What is Data Extraction?
Data extraction – the process of converting semi 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 finance departments in many companies 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 automatically pull data from various sources, thus saving time and costs, and improving their data-driven decision-making processes.
Want to know more about the intricacies of data ingestion? Speak to our experts by filling out this form.
Why Is Auto Data Extraction Of Required?
Why is data extraction important? Data extraction is very important to automate structured data collection for using it down the pipeline in analysis. The process provides necessary data from various sources like invoices, bills, statements, or correspondence such as agreements. These data help automate processes and to 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 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 million+ bills a year. Many companies currently process these manually. Now imagine how much money and time can be saved if it was automated?
3. Reduction of manual errors: There’s an old 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.
How Financial Planning and Analysis Software Can Help Your Business
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 find out about errors in the data ingestion process. All of this means valuable time lost, sometimes translated 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 the data extraction, its transformation, and loading. Step one means retrieving data from all your sources, followed by its validation and cleaning, which is then followed up with data loading in the correct database.
It is but obvious 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 these need to be converted into familiar formats.
In conclusion: Finance companies and finance departments have to automate their data ingestion and data extraction process because of the large volumes of incoming data. The benefits of automation are obvious and immediate. Your business teams cannot be spending a major portion of their time doing tedious, pre-analysis work. Help your data scientists re-focus on their primary task, which is analysis, rather than spend their valuable time on data ingestion.