The 3 Most Common Data Management Challenges in the Financial Industry
Tags: datamanagement, fintech, tradingtech, quant
Published: 2020-01-30 17:11:14

Financial institutions look to grow their revenue by reducing risk, cutting costs, and making wise business decisions. Business decisions increasingly rely upon volumes of data which can pose serious production challenges in areas including: data ingestion, data quality and data production.

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<h2>Production Challenges</h2>

If these problems are not overcome, they will become detrimental to your business by unnecessarily wasting time, man-power and money.

<b>1. Manual Data Ingestion</b>
It can be difficult and time consuming to track and ingest so much data from so many vendors. Leveraging an automated approach can help re-focus your efforts towards strategy and trading if much of your organization’s time and resources are committed to this. Thoroughly tested extract-transform-load (ETL) pipelines managed by an experienced operations team coupled with meaningful and well-laid-out dashboards make data ingestion easy providing your team with confidence and a strong foundation necessary to build a performant data analytics system.

<b>2. Poor Data Quality</b>
Not having the proper analytics is like steering a ship blind. Poor data quality is just as bad if not worse. If you cannot rely on your data for accuracy then you will not be able to rely on your forecasts drawn from that data. Data quality should be built into the data production pipeline early rather than later so issues can be found, marked and fixed before production data sets are built.

Cleaning data draws focus away from prime goals. Usage of machine learning and other statistical approaches can put your team back in the business of trading confidently knowing that your data is of the highest quality.

<b>3. Slow Data Production</b>
Many financial firms are still working with legacy software that is not geared for today’s data volume. Processing large data volumes quickly will require new techniques such as:
<li>Parallelization across many nodes</li>
<li>Vectorized processing</li>
<li>Distributed file systems</li>
<li>Efficient file formats such as Parquet and HDF</li>
<li>Pattern-based (machine learning) and statistical algorithmic approaches</li>
<li>GPU and other SIMD techniques</li>
It will be difficult to keep up with increased volume, variability and breadth of data in the future if these techniques are not implemented.

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<h2>The Outcome for Financial Firms</h2>

If these challenges are not met, financial firms will experience inefficiencies during all phases of their ETL. Problems that arise from manual data ingestion will only be exacerbated by a slow production pipeline. Without advanced customized software, your data will be of lower quality, more difficult to maintain and contain less actionable insights. Without better reliable data quality processing, you won’t be able to detect anomalies. All of this together produces bad data and leads to higher data management costs, increased risk and ultimately revenue loss.

This is definitely not the desired outcome, but it is not easy to adapt to an ever-changing technology landscape. Most financial firms do not have the man-power, time or resources to easily address these issues. That is why there are third party companies that exist who have already solved these problems.

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<h2>Third Party Data Management Solution</h2>

Technology and the financial industry landscape is evolving quickly making it difficult to keep up with while maintaining your core business. With the help from a financial data management company, one with a proven track record and decades of experience such as <a href="https://www.codewilling.com/">Code Willing</a>, you can stop fighting with your data and start leveraging it.

Only a few fintech firms provide end-to-end management of data production resources at this time but with more on the rise. They have a staff of Data Scientists, Data Experts and DevOps engineers that have experience ingesting cleaning, organizing, building and cross-referencing financial data sets from many vendors. Some have already developed complete solutions to these common data management problems and can help jump-start your technology and workflows into the future right now allowing your team to spend less time on administrative tasks and more time closing deals.

End-to-end financial data management firms can ensure a high-quality data product complete with analytical dashboards to provide insight into data content and the tools necessary to allow your team to extract targeted data allowing for decisions to be made that reduce risk, lower costs and increase revenue.

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The data revolution is here and is firmly rooted in the financial industry. Data will increase in volume, variability and complexity stressing ETL pipelines. Both data quality and processing speed will become more important and more difficult to handle requiring an experienced team of specialized data, coding and operation engineers to solve.

With the right fintech team working for you, your firm can overcome common data management challenges, become more efficient and gain an edge over the competition.