Financial transaction analytics 101: Efficiently identifying impact

The foundations of financial transaction analytics and how database pipelines play a part in trend analysis.
October 5, 2023

According to Gartner, misuse of financial analytics can cost organizations as much as one percent of revenue per decision. In today’s data-driven world, it’s one of the most essential analyses an organization must complete — but without the right data stack, it’s also one of the most difficult. 

That’s especially apparent when you consider financial transaction analytics. Financial transaction analytics is a subset of general finance analytics. It requires extracting insights and identifying patterns from large volumes of financial transactions data to help predict and plan for the future. By showcasing the impact of a transaction on assets, liabilities and revenue, organizations have the foresight to make informed, agile decisions. 

Critical projects fueled by transaction analytics

Transaction analytics sets the foundation for some crucial finance analytics projects such as:

Financial reconciliation 

Financial reconciliation is the quarterly activity of verifying financial information to ensure its accuracy. When completed manually, this task consumes a substantial amount of time and is highly error-prone due to the involvement of multiple systems. The byproducts of transaction analytics, such as well-documented transactions, data enrichment and pattern identification facilitate the detection of discrepancies and enable the acceleration of financial reconciliation.

Revenue analysis

Revenue analysis provides detailed information on revenue streams of an organization over a period of time. It helps determine the most profitable products or channels, identify areas to reduce costs and perform historical comparisons. Transaction analysis provides a granular view into revenue sources, identifies customer buying patterns and pricing patterns to further enrich revenue analysis projects like automating dashboards. 

Customer Lifetime Value and churn analysis 

Customer Lifetime Value (CLV) is the revenue generated from a customer over their buying lifetime. In simple terms, it measures the total spend of a customer with the company before they churn. Transaction analysis enables the identification of patterns among at-risk customers or the actions a customer may exhibit prior to churning.

How transaction analytics rely on databases 

Transaction analytics is primarily designed to take informed decisions and corrective actions based on insights generated by transactions. Financial transaction data, by its nature, is structured, sensitive and accumulates in substantial volumes. 

Due to their large data volume, databases are a popular tool for storing, managing and retrieving large sets of structured data that often serve as a repository to enable easy access. Hence, when working with extensive data volumes, such as financial data, they serve as a reasonable choice. 

The robust security features offered by databases, including access controls, authentication, encryption and auditing, make them an excellent choice for safeguarding financial data against unauthorized access or breaches. Moreover, they facilitate real-time analytics, which is crucial for making transaction analytics relevant. 

Most organizations need to extract their data from industry-specific software, such as core banking systems, ERP systems or policy admin systems. They typically achieve this through a direct connection to databases, rather than accessing the software directly. The movement of that data to and from databases is a critical component for effective and efficient transaction analytics.

Important factors when moving transaction data to and from databases

Before transaction analytics are possible, financial data needs to be consistent, standardized, error-free and up-to-date — which requires consistent cleaning and normalization

Additionally, databases require organized, consistent and structured data through enforced rules on data input and storage by tracking changes to data.

To unlock the true value of transaction data, the data needs to be extracted accurately from various sources and formats to facilitate analytics. To facilitate successful extraction, the pipelines used for extraction need to have certain characteristics: 

  1. Reliability: A reliable pipeline with a high up-time is important. The last thing a data engineering team wants is to get pipeline breakage emergency calls at odd hours. An unreliable integration tool can result in decisions based on incomplete or erroneous data and thus lead to revenue loss. 
  2. Data Transformation: Efficient pipelines aim to deliver analysis ready data by allowing data transformations during the extraction process. This might involve facilitating data cleaning, normalization, filtering, aggregation, joining or even basic calculations. When and how you complete these transformations could severely impact the efficiency of transaction analytics. 
  3. Incremental Extraction: Important from a cost and load perspective, the pipelines should support incremental extraction, where only new or changed data since the last extraction is retrieved. Incremental extracts are instrumental in speeding up the extraction process and highly cost effective. 
  4. Security: It’s important to implement strong security measures to protect sensitive data during extraction, transit and storage. Since financial transactions data is highly regulated, it’s important to ensure that the extraction method supports compliance requirements such as security certifications and includes encryption, access controls and compliance with data privacy regulations. 
  5. User-Friendly Interface: An intuitive and user-friendly interface not only allows for increased adoption, but also allows users to easily configure data extraction settings, define extraction criteria and monitor extraction progress.
  6. Documentation and Support: Comprehensive documentation and support resources, including user guides, FAQs and customer support channels assist users in effectively using and adopting the tool.

How Fivetran enables efficient financial transaction analysis

Maintaining and building pipelines on your own is laborious and time-consuming. It's also challenging and resource-intensive to incorporate data governance and security layers, while ensuring compatibility with modern data tools and requirements

Fivetran offers a fully-managed solution that simplifies data integration and automates data movement to and from databases reliably and securely. Our pre-built data pipelines ensure 99.9% reliability, with built-in idempotence. In case of a failure, we receive an alert, handle the issue and backfill all your data from the point of failure. Our team of 300+ data engineers is dedicated to keeping your data flowing smoothly at all times, allowing you to sleep soundly while your dashboards update and your products operate seamlessly worldwide.

Furthermore, we excel at replicating high volumes of data with Change Data Capture (CDC), enhancing operational excellence. This results in better insights, improved supply chain efficiency and a lot more. As a result, enterprises can reduce the time spent on data troubleshooting or waiting and allocate more time to high-value data analytics and operational optimization. 

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Data insights
Data insights

Financial transaction analytics 101: Efficiently identifying impact

Financial transaction analytics 101: Efficiently identifying impact

October 5, 2023
October 5, 2023
Financial transaction analytics 101: Efficiently identifying impact
The foundations of financial transaction analytics and how database pipelines play a part in trend analysis.

According to Gartner, misuse of financial analytics can cost organizations as much as one percent of revenue per decision. In today’s data-driven world, it’s one of the most essential analyses an organization must complete — but without the right data stack, it’s also one of the most difficult. 

That’s especially apparent when you consider financial transaction analytics. Financial transaction analytics is a subset of general finance analytics. It requires extracting insights and identifying patterns from large volumes of financial transactions data to help predict and plan for the future. By showcasing the impact of a transaction on assets, liabilities and revenue, organizations have the foresight to make informed, agile decisions. 

Critical projects fueled by transaction analytics

Transaction analytics sets the foundation for some crucial finance analytics projects such as:

Financial reconciliation 

Financial reconciliation is the quarterly activity of verifying financial information to ensure its accuracy. When completed manually, this task consumes a substantial amount of time and is highly error-prone due to the involvement of multiple systems. The byproducts of transaction analytics, such as well-documented transactions, data enrichment and pattern identification facilitate the detection of discrepancies and enable the acceleration of financial reconciliation.

Revenue analysis

Revenue analysis provides detailed information on revenue streams of an organization over a period of time. It helps determine the most profitable products or channels, identify areas to reduce costs and perform historical comparisons. Transaction analysis provides a granular view into revenue sources, identifies customer buying patterns and pricing patterns to further enrich revenue analysis projects like automating dashboards. 

Customer Lifetime Value and churn analysis 

Customer Lifetime Value (CLV) is the revenue generated from a customer over their buying lifetime. In simple terms, it measures the total spend of a customer with the company before they churn. Transaction analysis enables the identification of patterns among at-risk customers or the actions a customer may exhibit prior to churning.

How transaction analytics rely on databases 

Transaction analytics is primarily designed to take informed decisions and corrective actions based on insights generated by transactions. Financial transaction data, by its nature, is structured, sensitive and accumulates in substantial volumes. 

Due to their large data volume, databases are a popular tool for storing, managing and retrieving large sets of structured data that often serve as a repository to enable easy access. Hence, when working with extensive data volumes, such as financial data, they serve as a reasonable choice. 

The robust security features offered by databases, including access controls, authentication, encryption and auditing, make them an excellent choice for safeguarding financial data against unauthorized access or breaches. Moreover, they facilitate real-time analytics, which is crucial for making transaction analytics relevant. 

Most organizations need to extract their data from industry-specific software, such as core banking systems, ERP systems or policy admin systems. They typically achieve this through a direct connection to databases, rather than accessing the software directly. The movement of that data to and from databases is a critical component for effective and efficient transaction analytics.

Important factors when moving transaction data to and from databases

Before transaction analytics are possible, financial data needs to be consistent, standardized, error-free and up-to-date — which requires consistent cleaning and normalization

Additionally, databases require organized, consistent and structured data through enforced rules on data input and storage by tracking changes to data.

To unlock the true value of transaction data, the data needs to be extracted accurately from various sources and formats to facilitate analytics. To facilitate successful extraction, the pipelines used for extraction need to have certain characteristics: 

  1. Reliability: A reliable pipeline with a high up-time is important. The last thing a data engineering team wants is to get pipeline breakage emergency calls at odd hours. An unreliable integration tool can result in decisions based on incomplete or erroneous data and thus lead to revenue loss. 
  2. Data Transformation: Efficient pipelines aim to deliver analysis ready data by allowing data transformations during the extraction process. This might involve facilitating data cleaning, normalization, filtering, aggregation, joining or even basic calculations. When and how you complete these transformations could severely impact the efficiency of transaction analytics. 
  3. Incremental Extraction: Important from a cost and load perspective, the pipelines should support incremental extraction, where only new or changed data since the last extraction is retrieved. Incremental extracts are instrumental in speeding up the extraction process and highly cost effective. 
  4. Security: It’s important to implement strong security measures to protect sensitive data during extraction, transit and storage. Since financial transactions data is highly regulated, it’s important to ensure that the extraction method supports compliance requirements such as security certifications and includes encryption, access controls and compliance with data privacy regulations. 
  5. User-Friendly Interface: An intuitive and user-friendly interface not only allows for increased adoption, but also allows users to easily configure data extraction settings, define extraction criteria and monitor extraction progress.
  6. Documentation and Support: Comprehensive documentation and support resources, including user guides, FAQs and customer support channels assist users in effectively using and adopting the tool.

How Fivetran enables efficient financial transaction analysis

Maintaining and building pipelines on your own is laborious and time-consuming. It's also challenging and resource-intensive to incorporate data governance and security layers, while ensuring compatibility with modern data tools and requirements

Fivetran offers a fully-managed solution that simplifies data integration and automates data movement to and from databases reliably and securely. Our pre-built data pipelines ensure 99.9% reliability, with built-in idempotence. In case of a failure, we receive an alert, handle the issue and backfill all your data from the point of failure. Our team of 300+ data engineers is dedicated to keeping your data flowing smoothly at all times, allowing you to sleep soundly while your dashboards update and your products operate seamlessly worldwide.

Furthermore, we excel at replicating high volumes of data with Change Data Capture (CDC), enhancing operational excellence. This results in better insights, improved supply chain efficiency and a lot more. As a result, enterprises can reduce the time spent on data troubleshooting or waiting and allocate more time to high-value data analytics and operational optimization. 

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Kostenlos starten

Schließen auch Sie sich den Tausenden von Unternehmen an, die ihre Daten mithilfe von Fivetran zentralisieren und transformieren.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.