Big data has become invaluable in the enterprise landscape that it has been dubbed “the new oil.” However, data, like oil, must be extracted and purified before it can be utilized as fuel. By speeding up data capture, boosting data quality standards, providing context, and allowing all employees access to data insights, AI is driving a revolution in data capture.
Across the entire ecosystem, big data has been transforming business processes. Enterprises can better understand their consumers with big data foresee and manage risk far sooner, detect potentially profitable opportunities earlier, forecast new trends and market shifts, and more.
The only problem is that big data is, as its name implies, enormous. Many firms struggle to get the most value out of their data by crunching it, extracting valuable insights, and incorporating those insights into their decision-making processes.
Companies with a large data science team could run queries and make accurate predictions, but mid-sized businesses were typically overwhelmed by the volume of data in front of them and unsure how to proceed.
AI and ML have opened up new possibilities for big data capture, allowing self-learning tools to automate the collection, processing, and analysis of massive datasets for business use cases. Businesses have begun to use AI and machine learning-powered data platform solutions to manage their data, speed up processing, and expand the size of databases they can handle.
Big data may have revolutionized commercial decision-making, but here are 4 ways in which AI is transforming big data analysis.
Capturing Complex Data at a Faster Pace
New AI-powered intelligent data capture (IDC) solutions can take data from a variety of sources and turn it into the structured forms that data analytics tools require, all without the need for arduous, time-consuming manual data entry.
An ML-powered data capture tool, for instance, can recognize an invoice number regardless of where it appears on the document or how many digits are contained. Without machine learning, any automated tool would need dozens of complex rules to cover all possible scenarios, and even then, one cannot guarantee that it would get it right every time. Data can be extracted from transcripts or elaborate stacked tables with mismatched lines using IDC data tools.
AI-powered data capture helps businesses to mine new data sources while freeing up people for revenue-generating tasks and lowering the risk of manual errors by eliminating the requirement for manual data entry.
Improving Data Quality
AI data extraction can improve data quality by conducting data validation, comparing data points against similar datasets from a different source or even many sources at the same time, in addition to minimizing the chance of manual data entry errors.
Artificial intelligence (AI) tools can recognize the sort of document it is consuming and deliver the data to the appropriate structured data system. Automating the data organization and classification process not only saves time for data processing staff, but it also adds another layer of assurance to the data’s quality.
In a moment of exhaustion or distraction, an ML-trained engine is unlikely to make a mistake and misclassify datasets. Furthermore, automatic AI data extraction saves metadata and shares it with analytics engines, enriching data and enhancing analytics results.
The more context there is with business datasets, the more trustworthy the insights become. AI data capture maintains context, broadening the reach of data-driven insights and making them relevant to a wider range of applications.
Because business queries tend to transcend functions and units rather than sticking to departmental boundaries, business analytics become more useful when users can ask broader business questions that cut beyond imaginary departmental lines.
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Data Analysis Made Simple
Before AI and ML, data and analytics were thought to be two different entities. Data was kept in one area, and the user had to choose which data to access in order to run it via analytics tools in a different location. However, AI in analytics, often known as augmented analytics, has changed everything.
One of the biggest benefits of augmented analytics is that it eliminates the need for a data science team to choose the data and carefully construct the query in data science jargon. Queries can be run by any employee, regardless of whether or not they have a DS background, democratizing access to data-driven insights. The next generation of AI-powered data platforms takes it a step further, providing key insights automatically and sending them to the relevant team.