Syncler Use Cases
DQM made easy - how to get started with Syncler
Data is like a treasure in your organisation - but what good is the treasure if it remains buried deep and unused? It is only when data is managed, analysed and visualised in an understandable way that it unfolds its full value as a reliable basis for decision making.
In the reality of many companies, however, the opposite is often the case: data quality management (DQM) is often neglected. However, taking the first steps does not have to be complicated or expensive.
The topics at a glance:
- Data Quality Management starts with clarity
- What makes good Data Quality Management?
- The smart start to living DQM with Syncler
- DQM Live Scoring: Check your data in real time
- Application examples: Data cleansing and validation in practice
- Conclusion: Why DQM pays off
Data Quality Management starts with clarity
Let's face it: do you know how your data is doing?
- Does your organisation have data quality standards?
- Do you have visibility of your metadata?
- Have you defined processes and responsibilites for ensuring data quality?
- How do you rate your master data management?
If you are pondering the answers to these questions, you need to take action. Lack of visibility and inadequate processes lead to inconsistent and unreliable data. This in turn leads to inefficient processes, poor decisions and missed opportunities.
What makes good Data Quality Management?
DQM ensures a consistent, valid and trustworthy database. This means:
- Transparency about data sources and their availability
- Clear responsibilities for data maintenance
- Uniform standards for data assessment and cleansing
- Cross-departmental management for standardised information
This is the only way to establish data as a real asset in the company – as the basis for informed decisions, successful customer approach and efficient processes.
The smart start to living DQM with Syncler
Many companies shy away from complex DQM projects - especially in the SME sector. Our Syncler DQM module is designed to address this issue. With a focused set of functions, we offer an easy and cost-effective entry into the subject – and combine data integration, data validation and data verification in one workflow.
Our two core functions:
1. DQM for Syncs
Checks data as it is received, before it is created in a target system – continuously throughout the data exchange.
2. DQM for Reports
Analyses and cleanses existing databases and enables the monitoring of defined criteria, such as 'How many orders exist without an invoice?’.
Both functions are based on pre-defined rules as standard. However, these can also be customised as required. The result is an accurate, reliable, relevant and consistent database. This creates the scope for efficient processes, sound analysis, good decisions and the sensible use of AI applications.
DQM Live Scoring: Check your data in real time
Organisations often know that their data quality is not good - but it is difficult to quantify or qualify this. Manual checks are time consuming. Many companies lack qualified DQM resources and there are no guidelines for data quality standards.
This is where our new DQM Live Scoring comes in. It checks selected data fields against clearly defined dimensions:
- Uniqueness: Each record is unique an only exists once.
- Timeliness: All data is current and regularly updated.
- Completeness: All records are complete.
- Validity: Data conforms to specified formats and rules.
- Accuracy: The data reflects reality and is accurate.
All results are displayed in an easy-to-understand visualisation. Thanks to no-code modules, the analysis can be started independently with just a few clicks. An average value of all tested dimensions is calculated, providing the basis for targeted optimisation measures.
Application examples: Data cleansing and validation in practice
The Syncler DQM module analyses the data, identifies duplicates and inconsistencies and compares them. Incorrect records can then be corrected and merged either manually or automatically. In this way, we help companies to achieve a clean, consistent and trustworthy database in a wide range of areas.
The following examples provide an insight into the wide range of applications:
1. Data cleansing in CRM: how to improve customer communication
Outdated customer data and duplicates lead to inefficient marketing and sales processes, resulting in poor customer communication.
The solution: Successful data quality management corrects and validates data within the organisation. It can significantly improve customer targeting and provide the basis for personalised communication.
2. Data quality in BI & analytics: informed decisions through valid data
Incorrect and incomplete data distorts analysis and, in the worst case, leads to poor strategic decisions.
The solution: Syncler DQM ensures that the quality of all relevant data is improved. This means you can be sure that your analyses and forecasts are based on a valid and consistent database. This gives you the confidence to make informed decisions.
3. Compliance: meet regulatory requirements with valid data
Regulatory requirements and regulations, such as GDPR, demand accurate, complete and up-to-date data.
The solution: Continuously analyse databases to ensure they comply with all regulatory requirements. That way, you stay on the safe side.
Conclusion: Why DQM pays off
Data is valuable - but only if it is reliable. That is why DQM is crucial in many areas: to improve efficiency, compliance and customer focus. With Syncler DQM, we offer a straightforward introduction to the subject.
Whether for analysis, cleansing or continuous monitoring: Syncler DQM makes data quality measurable and easy to implement. Discover the real potential of your data. Try our DQM Live Scoring and bring transparency, structure and quality to your data world.
