In part 1 of the blog, we read about what data ingestion is and why financial institutions must automate this. In this blog, we will look at the benefits of Automatic Data Extraction in Finance.
Be it the approval of loans or predicting future trends, financial institutions are relying more and more on big data. The latter refers to the petabytes of structured and unstructured data that FIIs have access to.
The finance industry deals with structured data—data stored within the organization —and unstructured data coming in through various channels that offer analytical opportunities.
What is Data Extraction?
Data extraction – the process of converting semi-structured or unstructured data into a more formal structure – is a vital requirement of this process. But the sheer volume of data today means the process has to be automated.
Despite this, many financial companies and their departments continue with manual data extraction processes, creating serious bottlenecks in the pipeline.
Automated data extraction throws up data from a broad spectrum of sources such as invoices, emails, and contracts.
Automated data extraction software helps finance companies pull data from various sources, saving time and costs and improving their data-driven decision-making processes.
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Why is Auto Extraction of Data Required?
Why is data extraction important? Data extraction is crucial for automating structured data collection and using it further down the pipeline in analysis.
The process provides necessary data from various sources, including invoices, bills, statements, and correspondence like agreements. These data help automate processes and provide valuable insights and analytics for decision-making.
Here are some of the reasons why you need to have an extraction of data in the “auto” mode if you are a finance company:
1. Faster decision-making: It goes without saying that auto-data extraction allows, without any manual intervention, companies to extract the relevant information concealed inside unstructured data sources.
2. Cost savings: Automatic data extraction helps you not only to make a profit but also to save costs. Manual data extraction processes are not only expensive but labor-intensive.
Take invoices as an example: Any decent-sized company processes thousands, if not millions, of bills a year. Many companies currently process these manually. Now imagine how much money and time could be saved if it were automated?
3. Reduction of manual errors: There’s an adage – wherever there are humans, mistakes follow. Manual errors, i.e., any form of physical entries, can add to your bottom line. Entries can be incomplete, missing, or even duplicated. Automatic data extraction reduces such errors significantly.
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4. Reduces time-to-market period: Surveys show that many companies blame their inability to merge data in a timely fashion as the main reason for not achieving their business goals.
It so happens that even as FIIs are preparing for data analytics, they discover errors in the data ingestion process. All of this means valuable time lost, which can sometimes translate into millions of dollars.
5. Increases scalability: Automatic data ingestion means you can start off ingesting data from 10 or even fewer sources and then go on to many more within a matter of hours.
No need to keep going to your IT department to implement every new data source. Which means smooth and uninterrupted operations.
As we said before, data ingestion is how you acquire and then import data. The latter is necessary for preparing the data for analysis. Such ingestion can happen in real-time or in batches at periodic intervals.
Data ingestion includes data extraction, transformation, and loading. Step one involves retrieving data from all your sources, followed by validation and cleaning, and then loading the data into the correct database.
It is evident that as your data volume grows, manual data ingestion is no longer possible, & this is especially true in financial institutions.
A finance company can easily have over 300, if not more, sources of data, with most bringing in data around the clock. Also, this data flows into the company in different formats, so it needs to be converted into familiar ones.
Conclusion
Finance companies and departments must automate their data ingestion and extraction processes due to the large volumes of incoming data. The benefits of automation are obvious and immediate. Your business teams should not spend a significant portion of their time on tedious, pre-analysis work. Help your data scientists re-focus on their primary task, which is analysis, rather than spending their valuable time on data ingestion.