The expansion in business application and integration complexity is making it more difficult for organizations to sustain data quality at high levels. Datability Ninety X is built on innovative concepts like user accountability, automatic transformation and loading, and integrated manual cleansing, among other unique abilities to help organizations keep data clean all the time.
The Rules Management Engine is responsible on the administration of Datability Ninety X quality rule books, in which cleansing routines and parameters reside. The quality rules books have hundreds of rules ready out-of-the-box. These books contain general rules that are applicable on general customer and product data, like age and birthday rules, gender, names, contacts, … etc. Additionally, they contain country specific quality rules such as National ID verification, occupation category, ... etc. Moreover, the rule books also contain industry-specific rules including those imposed by the regulator. Customer-specific data are added on installation.
MACE is cornerstone component in the solution as it is responsible for genuine integration of manual cleansing into the whole process, sharing the same rules, user access rights, and parameters. MACE manages with data cleansing tasks that need human intervention and cannot be automatically cleaned. MACE manages the full cycle of bulk cleansing, distributing data to agents in controlled excel sheet that are not modifiable and that have clear marking of fields that have issues and notes showing the issue description. MACE also, using interfaces to the organization content management system, facilitates evidence-based cleansing. Finally, MACE automates manual record survivorship based on record and field matching.
DNX Platform leverages a set of activities that ensure organization goals are met in an effective and efficient manner. DNX performance management dashboard records & exhibits pending tasks, workload, AVG time per task and jobs achievements percentage ..etc,
What doesn’t get measured, doesn’t get done. This is the main purpose of the Quality Monitor, it tracks a comprehensive list of metrics that indicate trends in data quality in your organization, and provides dashboards to track individual and group performance in terms of quality upkeep. It also provides notifications through integration with email and SMS services, to send alerts and produce data quality mismatch reports. This serves as an immediate warning to users aiming for ongoing behaviour correction.
DNX Engine is the core of Datability Ninety X which contains diversified components as the following:
The DNX Data Area is all operational data reside in a very sophisticated structure. It contains restructured source data, reference data, parameter database rules data, quality analytics data, project data and clean data and its versions.
Using the relevant Rule Book, the Issue Isolation Engine sniffs through the data to isolate those data items that do not conform to relevant quality rules.
Once a data issue has been identified, the Remedy Execution Engine applies the corrective action on the relevant data element whether it came from an automatic cleansing task or a manual cleansing process.
Moving data between systems is an extremely error-prone task. DNX make it easier to implement, Business and application users design the interfaces using parameters to map source data. The engine, then, automatically generates the transformation and loading jobs.
DNX Manual Cleansing Engine (MACE) is a cornerstone component in the solution. MACE manages all aspects of manual cleansing, keeping data access rules compatible with those of the original data sets. MACE manages data survivorship, evidence-based cleansing, and bulk data cleansing efforts.
DNX Quality Monitor, tracks a comprehensive list of metrics that indicate trends in data quality in the organization. It provides dashboard to track individual and group performance in terms of quality upkeep. Additionally, data quality KPIs are produced and can be used to better manage performance.
The Publisher helps users design a target data structure to populate with clean data from Datability Ninety X. It leverages data services available by Datability Ninety X and interfaces with ATL to automatically create the extraction and loading jobs to populate the target structure.