Many companies face significant challenges when trying to leverage AI, become more data-driven, and use both internal and external data sources efficiently. These ambitions often require breaking down data silos and merging fragmented data. As a result, organizations can build a larger, more valuable proprietary data asset, which can be further enriched by adding or replacing flexible sources.
However, many companies still find themselves trapped within a maze of data silos. Valuable insights remain fragmented and underutilized. By consolidating these separated datasets, companies can understand operations, customer behavior, and market dynamics more effectively. This unified data ecosystem fosters synergy across departments, enabling the full utilization of data assets and informed decision-making. Furthermore, a unified data approach drives innovation and helps companies gain a competitive edge.
Data reconciliation is crucial in harmonizing diverse datasets with distinct schemas, fill rates, and meanings. It serves as the foundation for informed decision-making and actionable insights. Without effective data reconciliation, successful data governance is impossible.
Large companies almost always rely on data from external vendors. Occasionally, they notice a decline in data quality or face changes in a vendor’s pricing model. In both cases, they risk being trapped in vendor lock-in if they cannot transition to another vendor. A successful transition necessitates reconciling the current data universe with the incoming data from a new vendor.
Data reconciliation can be compared to assembling the pieces of a complex puzzle. It involves aligning and overlaying disparate datasets to derive coherent insights. These datasets may come from different sources – such as databases, APIs, or files – each presenting its unique structure and semantics.
Reconciliation in Different Industries
As soon as you have more than one overlapping data source, you will need Data Reconciliation. Let’s consider a simple example scenario: We have an e-commerce application and need to map the customer data given to the company’s CRM system. A customer enters their name “John Doe” but the CRM does not know this person but has an entry named “John H. Doe”. A reconciliation process now needs to check whether additional data fields correspond to each other (e.g., the address, the phone number, or the e-mail address) and whether the two entries can be merged.
Imagine some more fields of application in different industries, e.g.,
Retail: In the retail industry, data reconciliation is vital for integrating sales data from various channels such as online stores, physical outlets, marketing campaigns, and third-party platforms. It ensures accurate inventory management, sales forecasting, and customer analytics.
Healthcare: In healthcare, data reconciliation is crucial for integrating patient records from disparate sources such as electronic health records, laboratory systems, imaging systems, and wearable devices. It facilitates comprehensive patient care, clinical decision support, and medical research.
Financial Services: Financial institutions handle essential data packages, e.g., dossiers about wealthy individuals or company databases. More data sources can be added to increase the benefit of this data. These different sources must be harmonized to form one information foundation.
E-commerce: E-commerce platforms aggregate data from multiple sources, including product catalogs, customer interactions, payment gateways, and shipping providers. Data reconciliation ensures seamless order processing, personalized customer experiences, and effective marketing strategies.
Supply Chain Data Management: Supply chain analysis relies on data from diverse sources, including suppliers, manufacturers, distributors, and retailers. Data reconciliation ensures synchronization of data related to inventory levels, logistics, and order fulfillment, enabling efficient supply chain operations. Different data packages may vary in structure, quality, and fill rates.
These were just a few of many examples. Let’s move on and categorize reconciliation into different technological sections.
Technical Examples
Modern data stacks are enormous systems with many different services. Data reconciliation can live in various positions, connecting the dots. We can see some core technologies that we should have a glimpse at.
Entity Graphs: Data reconciliation is essential in entity graphs, such as social networks or customer relationship management systems, ensuring that entities from different datasets are correctly aligned and connected. Without proper reconciliation, duplicate or inconsistent records can lead to fragmented or incorrect relationships within the graph, undermining its reliability. By reconciling data, organizations can create a unified representation of entities, enhancing the graph’s ability to reveal meaningful insights, detect patterns, and support decision-making. Furthermore, consistent and accurate entity graphs are crucial for applications like knowledge management, fraud detection, and recommendation systems, where the quality of relationships directly impacts performance.
Data Mesh: By adopting the data mesh architecture, organizations manage decentralized data domains, each with its data models and sources. Data reconciliation enables consistent integration and data alignment across decentralized domains. In a data mesh, each domain manages its data products, but without reconciliation, discrepancies and inconsistencies between domains can arise, leading to fragmented insights and reduced data quality. Reconciliation ensures that data products across the mesh adhere to common standards, facilitating seamless data exchange, interoperability, and unified insights. This is vital for the success of a data mesh, as it relies on accurate and consistent data to empower domain teams to make informed, data-driven decisions.
Data Lake: Data reconciliation is important for a data lake because it helps ensure that diverse data sources are integrated consistently and organized. A data lake often contains raw, unstructured, or semi-structured data from various origins, which can lead to duplicate or conflicting information. Reconciliation helps align and standardize data, reducing redundancy and ensuring data quality. This process is essential to derive meaningful insights, maintain trust in the data, and support advanced analytics, as the accuracy and coherence of data within the lake directly impact the effectiveness of downstream use cases like machine learning and reporting with tools like Power BI.
Methodologies of Data Reconciliation
Data reconciliation has many challenges. Datasets may exhibit schema inconsistencies, varying fill rates, and semantic disparities. Moreover, the sheer volume and velocity of data influx amplify the complexity of reconciliation efforts. Given these challenges, data accuracy, consistency, and reliability are paramount objectives. The good news is that we can use several tools and methodologies to help us with our task.
Schema Mapping: One of the fundamental steps in data reconciliation involves mapping the schemas of different datasets. This process entails identifying corresponding fields, data types, and relationships across datasets.
Data Cleansing: Before reconciliation, it is imperative to cleanse the data to rectify inconsistencies, eliminate duplicates, and handle missing values. Data cleansing ensures the integrity and accuracy of the reconciled dataset.
Entity Resolution: Entity resolution involves identifying and resolving discrepancies in entity representations across datasets. This entails deduplicating records, aligning entity identifiers, and establishing standardized entity representations with the help of an entity schema. Statistical matching leverages advanced algorithms to align datasets based on statistical similarities. This approach enables reconciliation even without exact matches, thereby enhancing the reconciliation process’s robustness. Probabilistic record linkage techniques determine the likelihood of records referring to the same entity across datasets. These techniques facilitate accurate reconciliation amidst noisy and incomplete data by assigning weights to matching attributes.
Quality requirements
The level of reconciliation accuracy required can vary significantly depending on the domain and the implications of errors in this domain. Some scenarios allow for a margin of error, such as providing a list of best-matching items to speed up user workflows. Precision without room for mistakes is crucial in other disciplines, like financial transactions. Imagine a bank transferring money to the wrong banking account because an IBAN had transposed digits.
Hence, the nature and scope of data reconciliation are intrinsically linked to the specific domain. We see varying requirements for accuracy. Sometimes, a human in the loop can fix errors; sometimes, humans may not be part of the process. These requirements are often linked to compliance with law, guiding principles, and codices.
How to solve the problem?
First, you need to analyze your business problem. Reconciliation is a highly individual task. Here are a few questions that may guide your way:
- How many data sources do you need to align?
- How big are the data sets (storage, number of entries)?
- What data structures are involved?
- How dissimilar are the data schemas?
- What mapping accuracy is needed?
- How often does the mapping need to take place?
- What is the maximal run time for one mapping?
- Is it possible to transform some data fields and compute a deterministic mapping?
- Is reconciliation everything, or do you need an intelligent data merge from multiple sources?
Most reconciliation problems are actual problems, and an easy-to-use API will not solve them. When you move your data to a public cloud, you also move the reconciliation problem, which won’t disappear automatically. As soon as you have individual requirements, you also need an individual solution.
CID has a history of performing this task at scale. We have built a huge entity graph with nearly 100 million nodes from several data sources. We did several reconciliation initiatives for clients, even in a multi-modal scenario where financial price curves needed to be combined with textual descriptions to automatize the mapping of derivative instruments.
Are you interested in reconciliation? Reach out to us. Please also read our second post about the topic, which delves into the technical aspects of these steps. If you are interested in the different steps and the terminology of data reconciliation, please have a look at our article.
Do you have a reconciliation problem, thoughts, or experiences you would like to share?
Please contact us.
Author © 2024: Dr. Jörg Dallmeyer – www.linkedin.com/in/jörg-dallmeyer/
