Data democratization has changed the way businesses and brands approach information. What used to be a science reserved for the companies with the time and resources to dedicate to large-scale data collection has become a commodity available to all businesses. However, the widespread availability of data doesn’t necessarily make it any easier to understand and analyze. Data visualizations are the final element that makes the newly available universe of data into a tool that businesses can use to make insightful decisions. Instead of requiring the skills of a data analyst combined with the instincts of an experienced executive, visualization allow the analysts to present the data in a way that executives can draw insights from. Spreading Democracy, Bit by Bit The impact of visualizations on information democratization is not a particularly new revelation. The emergence of visualization can be seen by the literally thousands of infographics that are created and posted to sites like Pinterest.com, Visual.ly’s search engine or found with Google’s image search tools. What’s more, many of these infographics are exploratory in nature, allowing the viewers to not just understand a single point of data, but to make their own conclusions and decisions. The Emergence of Data Visualizations as Decision Making Tools Data visualizations have, similarly, existed as internal tools for a long time. Even before the onset of democratized data, pie charts, data tables, scatter plots and other charts have played a role in reporting information. However, these types of data visualizations tended to be primarily useful for presenting a concept, but they had limited usefulness when it came to developing multi-level, complex insights from the collected data. That meant that it was the role of an analyst to interpret the data, decide on what was important, and then present that interpretation. While many analysts were very capable of handling this responsibility, it meant that many knowledgeable executives weren’t allowed the opportunity to put their experience to good use. Advanced data visualizations bring those executives into the process earlier. Analysts are still required to build a good data visualization – it requires experience and skill to collect, organize and format the data in such a way that it can tell a story. But, instead of deciding what that story means and presenting it simply, brands can now allow the rest of their team to use these windows to the data in order to make sense of what’s being seen. A word of caution: While we’ve focused mostly on internal data, there is also a real benefit to using externally created data visualizations as decision-making tools. The nature of an infographic, however, makes it more difficult to track down the source data. If you’re making mission critical decisions based on an external visualization, it’s vital that you first confirm the validity of the data sources. The widespread availability of big data is only the first step to a true democratization of information. The next step, and the one we’re in the middle of now, is the regular and habitual use of visualizations as an internal decision-making tool. It’s this trend that will turn buzzwords like “taking advantage of information democratization” into “just doing everyday business.” If you feel you’re not getting full value from your organization’s available information, Boost Labs can help you present your story more effectively through effective visual content strategies and practical information design.
Data is everywhere (and it always has been). Buying habits have always existed, as have demographics and weather patterns. Though they’ve been measured and tracked effectively for the last 50 years or so, for much of that time, only very large corporations or entities could afford it. In the last few years, however, the age of data democratization has arrived and that once limited data is suddenly available to companies of all sizes. The democratization of data has leveled the playing field. With vast amounts of information readily available at the push of a button, this movement has ushered in a new kind of business. The Impact of Big Data Democratization The Internet has provided an enormous “data warehouse” that reaches across all sectors: from healthcare to education, from government to technology and from business to recreation — absolutely every facet of life has been impacted by the widespread availability of data. Healthcare – Data democratization will continue to facilitate greater sharing and collaboration between researchers, scientists and medical professionals. The sharing of discoveries can lead other teams to build on those successes and work toward the next major breakthrough. No longer confined to the walls of their lab — or the pages of a paper medical journal that might or might not be read — the new interconnectivity in medicine, science and healthcare is a perfect example of the democratization of information. Education – Data democratization can give more people access to high-quality and affordable education. Government – By focusing on data availability as opposed to websites, data democratization can lead to a more transparent government. Personal Use – The storage of vital personal information also benefits from a cloud-based approach (the heart of data democratization). For example, In the event of a fire, natural disaster, sudden death of a spouse or other tragedy, people are able to locate important documents much more quickly, making it much easier to work with insurance companies. Those are just a few examples – ultimately businesses and individuals can use the increased availability of data to make better-informed decisions and understand the world around them. Putting Data Democracy to Work A company looking to take advantage of the benefits that come from data democratization needs to start with preparation. This preparation takes the form of both technical changes and perspective changes – getting your personnel on-board with a new way of sharing and accessing data is key to making democratized data a company-wide tool. Connect Your Public and Private Data – Analysts cannot effectively create plans and designs without the full spectrum of information. By ensuring that they have access to both public and private information (and understand how they’re connected), you ensure that they can draw the right conclusions from the data you’ve collected. Use Information Designers and Visualizations – The wealth of data that will become available means that it will also be difficult to sort through. Data visualizations (created by information designers) will make that information easier to inspect and draw conclusions from. Prioritize The Impact of Your Data – Your data visualizations are likely to show a large number of relationships and dependencies between your customers and company activities. Understanding and teaching your team about which of those are actually meaningful and important is key to avoiding “Analysis Paralysis.” Knowledge is power. We have entered into an era where more knowledge is available to companies and individuals than ever before. Putting that data to good use takes some practice and preparation (and a few good tools), but everyone now has the power to use information that used to belong to a very few – and that’s why it’s called Democracy. If you’re ready to capitalize on data democratization but are unsure on where to get started, contact us to learn how we can help you put your data to work.
A slick chart, an interactive data-exploration interface or a KPI-based dashboard; all of these are data visualization products. They garner a lot of attention because they are a finished product, and look nice as well. However, for many companies engaged in data visualization, those final deliverables aren’t the most important benefit of data visualization. Instead, it’s the insights into the quality of their collected data that truly leads to success. Data visualization provides 3 key insights into data: Is the data complete? Is the data valid? Is the data well-organized? Without knowing those 3 elements, data collection and business intelligence processes become much more expensive, labor intensive, and may end up abandoned when the data doesn’t demonstrate what is intended. Using the insights from data visualization, these projects can have a much higher likelihood of completion and success. Insight into Data #1: Is the data complete? The most straightforward insight that visualization can give you about your data is its completeness. With a few quick charts, areas where data is missing show up as gaps or blanks on the report (called the “Swiss Cheese” effect). In addition to learning which specific data elements are missing, visualizations can show trends of missing data. Those trends can tell a story about the data collection process and provide insight into changes necessary in the way data is gathered. A Data Completeness Example: After creating a visualization on a collection of survey data regarding movie-going habits, it’s clear than there are a significant number of blanks after question 14 on the survey. The visualization helps the survey company recognize that those specific records need to be abandoned, but also that the survey should be shortened to accommodate for “respondent fatigue”, the likely cause of the incompletions. Insight into Data #2: Is the data valid? The importance of visualization among data validation techniques has been discussed before. It’s clear, then, that visualization can play a pivotal role in understanding data’s validity. By executing a quick, preliminary visualization on collected data, trends that indicate problems in the complete data can be found. A Data Validation Example: A collected dataset is designed to demonstrate the difference in male population statistics between Alaska and Florida. Examination of individual records and outliers show that the data is valid – there are a significantly higher percentage of males in Alaska than in Florida, this is expected. However, a visualization of the entire dataset shows that there are more males in Alaska than Florida. This is a red flag because, even with the gender ratio differences, Florida’s larger population means that it should have a higher total number of males. A well-designed, preliminary visualization can give insight into the validity of collected data that is difficult, or even impossible, to gain with traditional methods. Insight into Data #3: Is the data well-organized? Poorly organized data can be the bane of the final step of a data collection or business intelligence process. Using data organization tools from the start can help streamline later steps of the process. During collection, the data is often organized in a way that optimizes the gathering process. However, that same organizational scheme can be a problem when the time comes to act. The data visualization process serves to highlight the organizational challenges of your data and provides insights into how it might be done better. A Data Organization Example: A client wishes to use their collected customer data to develop a customer profile that defines demographic breakouts of snack-food purchases indexed by time of day. Their data visualization partner asks them where that data is stored and it is discovered that the transactional data is stored separately from the customer profile information, and that data can only be intersected through yet another correlational dataset. While all the data is technically available, the data needs to be reorganized to be functional in decision making. Data visualization isn’t just data organization and analysis tool; it can play a crucial role in the entire data gathering and management process. With a well-executed visualization, taking time to understand what is to be learned from the data and how the information will be gathered, companies are able to cut costs and eliminate the waste that comes from having to re-gather or re-organize their data. To find out what your data has to say to you, contact Boost Labs to learn about creating a visualization to give you the insights your project needs to succeed.