly what the customers are looking for—allowing for the creation of focused campaigns that are much likelier to achieve goals and attract individuals to their products and services.

3. Identification of risks

While some companies are comfortable working in high-risk environments, data analytics has made risk management much more effective, and many businesses are able to not only survive in high-risk markets but thrive in them.

As we mentioned above, data analysis can help businesses pull out patterns and trends in audience behavior, allowing for prediction of possible risks and the creation of effective risk management strategies to mitigate them.

4. Creation of new and innovative offerings

Using data analytics, companies have the information they need to understand how well their products and services are performing and what can be done to improve them. This gives companies the chance to continuously improve on existing products and services as well as create new and innovative offerings to stay competitive and keep customers coming back for more.

Conclusion

Data analysis has been pivotal to allowing companies to understand exactly what their customers want, and how they can create relevant products and services. Taking advantage of available data can help businesses stay ahead of the competition and become invaluable to customers.

If you’re looking to get more out of your data to reach your business’ goals, reach out to Gemini Data today. We can help your business understand, interpret, and act on your data using storytelling visualization, taking you from data to insights quickly and easily.