There’s no doubt that automation is changing, for the better, business performances in Finance. Automating data extraction and ingestion adds value to finance departments and institutions.
A recent survey by Dun & Bradstreet of finance operations in the US and the UK found that improved process speed was the leading motivator for automation, followed by cost savings.
The study concluded that when driven by analytics, automation could reduce operational costs, boost efficiency, and open more avenues of growth for finance teams by scaling and pulling in data from multiple sources at once.
Not only that, the report said enterprises that were driven by data and insights were 39 percent more likely to report year-over-year revenue growth of 15 percent or more.
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Indeed, data automation can bring to the fore the true potential of humans, machines, and data.
Yet studies like the one mentioned above, and others, have shown that the pace of automatic data extraction and ingestion has not picked up as much as one would have liked.
Surprisingly, one would believe that businesses tend to lean towards such “digital robotization” to get ahead in the game.
Also, using data scientists or analysts to perform repetitive tasks that can be automated is a waste of resources.
Quick Primer on Data Ingestion
Data extraction is crucial to automating the extraction of structured data for analysis. It provides data from multiple sources, such as balance sheets, invoices, timesheets, contracts, and more.
Data extraction is also the process of converting unstructured data into a more formal form, since it is such structured data that yields meaningful insights for analytics. But the cycle does not stop at merely mining data.
The data ingestion layer is the backbone of an analytics architecture.
There are many types of data ingestion; extract, transform, load (ETL) is one of them. It has the following steps:
- Data extraction: Mining data from sources like databases or websites.
- Data transformation: In tune with specific business rules, the mined data is transformed. Includes data munging /cleaning, and then structuring it to match the schema of the target location, such as a data lake or a data warehouse.
- Data loading: This ‘clean’ data is then loaded into the target location. There are many ways to ingest data, often guided by models or architectures. Batch processing and real-time processing are the most commonly used.
- Batch processing: Here, at a pre-determined point in time, the ingestion layer collects and groups the source data. It then sends it to the intended destination. Triggers can be either the activation of specific conditions or a time- or day-based schedule. For example, many banks use batch processing for customer statements, credit card billing, and other tasks because it is both convenient and cost-effective.
- Real-time processing: This is also called stream processing, and as the name suggests, data is sourced, transformed, and loaded in real-time; in fac,t as soon as it’s created. Obviously, this is more expensive than batch processing, as it requires systems to monitor sources 24×7.
How does data ingestion power extensive data set usage in finance? >>>> Learn more
Why Automated Data Extraction and Data Ingestion
Compared to other sectors, Finance places a high priority on improved analyses, which, in turn, means data integration must occur more frequently than in other sectors.
FinTech operations are about continuously imbibing a constant stream of information. Such ingestion has to meet a high standard of quality control because an undetected mistake can quickly lead to losses of millions of dollars.
Fundraising, reporting, and management systems are critical to the business of any FII and banking institution.
Here, the manual process of tracking investors and their investment choices wastes time on low-value activities and is ideally suited to automation. The benefits of this are many, some of which we enumerate here:
- Quicker Decision-Making: To reiterate, automation enables users to extract insights from unstructured data more quickly than manual ingestion.
- Highly scalable: Manual data ingestion is only as fast as the person inputting the data. Hence, scaling up will always be a problem. But with automation, FIs can start with a handful of data sources and automate them, and then scale up data automation over time.
- Cost savings: Consider what a company would pay to manually key in 1000+ invoices, and the low cost of fully automating the process.
- Low risks: Data is the start-off point in any business, and the same is true for a finance company or operation. Any error in the data ingestion process means not only a monetary loss but also a loss of reputation. Automated data ingestion mitigates that risk by eliminating human error that could otherwise be introduced during the ETL process.
- Improve time-to-market goals: Because of poor data quality or slow data ingestion, companies often struggle to complete their analyses on time and thus cannot achieve their business goals. So, the failure to launch your service or product on time means your business loses its competitive edge. Automation essentially eliminates this pain point.
In Part 2 of this post, we will detail the benefits of automatic data ingestion.
