Unlocking the Power of Data Integration: A Game-Changer for Business Success

Introduction - What is Data Integration

Data integration is essentially combining data from multiple sources into a single dataset. This allows for a more comprehensive analysis by bringing together different pieces of information. Here's a breakdown of what data integration entails:

Merging data sources: Data integration allows you to take data from various sources, like spreadsheets, databases, and web analytics, and combine them into a unified view.

Creating a single dataset: The integrated data can be stored permanently or can be a temporary view that facilitates analysis for a specific purpose, depending on the how the solution is formulated.

Analysis and insights: The key benefit of data integration is that it allows you to see how data points from different sources relate to each other. This can reveal patterns and trends that might not be evident if you were analysing each source in isolation.

While the concept is relatively simple to understand, its implementation can be very difficult to achieve in practice. The data sources above are independent of each other with no direct relationships between them.

The integration process has to establish associations between the data sources in the unified view to cross reference data between the sources. For integrity and effective planning and analysis, the data sources should be reconciled to give a consistent view of data across the various sources which are likely to be owned by different departments and functional areas.

The integration process has to be efficient and deliver results in a very short time interval. Once the integrity steps are completed, the data will be utilised for planning, analysis, charts, graphs and other visual output.

We will demonstrate an example of the problems that arise when key systems are not in sync with each other. This scenario is common in many organisations as are the challenges.

Case Study on Data Integration (Data Integration)

This case study considers a project which is billable to a client as the job progresses. At the end of it, all the costs are finalised and final bill sent to the client, after which no further costs can be billed. Any further costs will result in a loss to the organisation.

Reconciling Data Held in Unlinked Systems

In the example above, the commercial management of the project is based on the Cost Control Application which is used to monitor costs billable to the client. The actual payments to suppliers and contractors are handled and recorded in the financial system. However, significant costs are entered independently in each system. The Financial system is owned by finance team and is used for financial and regulatory reporting.

The financial system will be more reliable as unpaid costs will be claimed by suppliers and contractors. Here is the issue. The Cost Control Application is the basis on which revenue is earned. The critical time is at the end of the project when the final bill has to be submitted. After this point, if any costs are in the financial system and not in Cost Control, a company has to bear the loss as the costs are not billable.

If the two systems are not reconciled, the commercial and financial teams will be reporting inconsistent data and performance results.

For example, if the costs amount to £10.5m, the company will have to bear a loss of £500,000. Such a system mismatch can result in significant losses causing financial difficulty and hardship.

Investigation: Laying Down Principles

An investigation was undertaken to find a solution to this problem on a very large project which would have a significant effect on a company's earnings and financial position. After an extensive investigation, the solution was to compare all the supplier and contractor costs recorded in each system. No cash payments were made and all payments were recorded through the invoicing system.

The two data sources were integrated into one source and a comparison made of costs recorded against each supplier and contractor at a summary level. If there were any discrepancies, detailed transactions were available to reconcile and correct any differences.

This process was rolled in with the financial planning, analysis and reporting system to provide one unified view of data in each system.

Conclusion

Increased Data Integrity Minimising Financial Losses

This process significantly reduced the risk of costs not included in the final settlement thereby enhancing financial performance and strengthening the company's financial position.

This solution was optimal in this particular case. During a peer review, there was confirmation that this was the first time this reconciliation had been achieved as part of the regular monthly reporting.

We Can Help!

Although this solution appears to be simple, behind the scenes there is significant data processing and transformation going on to prepare the data for financial planning, analysis and reporting. This process was automated as there was a short time interval from when data is available and the results reported to key stakeholders.

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