Before the 2000s, “big data” was a relatively unknown buzzword outside of the tech industry. But now the threefold formula of volume, velocity, and variety that characterizes big data has become a core part of many modern business strategies. Big data technologies are now widely in use in the retail, manufacturing, and transportation sectors, among others. They are also gaining quite a bit of traction in the finance industry for entities like banks and lending corporations.

As such, the various challenges of a business environment powered by big data can’t be ignored by financial institutions for too long. Those who ignore big data solutions now may later watch haplessly as their peers overtake them in the finance industry. To get an edge over the competition, it’s a good idea for your company to embrace big data and invest in a solid data foundation.

Consider harnessing the power of big data for your company and confronting these five challenges. Doing so may ultimately prove rewarding for your business.

The Challenge of Onboarding New Data Analytics Solutions

The challenge of onboarding new data analytics solutions goes beyond just convincing people to invest in new software. The first challenge pertains to the issue of scaling up your company’s technology to accommodate big data. Admittedly, this is not the kind of decision that can be made with the snap of a finger. While some people may be resistant to change, it's essential to recognize the potential benefits of adopting new analytics software. If current methods for data analysis and reporting seem to work on a day-to-day basis, why change them?

One major advantage of new data analytics solutions is the ability to access and analyze larger volumes of both structured and unstructured data. By consolidating financial data from different sources such as

1. Revenues,

2. Treasury,

3. Risk management, and

4. Compliance,

You can gain more insights into your business operations and make more informed decisions. This can help your company stay ahead of the competition and respond quickly to changing market conditions.

However, onboarding new analytics software requires some level of training and adjustment, particularly for staff who are used to a manual system for data processes. To overcome this challenge, it's important to invest in proper training and support for your employees. By doing so, you can ensure that they feel comfortable and confident with the new software and that they are able to use it effectively to make data-driven decisions.

The Challenge of Handling Data of Better Quality

That said, it’s not enough to have the most cutting-edge data analytics solutions on your side. The availability of high-quality data is crucial for any organization that seeks to leverage data analytics for informed decision-making.

The challenge of handling data of better quality lies in ensuring that the data collected has the following benefits:

1. Accurate,

2. Reliable,

3. Complete,

4. Consistent,

5. Timely.

The first step to handling data of better quality is to establish data governance policies and procedures that define data quality standards, data ownership, data lineage, and data stewardship roles and responsibilities. Data governance ensures that data is captured, stored, and managed in a consistent and standardized manner across the organization, reducing the risk of data inconsistencies, redundancies, and errors.

Inaccurate or incomplete data can lead to incorrect insights, flawed conclusions, and wrong decisions, which can negatively impact the financial performance of the organization.

Handling data of better quality involves a combination of processes and technologies that aim to improve:

1. Data accuracy,

2. Completeness,

3. Consistency, and

4. Timeliness.

For instance, data validation, cleansing, and enrichment techniques can help to identify and correct errors, inconsistencies, and gaps in data. Data governance policies and procedures can also help to ensure that data is captured, stored, and managed in a consistent and standardized manner across the organization.

Moreover, organizations need to invest in data quality tools and technologies that can help to automate data quality checks and validation processes. These tools can help to identify data issues in real time and trigger alerts to data stewards, who can then take appropriate corrective action.

The Challenge of Automating Data Ingestion

The third big data-related challenge pertains to better data management practices. The challenge of automating data ingestion is a critical issue faced by many organizations today. Data is growing at an unprecedented rate, and businesses need to process and analyze this data quickly and accurately to make informed decisions. A big part of this involves weaning staff out of manual processes and helping them become proficient with automation technologies.

However, manually ingesting and processing data is time-consuming, error-prone, and inefficient. Automating data ingestion can help organizations overcome these challenges and enable them to make better decisions faster.

However, automating data ingestion presents several challenges that need to be addressed.

1. Data should be correct

The first challenge is ensuring that the data is accurate and complete. Inaccurate or incomplete data can lead to incorrect conclusions and poor decision-making. To address this challenge, organizations need to establish a process for validating and cleaning data before it is ingested.

2. The prompt ingestion of data

The second challenge is ensuring that data is ingested in a timely manner. Data needs to be ingested and processed quickly to provide real-time insights. This requires a robust data ingestion pipeline that can handle large volumes of data and scale as needed.

3. Security

The third challenge is ensuring data security. Data is a valuable asset, and organizations need to protect it from theft or misuse. This requires implementing robust security measures, such as encryption, access controls, and monitoring.

To overcome these challenges, organizations need to invest in the right technologies and processes. This includes implementing an automated data ingestion pipeline that can handle large volumes of data and scale as needed, establishing data quality controls to ensure data accuracy and completeness, and implementing robust security measures to protect data.

Once your company masters this challenge, however, you’ll notice how much money you’ve saved from manual inefficiencies. Soon enough, you’ll be reaping the fruits of better efficiency from business process automation automation and making it a longstanding element of your data processes.

The Challenge of Acquiring Data in a Timely Manner

Acquiring data in a timely manner is a critical challenge for many organizations, particularly in the finance sector where the pace of change can be rapid. Legacy systems and traditional data acquisition methods can make it difficult to keep up with the need for real-time data, which is essential for making informed decisions.

# Try New Tech

To meet this challenge, organizations need to adopt new solutions and implement new techniques that enable the timely acquisition, processing, and release of data. This requires a comprehensive data strategy that considers the entire data lifecycle, from acquisition to analysis and reporting.

# Go for platforms for broadcasting real-time data

One key solution to acquiring data in a timely manner is real-time data streaming platforms. These platforms enable organizations to ingest and process data as it is generated, providing real-time insights and enabling quick decision-making. Real-time data streaming platforms are particularly useful in the finance sector, where rapid changes in market conditions require quick responses.

# Apply the AI Power

Another solution is the use of advanced analytics and artificial intelligence (AI) to automate the data acquisition and processing process. These technologies can help organizations quickly analyze and derive insights from large volumes of data, reducing the time required to acquire and process data.

To implement these solutions, organizations need to invest in the right technologies and establish robust data governance and data quality processes. This includes implementing data pipelines that can handle large volumes of data and scale as needed, establishing data quality controls to ensure data accuracy and completeness, and implementing robust security measures to protect data.

The Challenge to Use Data Purposefully

The last challenge—and perhaps the most important one—is to use big data purposefully. The challenge of using data purposefully is crucial for financial institutions looking to leverage the power of big data to drive business value. It's not enough to simply scale up to big data solutions without a clear understanding of how the data will be used to achieve business objectives.

# Define clear business goals

The first step in using data purposefully is to define clear business goals and identify the data that will support these goals. This requires a deep understanding of the business and its operations, as well as an understanding of the types of data available and how they can be used to gain insights.

# Establish data-driven culture

Once the goals and data have been identified, it's essential to establish a data-driven culture within the organization. This means ensuring that all employees understand the importance of data and how it can be used to drive business value. It also means investing in the right tools and technologies to support data-driven decision-making.

# Customer is the king

Another key aspect of using data purposefully is to focus on the customer. Financial institutions must be able to gather and analyze customer data in order to provide personalized, customer-centric solutions. This requires a deep understanding of customer needs, preferences, and behaviours, as well as the ability to analyze large volumes of data to identify trends and patterns.

To use data purposefully, financial institutions must also prioritize data quality and governance. This means establishing robust data quality controls to ensure data accuracy and completeness and implementing strong data governance policies to ensure that data is used ethically and responsibly.

Conclusion

The financial industry is facing unprecedented challenges in today's fast-paced, data-driven world. To remain competitive, financial institutions must embrace new technologies and innovative approaches to stay ahead of the curve. This puts a great burden on the shoulders of key decision-makers, including chief financial officers, who must not only market their products but also drive innovation and keep up with the latest trends in the industry.

By taking a proactive stance on data challenges, financial institutions can harness the power of big data to drive innovation, improve customer experiences, and increase efficiency. This means investing in the right technology and tools, building a culture of data-driven decision-making, and attracting and retaining top talent with the skills to analyze and interpret complex data sets.

By leveraging customer data to personalize experiences and anticipate needs, organizations can build trust and loyalty and differentiate themselves in a crowded marketplace.

Call us at 484-892-5713 or Contact Us today to know more details about big data challenges every Financial Institution should rise up to.