If juggling multiple marketing channels has you feeling overwhelmed, you're not alone! Digital advertising comes in many shapes and forms, and modern marketers need to jump between different platforms, audiences, and channels.
This can often lead to an overwhelming amount of data from multiple data sources that are difficult to combine and compare. If not reconciled correctly, this can leave your marketing in a tangled mess that is impossible to unweave.
In this article, we’ll explain not only what data reconciliation is, but — more importantly — why it matters and how modern marketers can conduct data reconciliation effectively to provide clean, valuable data.
Simply put, data reconciliation is the activity of comparing two or more datasets together, identifying discrepancies, and, ultimately, resolving any differences.
Data reconciliation is a key building block of effective data governance. It entails the analysis of multiple datasets that have at least one common field or property, for example, a set of comparable metrics such as a cost figure and clicks. Any differences between the data sets are then identified and analyzed to understand the reason behind the discrepancy and how to resolve it.
Modern marketing often involves multiple channels and platforms, from digital advertising to email, social media, and offline campaigns. And, this generates a substantial amount of data.
However, different datasets from different platforms don’t always match one another and can be complex to compare. Modern customers tend to pass through multiple touchpoints on their customer journey as they interact with different advertisements or marketing activities across multiple platforms. And each of these steps comes with its own associated performance metrics, granularity, and costs.
Data reconciliation is crucial to marketers because it helps ensure accurate data, improves consistency between different datasets so they can be compared, and highlights errors or anomalies before they impact the business. Ultimately, data reconciliation is an integral part of building an accurate, holistic view of marketing performance and identifying which activities are generating the highest or lowest return on investment (ROI).
Data reconciliation can happen in multiple areas of a business and can vary. For example, marketing-related activities generate data discrepancies across multiple advertising sources. Marketers have to ensure that the totals of performance metrics (impressions, clicks, conversions, costs, etc.) match across two or more different data sources or at least explain the differences whenever reconciliation is impossible. Similarly, CRM-related activities often include reconciling customer lists sent from a third party with internal lists of customers. In addition, some business operations may involve aligning inventory records with sales orders to ensure that stock levels match customer demand.
Although the nature of data reconciliation can be different depending on the business or goal, it generally follows the same process:
How easy it is to do the above and how often will depend on the nature of the business and the datasets that need reconciling.
Data reconciliation is essential for ensuring data accuracy and consistency, but it comes with its own set of challenges. These challenges can range from data discrepancies and integration issues to handling large volumes of data and maintaining data quality across diverse systems.
Siloed data systems and sources pose a significant challenge for data reconciliation, as they create inconsistencies by limiting access to all necessary data. This issue often arises when data is contained within closed ecosystems or 'walled gardens,' preventing organizations from accessing the complete dataset needed for a comprehensive reconciliation process. Without full access to all relevant data, organizations can't accurately identify or resolve discrepancies, leaving gaps in their data management efforts.
One major challenge in data reconciliation is the absence of a single source of truth. This issue arises when the same dataset exists in multiple systems, each potentially containing different values. Without a clear benchmark or reference point to compare the data against, organizations struggle to determine which dataset is accurate. As a result, even if discrepancies are identified, it's unclear which data should be trusted, making it difficult to correct errors and ensure data integrity.
Even if all data is available and a single source of truth is established, resolving discrepancies can be challenging, especially with large volumes of data. Organizations need to ensure they have the right tools and processes to address these discrepancies effectively. It's crucial that these solutions are efficient and not overly costly in terms of effort, time, and resources, as excessive costs can hinder the reconciliation process.
Data reconciliation faces several challenges related to the data itself. One issue is missing records, where data present in one source is absent in another. Another challenge is mismatched entries, where data values differ across sources. Formatting errors, such as inconsistent date formats, also pose problems, as do duplicate records, where multiple instances of the same data exist, create additional difficulties. Outdated data is particularly problematic in fast-changing datasets, and ensuring that the benchmark data remains timely and valid is crucial in such cases.
Data reconciliation-related challenges stem from various sources and can require a multi-faceted approach to overcome, encompassing the right procedures, best practices, and technology to automate as much of the process as possible. Given these complexities, there are several best practices that organizations should follow to navigate these challenges effectively.
1. Choose your truth
In the absence of a single source of truth, it's crucial to select one primary source to compare against the others, as this will ultimately determine the feasibility of the data reconciliation process.
2. Compare apples for apples
Ensure that you’re comparing similar datasets, using the same time ranges, filters, and profiles. Even simple mistakes, like different time periods or miscommunications between sources, can lead to discrepancies that are difficult to resolve.
3. Involve relevant stakeholders, data sources, and technologies
Ensure that all necessary elements are involved in the data reconciliation process, including stakeholders who grant access, the data sources being compared, and the technologies used to analyze and resolve differences.
4. Sampling
When the data volume is too large to assess with available resources due to time constraints or high costs, it may be necessary to balance the trade-off between the time and resources required versus the value of resolving all discrepancies. If the investment isn’t justified, focusing on a smaller data sample might be a more practical approach than undertaking an inefficient or incomplete data reconciliation.
5. Notifications
Set up alerting mechanisms to notify you whenever data fails to meet required standards. This allows you to detect discrepancies promptly and gives you more time to address them before they escalate into larger issues.
6. Automate where possible
Automation can be a game-changer in this context. Human errors often lead to missed discrepancies and mistakes, so implementing automated checks for incoming data wherever possible can significantly reduce the risk of issues caused by human oversight.
Today’s marketing strategies and many other industries depend on a multitude of data sources and touchpoints, resulting in large volumes of data. To fully leverage this data and maximize advertising ROI, it’s essential to interpret and manage these datasets effectively. Without proper oversight, this intricate data environment can quickly spiral out of control. Consequently, data reconciliation is a critical practice for ensuring marketing teams operate at their best.