Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

How Artificial Intelligence Is Revolutionizing Data Capture

Big data has been transforming business processes across the entire ecosystem. With the help of big data, enterprises can understand their customers better, predict and take steps to mitigate risk far earlier, spot potentially profitable opportunities further in advance, forecast new trends and market changes, and more.

The only trouble is that big data is, as the name suggests, very large. It’s a struggle for many businesses to get maximum value out of their data, by crunching it, extracting meaningful insights, and integrating those insights into their decision-making workflows.

 

Companies that could afford a large data science team were able to run queries and produce accurate predictions, but mid-sized enterprises weren’t frequently overwhelmed by the mass of data before them and weren’t sure how to proceed.

However, the advent of artificial intelligence (AI) and machine learning (ML) opened up new possibilities for big data capture, enabling self-learning tools that can automate gathering, processing, and analyzing enormous datasets for business use cases.

Enterprises began using AI and ML-powered data platform solutions like Looker to handle their data, speed up processing, and expand the size of the databases that they can handle, making Looker performance a critical factor in data analysis.

Big data may have transformed business decision-making, but here are 4 ways that AI is revolutionizing big data analysis.

Speeding up Complex Data Capture

New tools that use AI for intelligent data capture (IDC) can grab data from a range of disparate sources and convert it into the structured formats that data analytics tools need, without requiring tedious, time-consuming manual data entry.

For example, an ML-powered data capture tool can identify an invoice number no matter where it appears on the document and regardless of how many digits are included. Without ML, any automated tool would require dozens of complex rules to cover all possible eventualities, and even then you wouldn’t be able to assume that it gets it right every time. IDC data tools can also extract data from transcripts, or complicated stacked tables with lines that don’t match up.

By removing the need for manual data entry, AI-powered data capture enables enterprises to mine more data sources while freeing up employees for revenue-driving tasks and reducing the risk of manual errors.

Improving Data Quality

Related Posts
1 of 3,137

As well as reducing the risk of manual data entry errors, AI data extraction can further raise the quality of the data by performing data validation as well, comparing data points against similar datasets from a different source or even from multiple sources at the same time…

AI tools can recognize the type of document they’re consuming and send the data to the right kind of structured data system. Automating the process of organizing and classifying data not only saves more time for data processing employees, but also adds another layer of confidence to the quality of the data.

An ML-trained engine isn’t likely to make a mistake, in a moment of tiredness or distraction, and mis-classify datasets. Additionally, automated AI data extraction holds on to metadata and shares that too with analytics engines, enriching the data and improving analytics outcomes.

Adding Data Context

The more context there is accompanying business datasets, the more reliable the insights will be. AI data capture preserves contextual information which widens the scope of data-driven insights and makes them relevant to more use cases.

Business queries tend to cross functions and units and don’t restrict themselves to departmental boundaries, so business analytics become more valuable when the user can ask broader business questions that cross theoretical departmental lines.

Simplifying Data Analysis

Before AI and ML came along, data and analytics were considered two separate things. Data was stored in one place, and the user had to choose which data to access to run it through the analytics tools in a different location. But AI in analytics, also called augmented analytics, changed all that.

With augmented analytics, you can bring your data and analytics together. ML can identify trends and anomalies in data without human input, so you can ask queries in natural language and rely on the data platform to grab the best data and run the best analytics processes for your needs.

A big advantage of augmented analytics is that it doesn’t require a DS team to select the data and carefully word the query in data-science-speak. All employees, with or without a DS background, can run queries, democratizing access to data-driven insights. The next wave of AI-based data platforms like Looker go one step further, automatically producing valuable insights and pushing them to the relevant team.

AI Helps Big Data to Reach Its Potential 

Big data proved invaluable to the business world, to the extent that it’s been referred to as “the new oil.” But like oil, data needs to be extracted and refined before it can be used effectively as fuel. Artificial intelligence is driving a revolution in data capture, processing, and analysis by speeding up data capture, raising the standards for data quality, adding context, and opening up access to data insights for all employees.

Comments are closed.