Unstructured Data Challenges for 2024 and their Solutions

Unstructured data is information that does not have a pre-defined structure. It’s one of the three core types of data, along with structured and semi-structured formats.

Examples of unstructured data include call logs, chat transcripts, contracts, and sensor data, as these datasets are not arranged according to a preset data model. Unstructured data must be standardized and structured into columns and rows to make it machine-readable, i.e., ready for analysis and interpretation. This makes managing unstructured data difficult.

Unstructured data is of growing importance, considering more than 80% of business data is available in an unstructured format. If that wasn’t enough, unstructured data is projected to grow rapidly in 2024, and beyond. Plus, it’s not just about the volume; unstructured data sources contain valuable insights. Purchase invoices, for example, can help a telecom provider segment its customers based on their demographic and economic details. This is just one example; unstructured data can be used in numerous ways to unravel patterns and trends for improved decision-making.

Despite its importance, many enterprises face challenges accessing and using unstructured data. Some of these challenges are:

• Inability to process growing data volumes
• Accessing siloed data
• Regulatory non-compliance
• Reduced data usability
• Increased vulnerability to cyber-attacks

Let’s discuss these challenges in more detail and how enterprises can overcome them.

Overcoming Unstructured Data Challenges

Challenge # 1: Inability to Process Growing Data Volumes

Businesses are collecting ever-growing amounts of information nowadays. The volume of global data is projected to rise to 175 Zettabytes by 2025. This presents the challenge of accurately capturing this data in a timely manner.

Enterprises need to capture and store unstructured data to extract valuable insights. But without proper storage planning and solution, this increasing data volumes put pressure on existing storage capacity. Of course, traditional, on-premises storage solutions cannot handle petabyte-scale data.

Enter cloud-based storage. Migrating data to the cloud is part of a flexible and scalable approach to data storage. Online data warehouses offer many benefits, such as connectivity to multiple unstructured data sources, faster analysis, and smoother disaster recovery.

A robust data integration tool simplifies connecting to cloud storage. Astera Centerprise streamlines data migration to the cloud while preserving data quality in a no-code environment. Furthermore, its automation capabilities allow business users to capture and transfer unstructured data in real time.

Challenge # 2: Accessing Siloed Data

In today’s digitized work environment, employees demand greater transparency from their employers. Privacy acts such as CPRA and GDPR have emphasized safeguarding employee information and improving employees’ access to their data.

Moreover, employee requests to access their personal details are increasing. The challenge is to provide seamless access to sensitive information stored in data siloes across multiple destinations, such as chats, emails, and audio logs.

The first step toward solving this challenge is discovering sources of employee information. The next step is combining disparate information stored across multiple systems and building a single repository. Subsequently, employers must implement a robust ID verification and data masking mechanism to prevent data leaks.

Ethically managing employee data, providing it on request, and communicating new laws regarding employee privacy help cultivate an environment of trust within an organization.

Challenge # 3: Regulatory Non-compliance

Unstructured data often goes unchecked as it’s difficult to store and analyze. As per IDC, around 90% of this data remains unutilized, and most companies are unaware of where it resides. Unregulated data can lead to numerous legal and compliance risks, for example:

• Sensitive information, such as customer details, may be lost in a data breach if not adequately secured.
• Using unstructured data for marketing purposes may undermine the consent taken during data gathering. For instance, Using real customer invoices to showcase a software’s functionality is a breach of privacy that may lead to a lawsuit.
• Uncategorized data may be stored in secondary storage. Privacy regulations require businesses to store sensitive information in their primary storage.
• Non-compliance with employee requests for information retrieval and deletion can harm a business’s reputation.

Non-compliance with employee requests for information retrieval and deletion can harm a business’s reputation. How can enterprises stay within the bounds of privacy laws? By prioritizing identifying untagged data and empowering workers to recognize and review it.

A company must locate unstructured data sources within the company and establish guidelines on what constitutes personally identifiable information (PII). All sensitive information should be marked and stored securely and must only be accessible to authorized users.

Challenge # 4: Reduced Data Usability

Reduced data usability presents another challenge for utilizing unstructured data. Companies must transform unstructured data into a machine-readable format before processing it. This data also needs indexing and schema to be useful. The additional data processing requirements increase time-to-insight, which can cause delays in decision-making.

For instance, scanned receipts cannot be parsed directly and must be passed through an OCR tool to capture relevant data. Similarly, social media posts must be scraped and converted into a structured format to conduct sentiment analysis.

Nowadays, data extraction tools can automate data extraction, processing, and loading, essentially the entire process. These solutions can scrape and process unstructured data at scale. Most companies prefer zero-code solutions that allow them to structure unstructured data without writing any code.

Astera ReportMiner is a powerful tool that simplifies unstructured data extraction and processing. Equipped with advanced AI capabilities, it allows users to generate templates with one click and ensures data, accuracy, and completeness through extensive data quality checks.

Challenge # 5: Increased Vulnerability to Cyber Attacks

Egnyte’s 2021 Data Governance Trends Report states that unchecked data growth and disorganization increase cyber risk. This is particularly true for unstructured data as it’s more prone to mismanagement and stored in siloed data systems.

Small to medium enterprises are at greater risk of data breaches. In addition to data loss, cyber attacks can result in loss of customer confidence and heavy fines. It can permanently damage a brand’s credibility and reputation.

The solution to increasing data security threats is not just strengthening security protocols. Companies need to identify scattered data and consolidate it into a centralized repository to minimize political vulnerability. They should also create a procedure for securely storing new incoming data.

An end-to-end data integration tool is an excellent option for consolidating data from multiple unstructured sources. Choose a solution that offers robust security and user permission features to ensure data integrity and security.

Apart from the five challenges stated above, there are other obstacles to utilizing unstructured data effectively. Douglas Laney, a leading authority on data and analytics, explained some of these challenges in a recent webinar.

How Enterprises Can Utilize Unstructured Data – A Telecom Perspective

We’ve discussed the challenges of managing unstructured data. Now let’s look at how this data can help create value. The Telecom industry is an excellent case as telecom providers (telcos) collect large amounts of information through call, network, and customer data. This information can be analyzed to extract valuable insights.

Telcos predict the churn risk for each customer by analyzing their past purchases. Predicting customer churn involves comparing current customer data to churned customer data and building a prediction model through a classification algorithm. Consequently, telcos can target customers at a high risk of churning through customized packages. Proactive targeting can significantly reduce customer churn and save time and money in attracting new customers. Other benefits include a more satisfied customer base with higher LTV.

There are other applications of data mining apart from churn prediction. By analyzing call detail records, they can find the most called places by their customers. Perhaps a large subset of customers makes regular calls to Spain. These insights help them design relevant international calling plans.

How Automated Data Extraction Fits Here

Data analytics help uncover profitable insights for telecom providers. There are additional benefits apart from crafting relevant marketing campaigns. Insights gained from data analysis can assist in reducing call fraud and better network optimization.

However, effective analytics requires structured and cleansed datasets. Even the most powerful analytical tool will be ineffective without accurate data. Extracting, preparing, and combining data from multiple sources is essential to view a complete picture.

An automated data extraction tool is essential to capture unstructured data. An ideal solution must be capable of accurately and quickly extracting raw data with minimal human intervention. It must also contain data validation checks to ensure data quality.

Enterprise-grade data extraction solutions like ReportMiner automate and streamline extraction to help organizations reach actionable insights faster.