Financial institutions, on a daily basis, deal with a sea of data that includes product and service purchase histories, customer information, financial transactions, service inquiries, social media streams, software logs, and much more unstructured data from various mobile apps and devices.
Undeniably, this huge amount of data that professionals can use creates opportunities. But, most of this data is unstructured, which suggests: this critical data is inconsistent across systems and ignored as a result. The chances of financial companies suffering due to such challenges increase ten folds when data workers realize that more than 75% of the data they operate on is unstructured. Moreover, financial companies are under many regulatory scrutinies; therefore, efficient management of the massive unstructured data becomes more important.
The most efficient way to deal with such massive amounts of unstructured data is to have a strategically devised system for unstructured data management for financial services. However, before diving deeper into the needs and relevance of data management, it is only necessary to know about the various challenges financial enterprises face.
Challenges Faced by Financial Services Enterprises
Among the many concerns, the following are the major six challenges that financial institutions face:
- They have vast amounts of raw, unstructured data that can add significant value, but they struggle to use it because of their siloed, product-oriented structures
- Connecting data from many sources is another administrative challenge. They must extract insights from data housed on-premise, in the cloud, and hybrid environments, among other things. Then, they need to process the unstructured data and transform it into a trustable format, with a level of transparency that allows them to evaluate the data's origin and quality
- Current data management for financial services frequently fails to connect company data relating to customer information, contractual agreements, and financial transactions, among others, with semi-structured or unstructured data, such as emails, social media streams, and image, audio, and video files. This makes it tough to put data science into practice and reap the rewards of data-driven analytics
- Businesses may find it difficult to sift through huge haystacks of data in quest of useful insights. This gap also limits them from delivering data-driven innovations, an important part of the digital transformation process
- Data has grown less accessible due to the lack of meaningful connections across these silos, jeopardizing knowledge of customers, partners, products, sales channels, and financial performance
- The growing number of data consumption endpoints, business processes, and analytics solutions that require real-time data access to support decision-making exacerbates these difficulties
Efficient Data Management: The Solution
Collaboration across business units and departments of an organization regarding the orchestration, enablement, and workflow of a specific data domain is referred to as data management. In simpler terms, it is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management for financial services is to optimize data usage within regulatory limitations to make smart business decisions and maximize the benefit. Another major reason it is necessary is that it uses existing assets, integrates data across the entire technology landscape, and reduces data redundancies, making analysis easier.
Client, product, and asset data domains are common in the financial services industry. Mastering these domains gives data workers a complete picture of all the data contained inside them. Therefore, companies must consolidate their data into a single universe to reduce complexity. As a result, they can understand their operations better, improve efficiency, and employ better collaboration solutions like document workflow automation with universal data.
It's bothersome to track who has access to business-critical data, who has the authority to change it, what modifications can be made, and when those changes will be implemented. However, with systems that embrace document workflow automation, data workers can centralize these changes and better understand how they affect all business divisions.
Significance of Unstructured Data Management in Financial Services Enterprises
Having a robust unstructured data management strategy in place to gather, organize, and analyze unstructured data can assist in overcoming the issues above and lead to:
1. Enhanced productivity
Employees know where to look for information when they need it because it's all in one location and searchable. They may even speed up internal operations and minimize reaction times by using machine learning techniques to handle their unstructured data.
2. Faster, better, and more accurate decision making
High-quality data is dependable and helps data workers make better decisions. They can spot pressing concerns and act fast by using tools to evaluate unstructured data in real-time. Discovering trends in large datasets can also help them predict market developments.
3. Improved compliance
Maintaining organized and up-to-date data makes it easier to comply with current legislation and standards and prevent legal difficulties.
4. Stronger data security
Financial services are frequently subjected to data breaches and cyber-attacks in the digitally transformed business ecosystem. Effective data management allows them to protect their data, create backups, and monitor in real-time for any threats.
In short, knowing how to manage data successfully assists data professionals in extracting more value from unstructured data and converting that value into opportunities. As a result, data management for financial services is a critical component of delivering the enhanced customer experience that businesses strive for in the digital age.
Manage your Unstructured Data with Needl.ai
Since companies now realize that trusted, connected, and intelligent data leads to digital transformation and business success, they no longer view data as a cost driver but a crucial asset. Therefore, you can take the guessing out of financial services data management by teaming with a dedicated team that has already navigated similar waters.
To begin, we construct a central repository that stores data from all users' emails, chat groups, social media, storage platforms, note-taking apps, and even meta searches from prominent platforms such as YouTube, podcasts, and search engines. This gives your complete team access to the knowledge base through a single login. In addition, individual teams' previously unshared data can now easily make its way into a common repository, opening up a slew of new possibilities for intelligent collaboration. We also let you reference documents, search within them, and clip data from many documents into a single document with our document workflow automation solutions, removing the need to go from one platform to another and making creating and sharing reports simple for your team.
Employing data management for financial services offered by Needl.ai, you can empower your enterprise to build a modern data management system that allows vast "haystacks" of unstructured data to be automatically analyzed for hidden "needles" of actionable insight.