10 Data Sourcing Tips for Reporting

June 18, 2024
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Data sourcing, which involves identifying, collecting, and integrating data from various sources, is crucial in ensuring the right information is available at the right time. Data is the lifeblood of business reporting, enabling informed decision-making and strategic planning. However, the quality and reliability of reports heavily depend on the effectiveness of the data sourcing process.

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So, what do we mean by a data source? A data source refers to the origin of the data being used for reporting or analysis. It can be a database, spreadsheet, XML file, or even hard-coded data within a program. The choice of data source varies based on the specific requirements of the computer system or application.

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Importance of Data Quality in Sourcing

Data quality is of the utmost importance regarding data sourcing. Poor data quality can lead to inaccurate reporting, flawed insights, and, ultimately, wrong business decisions. In fact, according to a study by Gartner, poor data quality costs organizations an average of $million per year.

Establishing robust data management practices is essential to ensuring high-quality data. This includes implementing data validation checks, maintaining data consistency, and regularly auditing the data for accuracy. By prioritizing data quality, organizations can build trust in their reporting and make confident data-driven decisions.

10 Tips for Data Sourcing

Let the Desired Business Outcome Dictate the Data Requirements

Start by defining the desired business outcomes and let them guide the data-sourcing process. Determine the data needed to support those outcomes and prioritize the sourcing efforts accordingly.

Profile Your Data

Take the time to understand the structure, granularity, frequency, and format of the data you are sourcing. Engage with data providers to ensure they can meet your specific data requirements.

Get as Close to the Original Source as Possible

Data should be sourced directly from the original source whenever feasible to minimize manual intervention and reduce the risk of errors and inconsistencies introduced by intermediate processing.

Keep It Simple and Consolidate Sources

Avoid the temptation to hoard data unnecessarily. Focus on collecting only the data essential for your reporting needs and consolidate sources to simplify the data landscape.

Set and Manage Expectations Around Data Quality

Understand the business impact of data quality issues and prioritize efforts based on the criticality of each data source. Establish clear expectations and tolerances for data quality with stakeholders.

Catch Data Quality Issues Early

Implement quality controls at the earliest stages of the data supply chain. Conduct basic checks for format, structure, and consistency to identify and address issues promptly.

Measure and Act on Data Quality Issues

Define metrics to measure data quality and establish processes to check the source data against those metrics regularly. Set up alerts for data anomalies and address recurring issues through automated data cleansing.

Expect and Embrace Change

Anticipate changes in the business environment, such as mergers, acquisitions, or new system implementations. Plan for the impact of these changes on data sourcing and maintain flexibility in your reporting processes.

Establish Change Management Controls

Implement controls and procedures for managing changes to the data supply chain. Ensure proper documentation, version control, and approval workflows to maintain data integrity during change.

Create a Controlled Environment for Data Manipulation

Allow for collaborative data analysis while maintaining control and auditability. Define specific points where adjustments are permitted in the data supply chain and establish logging and version control mechanisms.

Tools and Techniques for Efficient Data Sourcing

Leveraging the right tools and techniques is crucial to streamlining the data sourcing process and improving efficiency. Here are some key considerations:

  • Data Integration Platforms: Utilize data integration platforms like SolveXia, which enable seamless connectivity between disparate data sources. These platforms automate data extraction, transformation, and loading (ETL) processes, reducing manual effort and ensuring data consistency.
  • Data Quality Tools: Implement data quality tools that perform data profiling, validation, and cleansing. These tools help identify data anomalies, enforce data standards, and maintain the integrity of the sourced data.
  • Data Cataloging and Metadata Management: Employ data cataloging solutions to create a centralized repository of data assets and their associated metadata. This enables easier discovery, understanding, and governance of data sources.
  • Automated Data Pipelines: Build automated data pipelines that extract data from various sources, transform it into a standardized format, and load it into the target systems. Automation minimizes manual intervention and ensures timely data availability.

Overcoming Challenges in Data Sourcing

While implementing effective data-sourcing practices, organizations may face several challenges. Here are some common issues and strategies to overcome them:

  • Data Decay: Data can become outdated or irrelevant over time. Regularly review and update data sources to ensure the currency and relevance of the data being used for reporting.
  • Vendor Reliability: When sourcing data from external vendors, assess their reliability and data quality standards. Establish service level agreements (SLAs) and monitor vendor performance to ensure consistent data delivery.
  • Data Silos: Disparate data sources can lead to data silos, making it difficult to obtain a holistic view. Break down silos by integrating data sources and establishing a unified data architecture.
  • Data Security and Privacy: Ensure data sourcing practices comply with data security and privacy regulations. Implement appropriate access controls, data encryption, and anonymization techniques to protect sensitive information.

Case Studies and Real-world Applications

To illustrate the effectiveness of data sourcing best practices, let's explore a few real-world case studies:

7-Eleven Streamlines Financial Operations

7-Eleven, a leading retail company, faced challenges managing its extensive financial operations across numerous stores. By implementing SolveXia's automated reconciliation solution, they achieved significant improvements. Integrating data from multiple sources, including point-of-sale systems and financial databases, allowed for real-time visibility into financial transactions. This streamlined process reduced manual intervention minimized errors, and enhanced financial accuracy, ultimately leading to better decision-making and operational efficiency.

IOOF Enhances APRA Reporting

IOOF, a prominent financial services provider, must improve its APRA reporting to ensure compliance and accuracy. SolveXia's automation platform provided a robust solution by integrating data from various internal systems and external sources. This automated data processing and validation improved the accuracy of APRA reports and significantly reduced the time and effort required for reporting. As a result, IOOF could maintain compliance with regulatory requirements while focusing on strategic initiatives.

Harold Sharp Simplifies Tax Digitalisation

Harold Sharp, an accounting firm, faced the challenge of adapting to the Making Tax Digital (MTD) initiative. By leveraging SolveXia's automation capabilities, they transformed their tax reporting process. The solution enabled integrating client data from multiple sources, automated data validation, and streamlined tax report generation. This allowed Harold Sharp to provide timely and accurate tax submissions, improving client satisfaction and operational efficiency. The firm could focus more on advisory services, adding greater value to their clients.

Conclusion and Future Trends

In today's data-driven landscape, effective data sourcing is critical to business success. By adopting best practices, leveraging the right tools, and overcoming challenges, organizations can enhance their reporting processes' quality, reliability, and efficiency.

Looking ahead, the future of data sourcing is poised for exciting advancements. The rise of big data, artificial intelligence, and machine learning will enable more sophisticated data sourcing and analysis capabilities. Organizations that embrace these technologies and continuously refine their data sourcing strategies will be well-positioned to gain a competitive edge.

As the volume and variety of data continue to grow, the importance of data sourcing will only intensify. By staying attuned to emerging trends and best practices, organizations can unlock the full potential of their data assets and drive better business outcomes.

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You can watch a recording of a webinar that we presented on data sourcing best practices below:

(How to source good data from SolveXia)

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