unstructureddatamanagement

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Unlocking the Value of Unstructured Data for the Future of Financial Services

In today's digital age, the future of financial services is built on unstructured data. Financial institutions, banks, and other capital markets are continuously innovating to keep up with the challenges of digital transformation. Still, many fail to utilize the intelligence within the unstructured data across their organizations. To stay ahead of the competition, it is essential to unlocking the value of unstructured data, which promises to yield insight into changing customer needs, allowing firms to stay ahead of future trends.

Structured vs. Unstructured Data

Structured and unstructured data domains have primarily remained separate. Automated processes focus on structured database files with defined fields, data types, and parameters. It is easy to assign, request and restrict access to these data types. Traditional keyword-based search and analysis methodologies are typically applied to structured data. In contrast, unstructured data is more complicated as it comes from various sources, such as records from call center interactions and CRM systems, earnings transcripts, financial filings, email exchanges, survey responses, and even PowerPoint presentations. Unstructured files have ambiguities, making them more challenging than structured data. As a result, vast stored repositories of unstructured data, sometimes called Dark Data, sit dormant because firms lack the tools to harvest their full potential.

Importance of Data in Financial Services

In the financial services industry, managing and analyzing large quantities of data are essential for success in a digitized world. Digitization of financial products helps develop new services and enhance customer insight and engagement. This is essential to keep pace with traditional institutions and digital-first disruptors like Monzo and Revolut. Consequently, financial institutions are considering how they can meld existing workflows and network infrastructure with digital analytics to achieve the benefits of better, more contextual decision-making.

Challenges Faced by Financial Institutions

Formerly conservative institutions, banks, and financial services face challenges blending legacy technologies with newer innovative technologies. New platforms and systems require massive capital expenditures, with commensurate planning and time for technology integration, migration, and rollout. These organizations are often tied to legacy infrastructure by regulatory mandates and the limits and complexity of existing installed software and hardware.

Data Security

Data security is a principal determinant for current systems and new technology acquisition. Unstructured data accessed on file servers and Network Attached Storage (NAS) devices pose a huge security risk. Data breaches and subsequent criminal abuse of compassionate information are a paramount concern. The rising tide of ransomware cyber-extortion exploits is particularly alarming as criminals look to encrypt entire networks and hold lifeblood data hostage for millions of dollars.

Most financial firms and banking institutions rely on prevention technologies, such as perimeter and endpoint security systems, to help solve this problem. However, they almost universally agree that there is no impenetrable defense. These organizations are only as strong as their weakest link, often a single credentialed employee innocently victimized by a phishing or whaling scheme.

Melding Existing Workflows with Digital Analytics

Despite these challenges, many enterprises have already adopted technologies that can be seamlessly utilized to achieve digitization goals. File-sharing systems, the most advanced of which provide unstructured data management capabilities, can make it easier and more economical to securely store and work with data assets at speed and scale. Some of these solutions have next-level cloud-native capabilities, increasingly augmented by AI and machine learning that can be optimized to elevate secure data collaboration and decision-making among cross-functional and distributed teams. The speed and accuracy at which this can be achieved are unprecedented, and built-in data security offers clear business benefits for capital market players.

Extending the Value of Storage

Extending the value of storage involves finding ways to use storage beyond its original intended purpose. This can include repurposing storage for different types of data or applications, using storage for data analysis and insights, and leveraging storage as a service for other businesses or organizations.

One example of extending the value of storage is using historical data stored in a data warehouse for business intelligence and analytics. By analyzing this data, businesses can gain insights into customer behavior, market trends, and business performance, which can inform decision-making and help drive growth.

Another example is repurposing storage for different types of data or applications. For instance, using object storage to store unstructured data, such as images, videos, and documents, instead of just structured data like transaction records can help businesses gain new insights and extract more value from their data.

Finally, storage as a service (STaaS) is another way to extend the value of storage. By providing storage services to other businesses or organizations, storage providers can generate additional revenue streams and help businesses reduce storage costs and complexity.

Overall, extending the value of storage requires a strategic approach and a willingness to innovate and explore new use cases and opportunities.

Top of Form

Benefits of Cloud-Based File-Sharing Technologies

Cloud-based file-sharing technologies provide several benefits for banks and financial services firms. One of the primary advantages is that it enables them to efficiently handle massive volumes of unstructured files, which can be challenging to store and retrieve while complying with the financial industry's higher control and regulatory requirements.

Moreover, cloud-based file-sharing systems allow organizations to migrate or re-platform data, workloads, and applications to the cloud without refreshing existing infrastructure. This capability is particularly beneficial for banks and financial markets firms that can use NAS replacement systems to move applications and data as-is to a public cloud while enhancing or replacing some components to take advantage of the growing plethora of cloud services without rewriting or workflow changes.

Improving Data Accessibility and Collaboration

Cloud-based file-sharing systems are designed to improve data accessibility and collaboration among cross-functional and distributed teams. They provide next-level cloud-native capabilities increasingly augmented by AI and machine learning, which can optimize secure data collaboration and decision-making. This allows teams to work with data assets at speed and scale, achieving unprecedented speed and accuracy.

Meeting Regulatory Compliance Requirements

Meeting regulatory compliance requirements is a top priority for banks and financial services firms. Cloud-based file-sharing systems can provide discreet, fit-for-purpose solutions that comply with regulatory mandates, allowing organizations to securely and compliantly store and work with data assets. These solutions offer built-in data security and clear business benefits for capital market players.

Optimizing Infrastructure Investment

Reducing the incumbent technical complexity of the environment and the cost of data management is a primary objective for banks and financial services firms. Cloud-based file-sharing technologies have documented reductions in the related data-storage footprint of around 70%, allowing organizations to use their infrastructure investment dollars more effectively.

Conclusion

The future of financial services is built on unstructured data, which promises to yield insight into changing customer needs, allowing firms to stay ahead of future trends and, most importantly, stay ahead of the competition. However, unlocking the value of unstructured data requires organizations to manage and analyze these large quantities of data effectively.

Cloud-based file-sharing technologies offer several benefits, including improving data accessibility and collaboration, meeting regulatory compliance requirements, and optimizing infrastructure investment. Banks and financial services firms can use these technologies to move applications and data as-is to a public cloud while enhancing or replacing some components to take advantage of the growing plethora of cloud services without rewriting or workflow changes.

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The Future of Finance Relies on AI and Unstructured Data

The finance industry has long been a crucial player in the global economy. Without it, economies would struggle to expand, and innovation would be stifled. However, digitization has significantly boosted financial services, making them more efficient and convenient than ever before. With the advent of automation technology and artificial intelligence, the industry is poised to take another giant leap forward.

One of the keys to this forward momentum is using AI to process unstructured data management. Unlike structured data, which is measurable and easily contextualized, unstructured data is unorganized and lacks a predefined data model. It serves no apparent purpose and is often inaccessible in its raw form. However, with the right analytics approach and tools, unstructured data can be converted into valuable information that can provide deeper insights into customer behavior, market trends, and product development.

Unstructured data is everywhere, and financial services are no exception. Documents like CRMs, customer service records, earnings transcripts, and tax documents all contain vast amounts of unstructured data that could provide a competitive edge to companies that know how to use it. Structuring this data can help companies create more innovative products and provide better customer service.

So how does AI help process unstructured data? By analyzing patterns and correlations within the data, AI can unlock insights that may not be apparent through structured data analysis alone. For example, AI can help service providers better understand customer behavior by analyzing unstructured data from documents like mortgage applications. AI can also help automate common customer requests, like the change of address or quote requests, leading to faster and more efficient customer service.

Perhaps most importantly, AI can unlock the total value of document data. By analyzing unstructured data across contracts, policies, transaction records, and other documents, AI can identify patterns that point to the need for new products or services, like loans or financial counseling. Companies can use this information to create new offerings or tailor existing ones to individual customers' needs.

As the financial services sector evolves, AI and unstructured data will become increasingly vital. Companies that embrace these technologies and use them to their fullest potential will have a significant advantage over those that don't. With Automation Hero, a versatile, intelligent document processing platform with AI designed to master every type of unstructured data, companies can turn their junk data into gold. Schedule a call with our experts today to learn more about how Automation Hero can help your company.

[unstructureddatamanagement]

The Importance of Unstructured Data Management

In today's digital age, businesses generate vast amounts of data daily. This data is collected from various sources such as social media, emails, chat messages, images, videos, and other unstructured data sources. Managing and utilizing this unstructured data is crucial for businesses to make informed decisions, gain a competitive edge, and stay ahead. This article will discuss the importance of unstructured data management, its challenges, and how businesses can effectively manage and utilize their unstructured data.

Introduction to Unstructured Data

Unstructured data refers to any data not organized in a predefined format. This data can be in the form of text, images, audio, or video. It is estimated that unstructured data accounts for about 80% of all the data generated by businesses. Managing unstructured data can be challenging, as it requires a different approach than structured data.

Importance of Unstructured Data Management

Unstructured data management is essential for businesses to extract insights from the vast data they collect. Without proper management, unstructured data can be a liability rather than an asset. Here are some reasons why unstructured data management is essential for businesses:

1. Better Decision-Making

Unstructured data contains valuable insights that can help businesses make informed decisions. By analyzing unstructured data, businesses can identify patterns, trends, and anomalies that may not be visible in structured data. These insights can help businesses make better decisions, improve customer experience, and optimize operations.

2. Competitive Advantage

Businesses that effectively manage their unstructured data can gain a competitive advantage. By leveraging unstructured data, businesses can gain insights into their customer's behavior, preferences, and needs, which can help them develop more targeted and personalized marketing campaigns. This can help businesses improve customer loyalty, increase sales, and gain a competitive edge in the market.

3. Cost Savings

Unstructured data management can also help businesses save costs. By effectively managing their data, businesses can avoid unnecessary storage costs, reduce the risk of data breaches, and optimize their data processing workflows. This can lead to significant cost savings for businesses, which can be invested in other business areas.

Challenges of Unstructured Data Management

Unstructured data management comes with its own set of challenges. Here are some of the common challenges businesses face when managing unstructured data:

1. Volume

Unstructured data is generated in large volumes, making it difficult to manage and analyze. Businesses must invest in advanced tools and technologies to manage and process large volumes of unstructured data effectively.

2. Variety

Unstructured data comes in different formats, making it challenging to store and process. Businesses must ensure their data management systems can handle different unstructured data formats.

3. Quality

Unstructured data can be of varying quality, making it difficult to extract insights from it. Businesses must ensure that their data management systems can filter out irrelevant or low-quality data and focus on high-quality data that can provide meaningful insights.

4. Security

Unstructured data can be vulnerable to security breaches, making it essential for businesses to have robust security measures to protect their data. This includes implementing access controls, encryption, and data backup and recovery systems.

Effective Management of Unstructured Data

Businesses must follow a few best practices to manage their unstructured data effectively. Here are some tips on how businesses can effectively manage their unstructured data:

1. Define Goals and Objectives

Before implementing any unstructured data management strategy, businesses need to define their goals and objectives. This includes determining what insights they want to extract from their unstructured data and how they plan to use those insights to achieve their business objectives. By defining their goals and objectives, businesses can ensure that their unstructured data management efforts are aligned with their overall business strategy.

2. Invest in Advanced Data Management Tools

Businesses must invest in advanced data management tools and technologies to effectively manage unstructured data. These tools can help businesses store, process, and analyze large volumes of unstructured data and extract meaningful insights. Some of the popular tools used for unstructured data management include Hadoop, Spark, and NoSQL databases.

3. Implement Data Governance Policies

Data governance policies are essential for ensuring unstructured data quality, accuracy, and security. These policies define the rules and procedures for collecting, storing, and processing data and ensure that businesses comply with privacy and security regulations.

4. Use Machine Learning and AI

Machine learning and AI can help businesses analyze unstructured data and extract insights automatically. These technologies can help businesses identify patterns, anomalies, and trends that may not be visible to the human eye. This can save businesses time and resources and help them make informed decisions based on data-driven insights.

5. Integrate Data Sources

Businesses need to integrate data from different sources to manage unstructured data effectively. This includes social media, emails, chat messages, images, videos, and other unstructured data sources. Integrating these sources can help businesses get a more comprehensive view of their customers, needs, and preferences.

6. Train Employees

Effective unstructured data management requires skilled professionals who can use the right tools and techniques to extract insights from the data. Businesses must invest in employee training to ensure they have the necessary skills and knowledge to manage and analyze unstructured data effectively.

Conclusion

In conclusion, unstructured data management is essential for businesses to extract insights from the vast amount of data they collect. By effectively managing their unstructured data, businesses can make informed decisions, gain a competitive edge, and save costs. However, unstructured data management has challenges, including volume, variety, quality, and security. By following best practices such as defining goals and objectives, investing in advanced data management tools, implementing data governance policies, using machine learning and AI, integrating data sources, and training employees, businesses can effectively manage their unstructured data and gain meaningful insights from it.