Feedback from the Manutan Group.
What does Tale of Data have to offer to meet these challenges? What were the steps involved in choosing Tale of Data? What are the benefits for Manutan's businesses and customers?
Data-driven strategy: key points for Manutan Data quality is a strategic issue for the Group, which offers its corporate and local authority customers over 700,000 products and services (office supplies, IT equipment and fittings).
Manutan'sobjectives are to:
optimize all processes that include data;
prepare data for analytical initiatives;
comply with legal obligations.
" We needed to industrialize our data quality processes," says Aude Poorjabar, Data Quality and Governance Manager at Manutan.
Following a call for tenders, Tale of Data was the obvious choice for the following reasons:
- the quality of the solution for industrializing their data quality processes;
- security and collaborative features for data analysis and remediation;
- a drastic reduction in the number of manual tasks;
- the software's ability to easily combine different data sources, such as files or databases.
"We developed our use cases according to a number of criteria that were important to us, and Tale of Data came out on top," says Mbery Ngom, Data Quality Analyst at Manutan.
The cost of the solution was also an important criterion.
Manutan's data-driven strategy with Tale of Data: the benefits The Tale of Data solution is very easy to use, enables the processing of large volumes of data and automates processes. "It's much simpler than an Excel file, for example. The solution makes it possible to process rows automatically, combine different data sources, reuse workflows and share them with multiple users," says Mbery Ngom.
" Using two different data sources, we prepared a workflow and processed our use case in two hours, where it previously took a week, using Python scripts ," adds Mbery Ngom. This functionality is essential, particularly for operational professions - data managers today, merchandizing managers tomorrow - to prepare data and control its multiple uses. The solution also features a predefined data quality algorithm. In workflows, it detects the completeness rate for various product characteristics (the height of a table, for example), enabling mandatory verification criteria to be defined, thus avoiding customer returns.
The solution enables duplicate products to be detected, data quality issues to be identified and corrective action to be taken - in short, to implement genuine data governance.