How to Overcome Data Extraction Challenges for Smart Business Performance

Learn how to overcome data extraction challenges by applying Artificial Intelligence for higher performance

In today’s digital economy, the value and importance of data have dramatically increased. Achieving sales forecasts, maintaining customer satisfaction, and improving profitability are now all heavily reliant upon a company’s data collection, management, security, and analytics capabilities. Much investment has been done to improve upon these competencies with mixed levels of success. One particular vexing challenge has been how to best find, collect, and extract the right data – and to do so quickly. Unless data is clean, indexed, and readily accessible, its value is minimized. What follows are suggestions on how you can overcome data extraction challenges to drive smarter business performance.

Definition of Data Extraction

Consider data extraction a process where data is collected, read, and analyzed to retrieve applicable information. The data can originate from an application, a database, a file, or other sources. This information is then replicated to a destination location – such as a data warehouse – in a format that can then be readily shared with applications, employees, or other digital ecosystems.

Isolated data is not of much value in today’s connected world. Data that can be shared and acted upon with other employees, departments, partners, or suppliers is enormously valuable. It can be used to improve decision support, drive growth, or improve customer satisfaction. The way to unlock the value of data is to share it – which is easier said than done.

Let’s take a look at what options exist to overcome data extraction challenges.

Is There An API?

If your data extraction challenge is focused on better sharing between two applications or systems, then the first thing to determine is if a digital integration between the applications is possible.

Referred to as an API or Application Programming Interface, this integration technology can dramatically simplify how applications share and exchange data. Since data can seamlessly pass through an API “intact” between applications, there is no data identification, classification, or extraction issue. An API must first be programmed to accommodate each application’s data metatag requirements, at which point a perfect data exchange system is possible.

In situations where an API can work for your business, take advantage of it!

When No API Exists

Businesses that work with large partner networks, such as Title, Real Estate, or Financial Services companies, tend to not have much control to standardize the applications and systems that are in use. In this case, an API strategy might not work. Adding further complexity to the situation, these organizations must rely on a data extraction process that often includes both structured and unstructured data. This info can reside in many different locations, be in different formats, or have inconsistent metatag labels or indexes.

In this case, there are several approaches companies will consider. One is to scan the documents, save them as a .pdf file, and then set up an Optical Character Recognition (OCR) process to extract the required information. Other approaches include doing “screen scrapes” from websites or HTML documents. The challenge is that this raw data must then be cleaned before it can be used – the extraction process is far from perfect. A review and verification process must come next, typically requiring a combination of manual or automated tasks that consumes a substantial level of resources, time, and cost.

The Do-it-Yourself Option

For some companies, given this is such an important process to get right, the decision is made to create a custom, home-grown system. This can take a substantial upfront investment, involve considerable resources, and then require further investment to maintain performance. Over time, source documents change, data input needs to evolve, and other variables will impact how the process works and the cost to maintain.

Those that pursue this strategy can achieve their desired objective, resulting in data conversion success rates that seldom exceeded 60-70%. That means 30-40% of the original data must still be manually inspected, cleaned, and updated before it can be used.

There is another option. Work with a managed IT services provider that has experience with the documents and files typical in your industry. Over time, this tribal knowledge combined with new digital tools can be leveraged to improve data conversion rates.

Read this announcement to learn more, Axis Technical Group Launches AI-Based Smart Data Extraction Service

How AI Can Overcome Data Extraction Challenges

It turns out that AI is an ideal complement to data classification and data extraction. The algorithms that are part of a machine learning procedure can provide extremely valuable intelligence to vastly improve the performance of a data extraction process. The learning process that can be applied will not only improve data conversion accuracy to the 90-95% performance level, but it can also learn over time how to adapt and continue to deliver even high performance – despite an environment where change continues, new documents emerge, new terms become predominant, as well as other industry changes.

Those considering the purchase of purpose-built software to overcome data extraction challenges should consider this article, How to Evaluate Data Extraction Software

Regardless of the strategy chosen to overcome your data extraction challenges, be sure to include a way to measure your data conversion success rates over time. Should you see a change in this metric, you can then proactively take action to address any process issue before it becomes a big and costly problem.