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Align Data Remediation Tool

Bring data sets back into focus. Keep master data records free of errors or missing data using a fully configurable rule-based engine. Whether preparing for migration or just wanting to maintain data quality, Align makes data remediation easy.

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Master Data Mistake Mass Corrections

In the digital era, data is the lifeblood of modern businesses, making data remediation essential. Accurate data ensures informed decision-making, enabling organisations to stay competitive and agile. Keeping data aligned empowers businesses to unlock new levels of efficiency, enabling growth, innovation, and sustainable success in today’s data-driven landscape.

Align automates mass data correction in your SAP master data. Using its powerful, configurable rule-based engine, it does what would take employees hours or days in just minutes. This frees employees of monotonous tasks to focus on higher level activities to drive your organisation forward.

Why Align?

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Easily improve the user of experience of your employees and make data processing an efficient exercise with Align automation. Align reduces manual data checks completely, with the only manual activities being removals, edits or approvals of changes.

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Many large organisations are waking up to the reality of migration to S/4HANA. Align has been designed to play a key role in an SAP data migration strategy. Used as an SAP data migration tool, Align can prevent expensive data errors from causing disruptions during migration. Migration is an ideal time to cleanse data with Align.

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Align’s powerful rule-based engine reduces the risk of human error in data governance. Being able to check all data records in one go also allows for better checks which makes government compliance easy.

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Align is a one-of-a-kind solution on the market. Align is the ultimate cleansing tool and exists to identify all misaligned data records in one go. It does this by possessing a powerful rule-based engine. Rules come pre-delivered but can also be configured to the needs of the organisation. It’s simply the best way to remediate SAP data.


Align is an SAP data correction tool designed to identify and correct all erroneous or missing data values in bulk. If your current processes are slowed with manual, tedious Master data checks, Align provides a simple UX that gets the job done in minutes.

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Align boasts thorough selection criteria which come pre-delivered and with the flexibility to be configured to suit an organisation as it needs. Set up the rules and Align quickly draws up all missing or erroneous data sets for approval, removal or editing.


Focus on other strategic tasks and let Align work in the background. Just set the selection criteria and run. Free up employee time to focus on the bigger picture and instantly improve their user experience by removing the need for monotonous manual checking.

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Align makes data correction as efficient as it can be. Once highlighted, data can be corrected, removed, and approved in one go. This removes complexities for data governance staff and allows them to spend more time on tasks of greater importance.

Demo Align Today

Get in touch today to arrange a live session of Align in action. Remediate data in real time and discover just how fast your master data can be realigned. Improve your data-driven decision-making with Align!

Data remediations FAQs

Data remediation is the process of identifying, correcting, and enhancing inaccurate, incomplete, or outdated data within a dataset or database. It aims to ensure data accuracy, consistency, and quality, minimising errors and improving data integrity. Remediation involves various techniques, such as data validation, normalisation, deduplication, and data enrichment. This crucial procedure enables organisations to make informed decisions, comply with regulations, and optimise business processes. By maintaining reliable and up-to-date data, companies can enhance operational efficiency, customer satisfaction, and overall data-driven strategies.

Data remediation is performed through a systematic process of data assessment, identifying anomalies, and applying corrective actions. Initially, data is analysed to pinpoint inconsistencies, inaccuracies, and incompleteness. Defined data standards and guidelines help maintain data uniformity. Subsequently, data cleansing methods, such as data deduplication, standardization, and validation, are applied to rectify errors. Manual review and automated tools aid in the process. Once the data is cleaned, enrichment techniques are employed to enhance its quality. Regular monitoring and maintenance are essential to sustain data integrity, ensuring that organizations can rely on accurate information for decision-making and operational efficiency.

SAP S/4HANA migration poses specific challenges for data remediation. The transition may expose data quality issues like duplicates, inconsistencies, and incompleteness. Data models and structures in S/4HANA might differ from the legacy system, necessitating data mapping and transformation. Data cleansing and validation become critical to ensure accurate data migration. The migration timeline may be tight, increasing the pressure on data remediation efforts. Adequate expertise and tools for data profiling, cleaning, and enrichment are essential. Balancing the urgency of migration with thorough data remediation can be a delicate task, requiring meticulous planning and execution to achieve a successful migration while maintaining data integrity.

The cost of migrating to SAP S/4HANA can vary significantly depending on several factors, such as the size and complexity of the organization, the amount of data to be migrated, the extent of customizations required, the number of users, and the chosen migration approach. Generally, it is a substantial investment that includes software licenses, hardware upgrades, data migration tools, consulting services, and employee training. Estimates suggest that small and medium-sized enterprises could spend hundreds of thousands of dollars, while larger enterprises may incur costs in the millions, making careful planning and budgeting crucial for a successful migration. Lack of data remediation can vastly increase the long-term cost of migration via unnecessary storage costs, data failures or potentially poor data-driven decisions leading to missed opportunities.