Big data gives modern business intelligence its transformative power and allows organizations to remain competitive in a fierce market. What some refer to as “dark data” (the relevant substance within big data) provides the specific actionable insight organizations seek. This insight fuels faster, better decisions and much more. It also benefits every aspect of business from operations to R&D.
Big data undeniably offers a wealth of valuable insight, but it also presents unique challenges; furthermore, these issues only grow with technological advancement and data size. One of the biggest challenges remains sifting through a sea of data to both extract and analyze meaningful information.
Though our advances in hardware equip us to manage large data volume and processing, even the average data scientist could not possibly analyze large data collections quickly. This limit of human processing makes visualization a critical tool in big data analysis. Big data also require special tools to extract it from files, PDF, spreadsheets.
Big Data and Visualization
Data visualization bridges the gap between data science analysis and insight used in the field. It essentially translates data into actions, and opens data to an entire organization. It also aids in making big data an integral, drop-in part of the decision-making process. This results from the transparent, contextual, and intuitive nature of visualizations, which are already staples of scientific analysis.
The most recognizable types of visualizations are charts and graphs. These graphics reveal patterns and relationships in data, and enable the user to quickly absorb constructive information from large datasets. When paired with powerful applications, charts make data more interactive. Users can manipulate data to gather the exact insight needed in identifying and acting on trends.
Charts provide rich expression through their broad and growing variety, and the importance of data visualization in modern business continues to drive creativity and development in chart types. However, most charts conform to six proven, standard types which easily and quickly convey information:
- Comparison and Relationship – These charts reveal relationships and qualities of two or more variables, e.g., bar charts, line charts, and venn diagrams.
- Distribution – These charts illustrate the distribution of data; for example, distribution over a specific time period. Histograms and scatter plots fall in this category.
- Trends – Though other chart types show trends, some are specifically focused on delivering this information,e.g., line charts, OHLCs, and kagi charts.
- Composition – This chart type reveals data composition, e.g., pie charts, marimekkos, and stacked bar charts.
- Process – These charts detail and explain a process flow, e.g., a decision tree.
- Maps – These charts describe location data through overlays on maps; for example, a map shaded to specify dominant political parties by state.
The Problem with Charts
Though charts prove an excellent resource in visualization, they do suffer from certain inherent flaws like misleading or confusing information. This typically stems from poor chart types (e.g., bubble charts) or poor implementation. Chart solutions either aid in avoiding these problems or exacerbate them.
Current charting solutions offer a broad range of functionality, and serve every type of user from novice to expert. However, many fail in critical functionalities and include problematic features; for example, a cluttered interface, tedious chart creation, and limited compatibility.
- Ease-of-use – Js chart solutions provide simple, clean interfaces with the common designs of other web-based solutions. They typically structure interfaces for fast production and fast access.
- High-quality graphics – Js data tools create high-definition, professional images.
- Rapid production – Through solid integration, precision, and ease-of-use; Data tools accelerate the creation of graphics. Users spend less time navigating and familiarizing, and more time creating.
- Easy integration – The use of common, standard web technologies means smooth integration with various data sources, applications, and systems.
- Lightweight – Charting libraries support 100% client side solutions. These solutions avoid the added weight of more complex designs that exploit multiple server-side and client-side resources.
- Accelerated function – Js chart tools exploiting exclusively client-side function deliver responsive solutions, and avoid the lag and bottlenecks associated with more constrained options.
- Agility – Js data tools are inherently more agile than other solutions given the ease of customization and expansion. This allows organization technology to remain competitive.