What are the challenges of data extraction, and how to overcome them?

In today’s digital economy, data is the lifeline of business operations. Data extraction is critical to driving your business intelligence and analytics with the correct data to gain valuable insights into customers, market trends, and the organization’s operations.

However, various factors can make the data extraction process complex, time-consuming, and challenging. Understanding those challenges and implementing appropriate data extraction solutions can ensure your methods are efficient, accurate, and secure.

In this post, you’ll get a fundamental understanding of data extraction, the key challenges, and how to overcome them to achieve much smarter business performance.

What is Data extraction?

Typically, most organizations have data spread across multiple sources, including CRM data, financial data, spreadsheets, PDFs, documents, files, images, etc. This is where the data extraction process comes into the picture. It’s about collecting and consolidating all such data and storing the information gathered in a specific format for further use.

Enterprises globally store extracted data in a data warehouse and later integrate the data with business intelligence & analytics tools to derive meaningful insights. As a result, it is possible to identify patterns and trends, understand market changes and quickly act on the insights discovered from extracted data.

Types of data that require extraction

Broadly, there are two categories of data: Semi-structured and unstructured data which is imperative for the extraction process to prepare the data for analysis and interpretation.

Semi-structured data

Unlike traditionally structured data, semi-structured data is less organized and contains more contextual information. As businesses collect enormous amounts of data from unstructured sources, semi-structured data is becoming more and more prevalent in IT systems.

Semi-structured data contains a level of structure but doesn’t confine to a rigid data model. This may contain crucial elements that cannot be classified. Using metadata, keywords, and tags can help categorize the data elements and provide additional information for business decisions. The implicit nature of semi-structured data makes it challenging.

As the relationship of the data is uncertain or complex, they pose various complications in designing and implementing data pipelines, difficulties in data storage, and leads to resource-intensive processes.

Using XML and Objects — Exchange Model (OEM) to store and exchange semi-structured data can simplify it. XML arranges the data hierarchically to enable the information to be indexed and searched in the database. At the same time, OEM allows data to be stored in graphs. Implementing a custom data extraction solution to store and extract semi-structured data will help reduce costs. It may offer added advantages like flexibility, scalability, and faster data processing.

Unstructured data

Unstructured data is a form of poorly organized data, typically with no predefined data model. Conventional algorithms cannot easily interpret this type of data. 80% of the business data generated is available in an unstructured format.

Although unstructured data has been prevalent in IT systems for decades, organizations face challenges dealing with rapidly growing data volumes and increasing cyber-attack vulnerabilities. Thus, extracting and converting information from unstructured data into manageable formats becomes difficult.

Data attributes are retrieved from the content compiled rather than from sources. Unstructured data is prone to errors, inaccuracies, indexing complications, missing values, and unclear structure. While you can access the data from emails, videos, PDFs, files, documents, and many more, maintaining data security can be hard while extracting unstructured data.

Organizations can fine-tune the extraction component to maximize their chances of a successful outcome with unstructured data. To read about maximizing your results from unstructured data extraction, refer to the blog on 5 steps to get maximum insights from unstructured data extraction.

Challenges of data extraction

Enterprises face challenges while processing semi-structured and unstructured data throughout the extraction. Here are some significant challenges and ways you can overcome them.

Maintaining data integrity

Data quality is a significant concern in any extraction initiative. The entire data processing cycle can be compromised by inaccurate data, resulting in faulty analytics. Poor quality data can lead to corrupted results, revenue loss, and reputation impacts.

To overcome this challenge, you should ensure the data is correct and accurate to load into the analytics. Outlining the critical indicators of poor-quality data in your data management system can help you stay updated on every step of the data pipeline. It is also essential to clearly understand the data needs and identify the sources to maintain data integrity.

Inability to process large volumes of data

Businesses are collecting more data than ever without being entirely aware of it. While most of the data is unstructured, it grows tremendously. Managing a massive volume of data requires advanced data extraction techniques. Acquiring only the required information from the data source can help gain a focused workflow.

To overcome this challenge, an automated data extraction solution can handle large volumes of data, identify patterns and extract the relevant information. It is also essential to ensure that data privacy control, data warehouse, and storage infrastructure are in place to protect the data from cyber-attacks and breaches.

Reduced usability of the data

Unstructured data is transformed into a machine-readable format before it is processed. This process can only delay decision-making without proper indexing and schema. It reduces the likelihood of the data being used to improve real-time business performance.

To overcome this challenge, you can leverage a data extraction solution to cover the entire process (extraction, processing, and loading) and have a comprehensive approach to achieving the desired growth with data.

Integration with existing systems

Data is often extracted from multiple sources so another system can process it. Integrating a data extraction tool with existing systems can be challenging as there might be unexpected complexities. Especially when the data formats are wildly varied, have more rigid data models, or are incompatible with existing systems.

To overcome this challenge, you should ensure that the data is in a compatible format with the existing systems. Adopting a custom API allows data extraction tools to automatically integrate the extracted data and communicate with other applications seamlessly.

Addressing the challenges of data extraction

Conquering these challenges in a timely fashion is critical to business success. Choosing a robust data extraction solution is the key to overcoming these crucial challenges. You can manage data from disparate sources at your fingertips by leveraging an automated data extraction solution. Integrating the solution with your database, applications, and other visualization tools can help you make better decisions faster.

Once you get over the initial hurdles in the data extraction process, you’ll find a bigger picture of how data contributes to your business — ultimately improving your business’s data-driven journey and developing clean data pipelines that deliver value.

Xtract.io allows businesses to automate their intelligent data extraction and get the accurate data required from any data format. Drop us a note if you encounter any of the above challenges in your data extraction process. Our data experts can help you overcome any data obstacles to maximize your business results.

Originally published at https://xtract.io/blog/data-extraction-challenges-and-how-to-overcome-them/ on March 7, 2023. Check out our blog to read more articles and follow us on Twitter or Facebook.