DATA QUALITY DIAGNOSIS

Our Data Quality Diagnosis service will help you evaluate the level of quality that business data possess in order to determine the current status of data ahead of a current or future situation. Performing discovery data activities, profiling, and subsequent analysis of the results, we can identify data problems affecting critical business entities.

This assessment is supported by our experience in data management, our knowledge in the industries, and the validation of the analysis of key business users; in order to achieve work plans to reduce critical risks and allow to generate immediate business benefits.

DATA CLEANSING AND ENRICHMENT

Our Data Cleansing and Enrichment service, allows you to define, design and implement in detail the plans of correction and improvement of the data emerging through a diagnosis. Besides the cleansing and/or enrichment of the data, the service focuses on ensuring the consistency of the data sets that have been formed as the collection of data from multiple databases. The activities involve tasks such as correction and elimination of "dirty" data from one or multiple databases (eg. incorrect data, invalid, outdated, redundant, incomplete, non-intact, etc)

DATA CONVERSION AND MIGRATION

In a systems implementation project there are several working groups with specialized tasks, such as: PMO(responsible for project management), change Management group, Functional Analysis group, Development group, Integration group, data Migration group. The data migration tasks are not executed regardless of the project but are closely linked to its progress, and the working group on migration and data conversion interacts closely with other groups.

Our experience in systems implementation projects allows us to state that the planning, implementation and monitoring of issues related to data, becomes a key factor for the success of a systems implementation.

To achieve a proper migration and data conversion, Matrice Consulting has designed a data conversion control service focused on assisting Management of the project, the leader of the migration group, and the users involved, providing methodology and tools, to store the new set of data necessary and an acceptable level of quality, supporting the functionality of the new system.

The focus of our methodology considers performing the conversion control from an independent point of view, adding value at all stages of the project with an "operational vision" focused on solving problems, allowing to detect and correct errors in the appropriate time and not when the global testing and final migration takes place, or after the new system is in production, thus reducing stabilization times post-implementation, and adding certainty to the date defined as "D" Day.

DATA GOVERNMENT

Data governance refers to the policies and processes by which a company manages the quality, consistency, usability, security and availability of their data and corporate information. When we talk about corporate information, we are not limited only to data related to clients, products and services, but to all corporate data.

A data governance framework comprises the following components:

• Policies and Standards: Define and maintain policies and standards related to, for example data creation, data use, data quality, data ownership, internal and external data distribution, etc.
• Technology: Define, implement and manage technology to implement the government strategy (integration tools of customer data (CDI - Customer Data Integration)tools, Metadata Management, etc.)
• Organization: Define and implement the roles and responsibilities of the data governance organization. A possible structure can have the following equipment: Administration Committee, Governing Council Data, Quality Data Group (technical roles / business roles), etc.

A data governance framework comprises the following components:

• Processes: The processes and policies of the data must be formalized, published and communicated throughout the organization at all appropriate sectors. The data governance model process of implementation consists of four basic stages: Government Data Planning, Design and definition of Government Data, Implementation of Government Data, Execution and Monitoring.