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How do you handle complex data sources in financial industry automation?

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Process automation is a powerful way to streamline and optimize financial tasks, such as data entry, reconciliation, reporting, and analysis. However, one of the biggest challenges in financial industry automation is handling complex data sources that may come from different systems, formats, and quality levels. How do you ensure that your automation tools can access, integrate, validate, and transform the data you need for your workflows? Here are some tips and best practices to help you handle complex data sources in financial industry automation.

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Gajanan Patil

Automation Technical Lead @Accelirate | Official UiPath Chapter Lead | Community and Corporate Speaker | Devops |…
Sepideh Samavatian

Instrumentation & Controls Specialist
Swaroop Raj Gunisity

Intelligent Automation Lead | AI Enthusiast | EMBA

1 Identify your data sources and requirements

The first step in handling complex data sources is to identify where your data comes from, what types of data you need, and how you want to use them in your automation processes. You may have data sources such as ERP systems, CRM systems, spreadsheets, databases, web services, APIs, or files. You also need to consider the data formats, such as CSV, XML, JSON, or PDF, and the data quality, such as completeness, accuracy, consistency, and timeliness. By mapping out your data sources and requirements, you can plan your automation strategy and choose the right tools for your needs.

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Gajanan Patil

Automation Technical Lead @Accelirate | Official UiPath Chapter Lead | Community and Corporate Speaker | Devops | Corporate Trainer | Workato Pro
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For Financial institutions data and governance is one of important aspect and achieved through below ? No logging of data should be permitted ? If on-prem orchestrator is configure you can use some level of data in queues apart from account number and employee details ? Do not send execution report add that into dashboard using tools such as power Bi ? For input data use SQL with only read writes configured

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Sepideh Samavatian

Instrumentation & Controls Specialist
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ERP program , are core software companies use to coordinate information in every business area. CRM software, which stands for Custom Resource Management, is a specialized type of ERP.

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Mozammil Rizwan

Hyper Automation Solution Consultant | Azure AI Services | OCR | Document Processing | GenAI | ERP, CRM, HCM, EDI, SCM, E-Commerce, Healthcare Automation Specialist
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In financial industry automation, managing complex data sources involves employing robust data integration tools, implementing efficient data pipelines, and leveraging advanced analytics. Utilizing technologies such as ETL (Extract, Transform, Load) processes, API integration, and machine learning algorithms enhances data accuracy and facilitates timely decision-making. Additionally, employing data validation and reconciliation mechanisms ensures data integrity, supporting the automation of financial workflows with precision and reliability.

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2 Choose the right automation tools for your data sources

When selecting automation tools, it is important to consider their compatibility and connectivity with your data sources, scalability and performance for large volumes of complex data, flexibility and functionality for data manipulation and transformation, security and compliance for sensitive and regulated data, and ease of use and maintenance for your team. Different types of automation tools exist for different purposes, such as robotic process automation (RPA), business process automation (BPA), data integration tools, or data transformation tools. Choosing the right automation tools can help you handle your data sources effectively and efficiently.

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Swaroop Raj Gunisity

Intelligent Automation Lead | AI Enthusiast | EMBA
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Especially working in the financial sector, the focus on scalability, security, and compatibility is crucial while handling complex data. Analyzing how the various automation tools integrate or are compatible with your existing enterprise. If there is a need to automate certain routine tasks RPA could be considered, and entire process automation BPA tools can be selected.

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3 Use data integration techniques to connect and consolidate your data sources

To handle complex data sources, data integration techniques are an essential aspect. These techniques can extract data from various sources and formats, transform it according to business rules and logic, and load it into a destination system or database. Furthermore, they can validate and cleanse data to maintain quality and accuracy, as well as enrich and augment data with additional information or attributes. Data integration techniques can be automated with tools such as RPA, BPA, or data integration tools, or performed manually through code like SQL, Python, or ETL frameworks.

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Karan Somnathe

Microsoft Power Platform Expert with 6+ Years' Experience | Driving Business Transformation through Advanced Power Apps, Power Automate, and Power BI Solutions
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Utilize robust data integration platforms or ETL (Extract, Transform, Load) tools to harmonize data from various sources. These platforms streamline data extraction, transformation, and loading processes, ensuring consistency.

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4 Use data transformation techniques to prepare and analyze your data

When handling complex data sources, it's important to use data transformation techniques to prepare and analyze the data for automation processes. These techniques can help you to filter and sort data based on your criteria, join and merge data from different sources, aggregate and summarize data to create metrics, pivot and reshape data to create different views, and apply calculations and formulas to create new values. Automation tools such as BPA, data transformation tools, or reporting tools can be used to perform these techniques, or alternatively code such as SQL, Python, or the pandas library can be used.

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5 Monitor and optimize your data sources and automation processes

The final step in handling complex data sources is to monitor and optimize your data sources and automation processes. You need to ensure that your data sources are reliable, available, and updated, and that your automation processes are running smoothly, accurately, and efficiently. You can use tools, such as dashboards, alerts, logs, or audits, to track and measure the performance and results of your data sources and automation processes. You can also use feedback, testing, or experimentation to identify and resolve any issues, errors, or bottlenecks that may arise. By monitoring and optimizing your data sources and automation processes, you can improve the quality and value of your automation outcomes.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Karan Somnathe

Microsoft Power Platform Expert with 6+ Years' Experience | Driving Business Transformation through Advanced Power Apps, Power Automate, and Power BI Solutions
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Implement stringent data security measures, especially when dealing with sensitive financial information. Employ encryption, access controls, and other security protocols to safeguard data integrity and confidentiality.

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How do you handle complex data sources in financial industry automation?

1

2

3

4

5

6

1 Identify your data sources and requirements

2 Choose the right automation tools for your data sources

3 Use data integration techniques to connect and consolidate your data sources

4 Use data transformation techniques to prepare and analyze your data

5 Monitor and optimize your data sources and automation processes

6 Here’s what else to consider

Process Automation

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How do you handle complex data sources in financial industry automation?

1

2

3

4

5

6

1 Identify your data sources and requirements

2 Choose the right automation tools for your data sources

3 Use data integration techniques to connect and consolidate your data sources

4 Use data transformation techniques to prepare and analyze your data

5 Monitor and optimize your data sources and automation processes

6 Here’s what else to consider

Process Automation

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