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Some of the ways in which automation can unlock business intelligence and analytics include intelligent automation of data gathering and formatting procedures, automated insights discovery, data quality enhancement, Automated data visualization, and reporting. 

FREMONT, CA: 729 Harvard Business Review readers were surveyed to better understand the challenges organizations face in becoming agile, innovative, data-driven, and truly competitive. In the survey, 86 percent of respondents said it was "very important" to extract new value and insights from enterprise data, and 75 percent said it was "essential" to provide actionable intelligence to employees.

In most organizations, gaining more value from data, making better decisions, and quickly acting on them are mission-critical.

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Some of the ways in which Automation can help realize the full potential of Analytics and Business Intelligence (BI) are:

Enhancing the quality of data: Poor quality data is estimated to cost an organization $15 million annually, according to a study.

To aid in data repair and to identify data quality issues ahead of analysis, data preparation is an important step. Data collection, cleansing, and data repair can be automated to reduce the amount of time analysts spend preparing data.

With robotic process automation (RPA), data is extracted from multiple systems, quality checks are performed, and data is compiled into a single file or report ready for preparation and analysis.

In addition to data extraction and preparation, Automation can also improve underlying data quality by reducing errors caused by manual entry.

By automating advanced processes like digitization and data collection, RPA ensures data quality remains high. Automating data management involves extracting data from documents and synchronizing it.

Automating complex businesses and IT processes using BI data: In order to gain insights into their businesses and make more informed decisions, organizations are embracing analytics and data science. As part of an advanced business workflow, BI data can also drive better decisions.

In most cases, either manual extraction or new code would be required to extract data from the BI system. With RPA, BI data extraction can be automated quickly.

Invoice payments reaching their maximum payment terms can be reported and acted upon by finance departments. RPA robots can automate reminders and escalations using information from the automatically downloaded BI report. Organizations can make better decisions faster and more efficiently by automating BI data extraction and using that data in their complex business processes.

Any system can be used to analyze data: With RPA, organizations can extend the reach of BI and analytical tools into legacy systems, virtualized environments, and systems without APIs. Whether organizations want to extract and analyze core banking information or gather exchange rate data from a website, Automation can help in this situation.

Additionally, AI-powered RPA can analyze unstructured data such as emails, PDFs, images, handwriting, and scanned documents. Unstructured data is consolidated into a single data source, such as a line-of-business system, spreadsheet, or database, and can be analyzed instantly.