Data Quality & Health
Our Health suite contains applications to help clean and maintain a healthy state of data. Clean up your legacy data ready for an S/4 migration, or simply clean up missing or duplicate data to streamline your processing using our ‘Align’ engine
Why Clean Your Data?
Your data is the lifeblood of your business. It enables transactions, operations, and decisions to be carried out. It runs so your company can sprint to the next big opportunity. Clean master data saves costs associated with errors, inefficient processes, and poor decision-making. It ultimately streamlines operations, improves data-driven insights, and ensures a reliable and trusted source of information for your organisation's success. It may seem like a tedious, back-end topic but poor data in a modern business is always the first domino.
The Benefits of Data Quality Tools
Data quality tools help identify and correct errors, inconsistencies, and inaccuracies, ensuring accurate and reliable data for decision-making and analysis.
By providing high-quality data, these tools enable informed decision-making, leading to better strategic choices and improved business outcomes.
With clean and accurate data, organizations can provide personalized and targeted customer experiences, improving customer satisfaction and loyalty.
Data quality tools streamline data validation, cleansing, and integration processes, saving time and resources while improving overall operational efficiency.
Our Data Health Solutions
Maextro's data health solutions have been designed to assist businesses in improving the quality and integrity of their data. Our apps offer functionalities for data profiling, validation, cleansing, and monitoring. They help identify and rectify data errors, inconsistencies, and duplicates, ensuring data accuracy and completeness. With features for data integration, alignment and governance, Maextro's apps enable businesses to achieve seamless data operations and make informed decisions based on reliable insights. The apps also support compliance efforts, enhance customer experiences, and optimise business processes by providing a comprehensive solution for data health management.
DITTO - Duplicate Check
A member of the Maextro family, but delivered as a standalone solution. Ditto is your all-in-one data duplicate and consistency check tool. Powerful algorithms power this simple application that gives you complete control over your search criteria. Search by multiple values and also define a match weighting, allowing searches for exact matches or similar records.
Currently available for customer and vendor records, allowing you to search by name, address, VAT and IBAN codes, Account group and tax code. Find business partners that are unique, duplicates, shared across systems, contain invalid or incorrect data and or Invalid BPs to help you streamline the archiving of data.
Align
Maextro's new Align functionality allows you to quickly and efficiently correct any missing or incorrect data in your SAP system. Identify misaligned data records en masse, either manually, or as a background process using a thorough yet simple selection criteria. This allows corrections to be made to user specified ranges or groups of records.
Align boasts a powerful rules based engine with pre-delivered rules, plus functionality to create your own custom rules. A collaborative workflow enables security and validation on the requested changes with a final approval triggering the update to SAP.
Deployment on-premise or in the cloud, with UI options including SAP GUI, SAP Screen Personas and SAP UI5.
Data Health X
Data Health X by Bluestonex is a reporting tool for your data’s health. With the option of setting up a secure connection to your ECC or S/4 system to obtain the data, or utilising an export program we deploy to your ECC system and importing to a Cloud based HANA database. Currently available for Material Master and Business Partner Records.
Data Health X will give you a high level breakdown of the state of your data. Providing an insight into information such as the number of records created / changed over periods of time, broken down into categories such as material type or group for material, or Account group or BP role for Business Partner; as well as used vs not used records which give you the ability to identify records relevant for archiving.
Data Survey
We send out a Data Maturity survey to the users who primarily deal with data. In this survey, we wish to gain an insight into how the organisation uses data. We are interested in data strategy, data governance, data quality, data velocity, the processes affected by data, and finally the user experience for those who maintain data in the organisation. It takes about 10 mins to complete the survey and all the responses are confidential.
The Bluestonex Data Maturity Index can be analysed with the help of a dashboard where an overall score is given based on the data collected by the survey. In addition to this, we analyse each area and provide a score for them (data strategy, data governance, data quality, data velocity, the processes, and user experience). All these scores are out of 5 and give a better understanding of the current state of data in the organisation. The dashboard also gives an insight into the survey audience and analyses a few of the responses under each category.
Data Survey is available as both a product or a service.
Book a Demo of our Data Quality Apps
To experience the power of our Data quality apps first-hand, book a demo today. Our team will showcase the features and benefits of our tools, applied to your business and processes.
Gain an understanding of how they can improve data accuracy, enhance decision-making, and optimise your data management processes. Revolutionise your data quality practices today!
Data quality FAQs
We get asked a lot of questions about data quality every day. We've decided to compile a few of the most common for you to look through.
Data quality refers to the reliability, accuracy, completeness, consistency, and relevance of data. It is a measure of the overall fitness of data to serve its intended purpose. High-quality data is crucial for making informed decisions, conducting accurate analysis, and achieving desired outcomes. It means that data is free from errors, comprehensive, internally and externally consistent, applicable to the specific needs, up to date, valid, precise, and protected from unauthorised access or modification. Ensuring data quality involves practices like data validation, cleansing, profiling, and ongoing monitoring. By maintaining data quality, organisations can enhance decision-making, improve operational efficiency, and derive meaningful insights from their data assets
Identify and rectify data errors
Data quality tools help in identifying and fixing errors, inconsistencies, and inaccuracies in datasets. These tools can perform data profiling and analysis to identify anomalies, missing values, and inconsistencies, allowing organisations to rectify issues and improve data accuracy.
Enhance data completeness and consistency
Data quality tools can ensure that datasets are complete and consistent. They can validate data against predefined rules, standards, and constraints, highlighting missing values or data that does not conform to defined criteria. By enforcing data completeness and consistency, organisations can rely on accurate and reliable information for decision-making.
Improve data integrity
Data quality tools can detect and prevent data integrity issues by monitoring data changes, identifying unauthorised modifications or deletions, and enforcing data protection measures. They help ensure the security and reliability of data, maintaining its trustworthiness throughout its lifecycle.
Streamline data cleansing and transformation
Data quality tools provide functionalities to cleanse, transform, and standardise data. They can automate processes like deduplication, normalisation, and data enrichment, reducing manual effort and ensuring consistent data formats and structures.
Facilitate data governance and compliance
Data quality tools support data governance practices by providing visibility into data quality metrics, tracking data lineage, and facilitating data stewardship. They help organisations comply with regulations and industry standards by ensuring data accuracy, completeness, and security.
Increase efficiency and productivity
By automating data quality processes and providing a centralised platform for managing data quality, these tools save time, effort, and resources. They enable organisations to focus on data analysis and decision-making instead of spending excessive time on data cleaning and validation tasks.
Overall, data quality tools play a vital role in maintaining the integrity, accuracy, and reliability of data. They enable organisations to derive meaningful insights, make informed decisions, and achieve their business objectives based on high-quality data.
Define data quality requirements: Start by defining clear data quality requirements and objectives based on the specific needs of your organisation. Identify the critical data elements and attributes that are essential for decision-making and operational processes.
Establish data governance practices: Implement robust data governance practices to ensure accountability, ownership, and oversight of data quality. Define roles and responsibilities for data stewardship and establish data quality policies, standards, and procedures.
Implement data validation processes: Develop data validation processes to enforce data quality rules and constraints. Validate data against predefined criteria to identify and correct errors, inconsistencies, and inaccuracies. Implement automated validation checks and data quality controls wherever possible.
Cleanse and standardise data: Use data cleansing tools to remove duplicate records, resolve inconsistencies, and correct errors. Normalise and standardise data formats, units, and terminology to ensure consistency and accuracy across datasets.
Monitor and measure data quality: Establish ongoing monitoring and measurement processes to track data quality over time. Define data quality metrics and key performance indicators (KPIs) to assess the effectiveness of data quality improvement efforts. Continuously monitor data quality and address any emerging issues promptly.
Provide training and education: Train and educate data users and stakeholders about the importance of data quality and best practices for data management. Foster a culture of data quality awareness and continuous improvement.
Implement data quality tools: Utilise data quality tools and technologies to automate and streamline data quality processes. These tools can help with data profiling, validation, cleansing, and monitoring, making the data quality improvement process more efficient and effective.
Data Profiling: The tool should provide data profiling capabilities to analyse the quality, structure, and content of datasets.
Data Cleansing: Look for tools that offer data cleansing features to identify and correct errors, inconsistencies, and inaccuracies in the data.
Data Validation: The tool should support data validation by allowing you to define and enforce quality rules and constraints.
Data Integration: Consider tools that facilitate data integration by enabling the consolidation and merging of data from multiple sources.
Data Monitoring and Alerts: The tool should provide monitoring capabilities to track data quality over time and notify users of quality issues.
Data Governance: Look for tools that support data governance practices, allowing you to define and enforce data quality policies and procedures.
Data Quality Metrics and Reporting: The tool should enable you to measure and report on data quality metrics and key performance indicators (KPIs).
Data Security and Privacy: Consider tools that incorporate data security and privacy features to protect sensitive data.
Scalability and Performance: Assess the tool's scalability and performance capabilities, ensuring it can handle large volumes of data efficiently.
Integration and Compatibility: Evaluate the tool's integration capabilities with your existing data infrastructure and compatibility with data formats and technologies used in your organisation.
Accuracy: Accuracy refers to the degree to which data represents the true or correct values or characteristics it is intended to represent. Accurate data is free from errors, omissions, or discrepancies.
Completeness: Completeness measures the extent to which data is comprehensive and includes all the necessary information for its intended purpose. Complete data contains all the required fields, attributes, and values without any missing or null values.
Consistency: Consistency refers to the degree of agreement and coherence among data values within a dataset or across multiple datasets. Consistent data ensures that there are no contradictions, conflicts, or discrepancies in the data.
Timeliness: Timeliness assesses how up-to-date the data is and measures its relevance within a specific timeframe. Timely data is available and reflects the most recent and relevant information for its intended use.
Relevance: Relevance evaluates the alignment of data with the specific needs and objectives of the intended use. Relevant data is applicable, meaningful, and aligned with the purpose it serves.
Validity: Validity measures the degree to which data adheres to predefined rules, standards, or constraints. Valid data conforms to the specified data model, format, and criteria for acceptance.