What is Data Profiling?
Data profiling is the process of examining data from an information source and summarizing information about the data such as structure, content, accuracy, and completeness in order to determine the potential of the data for projects. This is an important step in data analysis because it assesses data quality and helps to ensure conclusions drawn from the data will be valid.
This process is necessary in traditional data analysis projects that require manual data collection, cleansing, and processing. In this article, we will discuss data profiling for external or alternative data and the downfalls of this process, and present a better alternative to traditional data profiling.
Alternative Data for Business Intelligence
Alternative data sources are becoming more and more important for organizations to use. Internal datasets are no longer a strong enough foundation for business decisions due to the amount of data available on the web. Organizations now have the potential to analyze competitor data, market data, customer sentiment, and more through web data.
Data profiling is a crucial step when using alternative data, because there is a higher risk of inaccurate data. Traditional web crawlers are coded to parse HTML, which provides incomplete and inconsistent data. This has the potential to skew business decisions by including only part of the information on the web. Incomplete data means inaccurate data, which is not valid for business decisions.
The types of business decisions that are based on alternative data are crucial for the success of the business. For example, setting prices based on competitor price data, making changes to products/services or customer experience based on sentiment analysis, expanding services to keep up with market trends, or making investment decisions based on news reports or financial statements. The need for these important business decisions spans across industries such as Finance & Equity Research, Online Travel & Hospitality, eCommerce & Retail, Sales & Marketing, Manufacturing, Real Estate Property Listings, and more. The challenges of traditional data profiling are a major issue due to the wide necessity for valid alternative datasets.
The Challenges of Data Profiling
Data profiling is a time-consuming process, which means organizations must invest time and money into their alternative data analysis projects. The time it takes to complete each data management step means that the data will be outdated by the time it is ready to be analyzed. Outdated data doesn’t allow the organization to stay ahead of competitors or keep up with changing customer needs and market trends. If your organization is using slightly outdated information, you will always be a step behind the competition.
Since data profiling is so time-consuming, it’s likely that you won’t be able to go through the entire dataset. This presents another challenge as the conclusion drawn from the data profiling process may not be accurate itself, leaving you with data you consider accurate while it might not be. This uncertainty can cost organizations by providing data with errors as a foundation for business decisions.
Another challenge with the process of profiling data is that it determines whether or not the data is complete, accurate, and valid, but it doesn’t provide a solution if the data is found to be incomplete. This can leave organizations without the ability to use alternative data because if they use an HTML scraper, then they may not have a way to scrape complete and accurate datasets from the web.
The Solution: Web Data Integration
Web Data Integration (WDI) combines the steps of web data analysis, allowing organizations to identify, extract, prepare, integrate, and consume web data in one quick and easy step as opposed to manually collecting, profiling, and cleaning the data. WDI makes data profiling a part of the automatic process, eradicating the large amounts of time spent on manual steps. It also provides organizations with more accurate and usable data by automatically performing built-in quality control while taking data through all steps of the process. And with WDI, all data is stored in a data warehouse in case future reference is needed.
WDI is a solution for all the business uses of alternative data mentioned before and more. It can provide your organization with complete and accurate web data in minutes rather than months. This means that you can have real-time competitive price monitoring, customer sentiment analysis, market data, financial data, and anything else that exists on the web. Organizations can get web data that is as reliable as their internal data, revolutionizing their business intelligence.
Revolutionizing Your Business Intelligence
Having access to accurate web data in real-time will revolutionize your organization’s business intelligence. Not only will you be able to trust your internal datasets, but you’ll also be able to trust your web data. WDI can get data from anywhere on the web, leaving nothing out, not even PDFs. The business decisions you can make with real-time data are extremely valuable because they allow your organization to keep up with market trends and stay ahead of competitors.
Without the need for coding that HTML web scrapers require, WDI saves months of time on every web data project. The data is collected, cleaned, integrated with your data analysis apps, and presented in a usable format all in minutes. Therefore, there is no longer time spent on steps such as coding the scraper, crawling the web source code, profiling the data, cleansing and munging the data, and formatting the data in a presentable way. With Web Data Integration, organizations can gain a competitive advantage by utilizing web data in a much more efficient way than most web data projects allow.
Find out how Import.io can help your organization strategize for success with smart web data by contacting our team of experts to discuss solutions and schedule a demo.