December 9, 2021

6 Ways Automation Unlocks the Potential of Data Analytics and BI

Decision making and implementing faster actions are two elements central to any organizational success. However, no actions and decisions work perfectly without clear, logical data. In order to remain competitive and unbeatable in relevant market, it is important overcome challenges through data-driven analytics and business intelligence.

Automation of tasks on the other hand is an innovative way to handle the enterprise data and make you more agile in your job. In a survey by Harvard Business Review (HBR) report , 86% respondents agree that enterprise data should offer massive value and pivotal insight while 75% admits that employees should be given actionable intelligence.

To understand how automation extracts true potential of analytics and business intelligence, here are 6 ways you can:

Improve Data Quality

Analytics and intelligence is all about extracting value and purpose-driven information. However, in the absence of well-organized data, the results from BI are not that satisfactory. The data used in predictive models and futuristic analysis might just lead to losing trust from your business customers while disturbing the financial balance in your business.

As Gartner article explains about the importance of data quality, the poorly managed data of average quality is estimated to result in the loss of on average $15 million per year for a given organization. By means of automation, it becomes easy to manage data preparation and repair operation. The job also helps minimize the time analysts spend on collecting and preparing true data.


Data Preparation, Repair and Collection

As said earlier, improving data quality means a lot for the end result analysts receive in terms of business intelligence solution. Data preparation is key to identifying possible quality issues in data to be processed and comes prior to analysis stage. Since data scientists spend most of their time preparing, correcting and managing data, data preparation becomes a crucial step of analysis.

Hence to increase the time spent on actual hardcore analysis, it is advisable to opt for automating the functions like data collection and data repair. Cleansing the unnecessary data can greatly reduce the time analysts spend on data management.

To extract data from various sources and assess the quality of data, Robotic process automation (RPA) plays a key role. Its main task is to extract data, run a few quality checks and compile refined data into a single report. The entire process ensures that data ready for preparation and analysis is free from errors caused by manual data entry jobs.


Expanding Role of RPA

The introduction of RPA in any organization for supporting many repetitive tasks improves data quality while ensuring data collection and preparation. Apart from data extraction, RPA also performs data synchronization as part of the data management automation process. It revolutionizes the existing legacy systems and mainframes of many organizations that lack efficiency in data-centric operations.

As the global mainframe market report implies, nearly 70% of banking corporate data still relies on mainframes. This means many of their legacy systems and business process applications lack APIs required for precise data extraction from documents. This in turn makes the task of preparation and effective collection of data for analysis a little hectic and challenging since it involves a significant amount of manual work.

A well-implemented RPA can go beyond the usual and inject the solution of BI and analytical tools into corporate and industrial legacy systems. Specifically, in banking, automation can help extract and gather any form of banking data from a website into analytics tools.


Analysis-ready Structured Data

Understanding and switching data from one legacy system into a new automated digital environment are possible with RPA. The same goes for the unstructured data format.

With the power of artificial intelligence, RPA can process the unstructured data (emails, images, PDFs, scanned files) and consolidate them all into a single data source for further analysis. The single data source could be a spreadsheet, business-specific format, or a refined database ready for analysis.

The Hollard Group, a South-African-based insurance company went the way of automation. Earlier, the company used to receive 1.5 million emails a year, each being manually processed to understand the context and content for analysis. Maintaining high accuracy and strict compliance were the major requirements of the process.

The Hollard Group, after the successful implementation of RPA, now lowered the cost by 91% for each transaction, saves weekly 2,000 work hours while working 600% times faster.


Decisions Turned into Immediate Actions

BI solution is where action meets decisions – which is the main motive of having powerful analysis in place. Business intelligence work wonders where it is most needed. This is the concept that reinforced the course of actions taken by decision-makers based on accurate analysis.

To expedite the actionable decisions from BI tools, nowadays analytics and BI platforms also offer quick call-to-action options on their dashboard. This enables business deciders to take immediate action on the analytical insights.

Upon finding the need for quick actions in the low-risk use case, automation can sense well-structured data and initiate the required business processes straight from the platform. In real-world cases, one can easily automate the repetitive task of sending marketing emails to their prospective customers.


BI Automated Reports

RPA can aid in generating automated reports and streamline the insights through the entire enterprise. This helps you empower your decisions, timely actions and improve your performance metrics. For example, RPA can automate your company’s reporting activities and generate reports and reminders every week, which boosts productivity and saves a lot of time.


Closing Thoughts

Data analytics and BI capabilities have made a leap forward in recent years, along with an exponential increase in data volume and steady improvement of computational power and storage. With a myriad of technologies & applications, automation-enabled data analytics and business intelligence are definitely transforming the future of business. As the markets get more competitive, businesses that leverage automation, stand to benefit in multiple ways and get better results faster.

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Tags automation bi business intelligence data analytics