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.
Big data has changed business processes across the ecosystem. With big data, companies can better understand their consumers by forecasting and managing risks much earlier, identifying potentially profitable opportunities earlier, predicting new trends and market changes, and much more. As the name suggests, big data is huge. Many companies strive to get the maximum benefit from their data by processing it, extracting valuable information, and including that information in their decision-making processes. Organizations with a large data science team could query and make accurate predictions, but midsize businesses were generally overwhelmed by the amount of data in front of them and didn’t know how to proceed.
Artificial intelligence and machine learning have opened up new possibilities for big data ingestion and enable self-learning tools to automate the collection, processing and analysis of huge data sets 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 the databases that can handle it. Big data may have revolutionized business decision making, but here are 4 ways AI is transforming big data analytics.
Capturing Complex Data at a Faster Pace
New AI-powered Intelligent Data Collection (IDC) solutions can ingest data from a variety of sources and transform it into the structured forms that data analysis tools require, without the arduous, time-consuming manual data entry required. An ML-powered data capture tool, for instance, can recognize an invoice number regardless of where it is in the document or how many digits it contains. Without machine learning, any automated tool would need dozens of complex rules to cover all possible scenarios, and even then there is no guarantee that data can be extracted from transcripts or stacked tables with mismatched rows using IDC data tools. AI data capture helps companies extract new data sources while freeing up employees for revenue-generating tasks and reducing the risk of manual errors by eliminating manual data entry.
Improving Data Quality
AI data extraction can improve data quality by performing data validation, comparing data points with similar data sets from another source or even many sources at the same time, and minimizing the likelihood of manual data entry errors. Artificial intelligence (AI) tools can recognize the type of document used and deliver the data to the corresponding structured data system. The automation of the data organization and classification process not only saves time for the data processing personnel, but also increases the security of the data quality. At the moment of exhaustion or distraction, an ML-trained engine is unlikely to go wrong and misclassify records. In addition, the automatic AI data extraction stores metadata and shares it with analysis engines, which enriches the data and improves the analysis results.
Contextualizing Data
The more context there is with business records, the more reliable the insights become. AI data capture maintains context, expands the scope of data-driven insights, and makes it relevant for a broader range of applications. Because business queries tend to transcend functions and units rather than complying with departmental boundaries makes business analysis more useful when users can ask broader business questions that go beyond imaginary departmental boundaries.
Data Analysis Made Simple
Before AI and ML, data and analytics were viewed as two distinct entities: data was stored in one area and the user had to choose which data to access in order to run the analytics tools in another location. However, AI in analytics, often referred to as augmented analytics, has changed everything. One of the greatest advantages of augmented analytics is that a data science team no longer has to select the data and carefully build the query in data science jargon. Queries can be run by every employee, regardless of whether or not they have DS experience, democratizes access to data-driven information. The next generation of AI-supported data platforms goes one step further, automatically provides important information and sends it to the responsible team.
