Ataccama sponsors 2022 State of Data Quality e-book by TDWI
We integrate data from many different data sources for the purposes of unification and consolidation, in order to make the data available and facilitate reporting. Throughout the integration process, we comply with the data governance and data anonymization required by GDPR as well as the organization’s internal policies.
Data integration makes it possible to merge relevant data, transform them, and gain a better overview of the data as a whole
Customers gain unlimited options for advanced data analytics
Data integration facilitates standardization, cleaning, enrichment and validation
We improve data quality and deliver reliable and clear reports to end users
As part of the data integration process, we also standardize and centralize the metadata
We ensure comprehensive data governance
We can mask the integrated data using advanced tools
Data are anonymized according to the specific needs of the organization/department. We can implement anonymization during data transfers as well as in any layer of the target storage
We focus on security when storing data (in order to prevent unauthorized access), and when making them available on the platform
We ensure data security in all environments and layers, and for all user roles, in accordance with the company’s data governance
We classify data for each data source, starting from the lowest level. In practice, this means that different datasets from the same data source may be classified separately
We create and build different environments that only authorized users can access
We apply automated orchestration to guarantee operations are performed regularly and smoothly
Integration workflows remain consistent, according to predefined rules
During the data integration process, we analyze the source data and the output that has been requested. At the same time, we process and supplement the metadata, which is then used for the data integration.
The integrated data are usually transferred into several layers, and in each of those, the data may serve a different purpose and take a different form.
(GitHub, GitLab, Azure DevOps, etc.)
(SQL databases, NoSQL databases)
We have enabled a large automotive company to work efficiently with (IoT) sensor data from manufacturing. At the same time, we have lightened the system load and introduced data retention in the source system.
The car manufacturer gained the required space savings in the source production system, the system load was reduced, and 12-month data retention was implemented.
For one large automotive company, we have improved the data quality of customer data, integrated individual CRM modules into a unified data solution, and further enriched it with data from public registers.
Throughout the solution, we have emphasized anonymizing sensitive data, with some data anonymization already being carried out during acquisition.
By integrating data directly from the JIRA source system, we are able to prepare a detailed overview of the status of multiple projects, including all their subtasks and timesheets, for a large automotive company.
As we are able to determine risks early, we can give the persons responsible on the customer’s side advance warning of any potential dangers or complications.
At a large manufacturing company, we have automated data uploads from public registers. This has eliminated errors caused by freely, manually entering data on company addresses and executives.
Let's get more loyal customers by giving them a better customer experience.
"We have quite a broad portfolio of competencies. Of course, each competency requires a slightly different background, but generally, people in our team have to be motivated, eager to do something, and have common sense. And also a great deal of inventive
We will contact you as soon as possible.