We are now living in the age of big data. In fact, web data is projected to grow even faster due to both machine-based and human-generated data experiencing an overall growth rate of 10 times faster than conventional business data.
With such major potential, 89 percent of users believe big data will revolutionize business operations in the same way the Internet did. However, almost eight of every ten users (79%) agree that companies are not actively seeking out or embracing big data in a way that will help them gain meaningful business insights.
As the big data era continues to grow, it’s only going to become more crucial for businesses to seek out the value of big data from the metrics, insights, and information gained through a process called data discovery.
What is data discovery?
Data discovery is the process of detecting patterns and trends in web data through data collection and analysis. It is the first step in leveraging web data to inform future critical business decisions with useful guided analytics.
Through the discovery process, data is collected, combined, and analyzed to provide businesses with clearer insights regarding their customers, business, and their industry. Data discovery is clustered into three main categories:
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Data preparation
Data preparation can be described as a “pre-processing” step in which data from one of more sources is cleaned and transformed to improve its quality prior to its use in business analytics. It’s often used to merge different data sources with different structures and different levels of data quality into a consistent format for later processing.
In the big data era, any business can take advantage of fast and effective data preparation techniques with advanced software like Import.io that can quickly and easily grab data from multiple websites and prepare datasets of web data.
One of the most crucial but overlooked steps in data preparation is knowing your data before properly preparing it for downstream consumption. It takes profiling, visualizing, detecting outliers, and finding null values and other inaccurate data within your dataset, outside of just a simple visual examination of the data set.
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Data visualization
Data visualization is a key part of data analysis. Representing collected web data into a graph, chart, or other visual format, data visualization helps communicate the various relationships of data with images.
With the rapid rise of big data, being able to interpret increasingly larger batches of data is crucial. Machine learning and predictive analysis make it even easier to conduct analyses for helpful web data visualizations.
This portion of data discovery is so important just because of how effective it is at making web data findings easier for everyone to understand, whether you are working in tech, design, finance, marketing, or anything else.
Effective data visualization is one of the most crucial final steps of data analysis. Without it, important insights and messages can be lost. Import.io understands the importance of data visualization, which is why it’s part of our Web Data Integration solution.
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Data reporting & descriptive statistics
This is where data is summarized and organized into something that is more manageable and easier to understand. These descriptions can include either the entire dataset or merely a portion of it. Descriptive data analysis only focuses on the data itself and not on possible far-reaching implications that are beyond the data that’s represented.
Instead of having numbers and spreadsheets to look at, data reporting provides a visual that represents the information from within the dataset. While descriptive statistics can break data down into something more digestible, data visualization goes even further, taking that data and creating a visual that instantly communicates a story.
Combining both descriptive statistics and data visualization transforms them into a valuable asset for any company. This is one of the ways that web data integration helps company leaders make key business decisions.
Why is data discovery important?
Whether you’re a data scientist, project manager, or a C-suite executive, in today’s digital age, all business users need to be able to access and understand the data that concerns their industry. Being a data-driven organization starts with understanding your data.
Implementing your findings from data discovery will help your organization develop a competitive edge as you fine tune your efforts to remain relevant, successful and more inherently data-driven.
What are some data discovery tools?
Data analysis, descriptive statistics, and data visualization should become part of a business’s arsenal. Data has so much to offer in terms of informing business decisions and planning upcoming business strategies. Missing out on these capabilities means missing out on possibilities and opportunities to grow and find greater success that’s sustainable.
Creating meaningful business insight requires the use of an effective and versatile data discovery tool, like Import.io. A good data discovery tool is extremely helpful in turning abstract data into something much easier to grasp.
Along the journey of turning data into visual insights, the data gets cleaned, shaping into a manageable item and filtering out data values that may unnecessarily interfere with the message communicated through the information.
Insights
The quickest way to understand web data is to visualize it, and Import.io works directly with your web data to identify, extract, prepare, integrate, and allow you to consume data insights in real time.
Talk to a member of our team to learn more about how Import.io can help your organization.
