IS architecture: the key to structuring, harmonising and developing your information system into a coherent, agile whole. Align IT with corporate strategy!
This content on information system architecture is part of our feature on interoperability and data flows.
Only 9% of French companies claim to use between 71% and 80% of their data assets. Organisations need to be able to store and preserve data so that it is easily accessible, available and protected against the risks of theft, loss or damage.
Data cleansing is a crucial step in this process, as inaccurate or obsolete data can distort analyses and lead to incorrect strategic decisions. By eliminating duplicates, correcting errors and standardising formats, companies can ensure that their data is not only reliable, but also usable. This enables better decision-making, more precise targeting of marketing campaigns and optimisation of resources. By investing in data cleansing tools, companies not only improve the quality of their information, but also their ability to innovate and adapt to market demands.
At the same time, implementing good data management practices is essential to guarantee the long-term integrity of the data. This includes data governance policies, team training and the use of appropriate technologies. A corporate culture focused on data quality can transform an organisation’s information potential, enabling it to stand out in an increasingly competitive environment. Ultimately, data cleansing is not just a technical operation, but a strategic lever for business success.
Data cleansing is an essential step for any organisation wishing to optimise its information assets. It ensures that data is ready to be used for accurate analysis and efficient operations.
Investing in data cleansing is a strategic choice that offers significant benefits, both operationally and economically. It’s an investment that not only supports growth and innovation, but also establishes a solid foundation for responsible and effective data exploitation.
Reduced risk of data leakage
A higher level of compliance
Implementing a responsible digital policy
Reduce costs by saving time and optimising resources
More informed decision-making
Data cleaning is the process of identifying, correcting and eliminating errors, inconsistencies and duplications within a data set. This process ensures the quality, accuracy and reliability of the information, making it easier to use for informed analysis and decision-making. Data cleansing includes various techniques, such as data normalisation, enrichment and validation, and is essential for any organisation seeking to maximise the value of its information assets. By ensuring that data is clean and reliable, data cleansing helps to improve operational efficiency and increase confidence in the results of analysis.
Effective cleaning is not just a random process; it requires a structured and methodical approach:
When it comes to data cleaning tools, there are a number of points to bear in mind if the process is to be effective. First of all, it is crucial to choose tools that are adapted to the specific needs of the organisation, whether they be dedicated software or solutions integrated within other systems.
Secondly, it is important to consider how easy these tools are to use, as a user-friendly interface makes it easier for teams to adopt them. In addition, it is important to ensure that the tools offer the full functional scope required for quality assurance, such as duplicate detection, data validation and standardisation, to ensure complete coverage of cleansing needs.
Finally, the ability to integrate with other data management systems – such as MDM – is essential to ensure a smooth workflow. By taking these elements into account, organisations can put in place an effective and sustainable data cleaning process.
MyDataCatalogue is the Phoenix platform module dedicated to cleansing and mapping your data assets.
The MyDataCatalogue module natively integrates data cleansing functionalities to optimise data storage and retention, improve operational efficiency and reduce the risks associated with data management. MyDataCatalogue also facilitates data discovery, understanding and use, while guaranteeing data compliance and security. With MyDataCatalogue, you can identify, understand and visualise your data within a data catalogue, efficiently and collaboratively!
With its Data Catalog and Data Cleaning functions, MyDataCatalogue lets you define data access policies to ensure that only authorised people can view or modify sensitive information.
With regular, automated audits, ensure your compliance with data protection regulations, such as the RGPD, by easily identifying and documenting data sources.
Modifications and accesses to data are traced, facilitating internal and external audits and ensuring complete transparency of data operations.
Data Discovery features automate the extraction and analysis of metadata, enrich data using AI, and offer an intuitive search interface for a 360° view of information assets.
You create a common knowledge base, enriched and accessible to all, to ensure uniformity of the data used throughout the organisation. You base your strategic decisions on controlled information, and reduce the risk of misinterpretation.
At Blueway, we are convinced that freeing yourself from technical constraints is a prerequisite for putting your Information System at the service of business processes and corporate strategy, now and in the future.
That’s why our Phoenix Data Platform unifies BPM, MDM, ESB, API Management and data mapping practices. This approach, focused on your business and human challenges, contributes to the flexibility and scalability of your IT architecture and infrastructure.
The functions of MyDataCatalogue can be combined with the other modules of the Phoenix platform to provide a solution for the entire data cycle, from identification to urbanisation, governance and movement through processes.
Data cleansing is the process of detecting and correcting (or removing) errors and inconsistencies in data to improve its quality. This includes removing duplicates, correcting typographical errors, managing missing values and standardising data formats.
Data cleansing focuses on correcting errors and inconsistencies to improve data quality.
Data preparation includes cleansing, but also other steps such as transforming, integrating and enriching data to make it ready for analysis.
