Data governance, the key to the quality of your data assets.
This content on Data Governance is part of our dossier on Master Data Management.
Controlling the quality of the data in your data repository is a crucial issue. To make informed decisions, you need data that is accurate, complete, up-to-date, consistent and understandable to everyone. But how do you manage large quantities of data? Which tools are best suited to your needs? What approach should you adopt?
The growth in the volume of data has become a phenomenon that is difficult to (re)curb. Companies and public bodies now need to introduce genuine data governance. The objective to keep in mind is ‘better control of my data allows me to optimise resources and improve IT security’.
Given the extent of the phenomenon, it’s sometimes difficult to know where to start. Let’s take a step-by-step look at the issue:
Data governance aims to identify, understand and control data so that it can be used more effectively. By identifying the roles and methods of data collection, I can considerably improve its reliability. Another point not to be overlooked is the need to know what the data is being used for, i.e. what it is being used for.
As with all projects involving the organisation of work, transparency and communication between each department and business area is a decisive factor in success, and will greatly facilitate the implementation of effective data governance. Fortunately, the human factor remains paramount.
Speaking of people, let's take a closer look at the key people involved in such initiatives. There is increasing talk of Chief Data Officers (CDOs) and data scientists. But who are they and what do they do?
A CDO is responsible for data governance. He or she is in charge of managing the organisation's data: quality, security, consistency, data protection and dialogue between each division.
As for data scientists, they are the experts in their organisation's data. Responsible for collecting and processing data, their job is to make it usable (format, volume, etc.). They then analyse the data to detect tangible trends in line with the company's business and strategy.
As you will have realised, data governance requires, above all, a good understanding of your data assets. To achieve this, the recommended exercise is to draw up a data map and keep it up to date! By mapping your data, you can quickly put in place a precise and effective action plan.
Data, whether structured or unstructured, is the driving force behind decision-making, innovation and digital transformation. However, with the explosion in data volumes, it is becoming increasingly difficult to organise it, secure it and exploit it optimally. Each company or organisation has its own challenges, depending on its strategy, environment and objectives. Data comes into play in a number of ways.
Make informed decisions and analyse performance
Understanding and managing customer relations
Facilitating innovation and the development of new products/services
Forecasting and planning
As well as possessing data, it is important to manipulate quality data in order to extract its potential. But how can you guarantee the quality of your data assets?
Putting in place and maintaining solid governance that guarantees the quality and accuracy of data strengthens users’ confidence in the information they consult and use. One of the key aspects of data governance is data quality.
As part of data governance, it is essential to define policies and processes to identify, cleanse and delete non-compliant or unnecessary data.
Who is responsible for ensuring data quality? We are convinced that data quality should not be the sole concern of the Data or IT teams. It should be everyone’s business!
To ensure data quality when implementing data governance, a data culture needs to be developed within the company or organisation to raise awareness of various issues such as over-stocking, sources, classification of recorded data, etc. But also, and of course, the quality of recorded data. But also, and of course, the quality of the data recorded. For example, this could be contact data from a trade show (sales team), specific customer requests (sales administration team), stock updates (production team), etc. Data is generated at all levels, in the different business lines, etc.
Technically, here is an example of how to set up data quality monitoring. It requires an approach involving analysis, classification and ongoing processing throughout its lifecycle. All existing data must be carefully examined, catalogued and processed, while new data requires an initial assessment, appropriate classification and processing adapted to its nature.
It is crucial to recognise that data is not a permanent record once it has been processed. It can become obsolete, vulnerable, non-compliant or sensitive over time. As a result, constant or recurring vigilance is required to ensure that data remains secure, compliant and relevant as it evolves.
By adopting operational data governance, organisations can ensure that every piece of data is assessed on a regular basis, while putting in place appropriate measures to maintain its value. This iterative process ensures robust data governance and promotes informed decision-making based on reliable information.
An iterative approach is put in place for the purpose of analysis, observation and continuous improvement. For an overview of how to maintain data quality, here is an example of an iterative approach to monitoring data quality, with the different stages and the many solutions/tools available for each of them:
By understanding the different stages in the data life cycle and the continuous improvement approach (above), it is easier to associate the tools available with each stage.
The Data Governance module of Blueway’s Phoenix data integration and management platform, which takes full advantage of your data cycle, enables you to share a common representation of your data assets throughout the company! Thanks to this module, you can ensure the quality, security and relevance of your data from a unified, scalable platform.
Management of different environments, applications and servers, adaptable and scalable data model.
Management of authorisations and access, management rules, automatic error checking, declaration of event elements (triggers).
Access to directories, business applications and standard application connectors, application mapping and impact analysis.
Documentation, revision management and versioning, rebuilding repositories to date.
Data quality indicators and dashboards to analyse all aspects of your data assets.
At Blueway, we are convinced of the importance of end-to-end data governance. It’s what makes data consolidation possible, combining data from several different sources into a single, consistent set of data. That’s why our modular Phoenix platform orchestrates both technical and hybrid processes, and guarantees compliance with business rules within these processes (management of sequences, alerts and information routing rules) according to predefined management rules or dynamic rules.
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It is estimated that the global volume of data will reach 175 zettabytes by 2025, with only 0.5% processed to date.
Data quality is crucial for informed decision-making, performance analysis, customer understanding, innovation and process optimisation.
Data quality includes the accuracy, reliability, consistency and relevance of the information to its intended use.
Tools such as data catalogues, Data Lineage, Data Discovery, data quality analysis, master data management, a data repository, visualisation and reporting are examples of tools for guaranteeing data quality.
