Our Partners

 AVS Systems is a Collibra Business Partner and has extensive experience implementing Collibra end-to-end.

Collibra Highlights

• Articulate, promote and position Data as an Enterprise Asset, Standards and Best Practices
• Maintain Collibra DGC Framework & Operating Model
• Implement Business Glossary (terms, metrics, kpi), Policy & Rules, Dashboard
• Data Stewardship Process & Customization
• Workflow Process and Customization (Activiti)
• Reference Data Management & Data Dictionary
• Collibra Connect Integration w/Mulesoft ESB (Rest-API, Web-Services)
• UI Customization, Pagination/Overrides, LDAP/SSO integration, Roles & Responsibility
• Implementation of RACI activities
• Mentoring, Training and Documentation.

• Data governance is a process framework focused on managing the quality, consistency, usability, security, and availability of information. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures. 
• Data Governance is the convergence of data quality, data management, policies, rules, business process management, data stewardship management, change management, issue management, and risk management functions surrounding the handling of Information such as master data, enterprise dimensions, hierarchies, relationships, associated attributes and mappings as an Enterprise Asset.

Today’s economy and business trends demand quick action and cohesive handshake among all business processes so that companies can understand the real benefits that are hidden underneath the guise of inaccurate and unreliable data spread across disparate systems and also due to business initiatives taking place such as citizens or corporate name changes, mergers and acquisitions, parent-subsidiary relationships, divestiture, splits, etc. 

Businesses are faced with the immediate need to consolidate their data and integrate them so that immense value can be realized. Companies need to be able to work together in a unified environment so that Master Data Management/Customer Data Integration can take place. 

• Data Warehouse is termed as a collection of data from disparate systems which are systems of record  (built in silos, of course) and consolidate and integrate them in a common repository in a manner where data is standardized, cleansed, de-duped, matched, corrected and validated to  serve as a 'single version of the truth',  a.k.a., golden copy. 
• Data warehouse implementation requires solid strategy, architecture and design to make it successful.
• To that end, AVS has  developed innovative architecture and methodologies to implement and support  Data Warehouses and have several years of  experience to back that. 

• Data Integration is a common process of integration surrounding around similar data stored in disparate systems across the enterprise. The common similar data such as Customers,  Suppliers, Employees or even value chain  process coming from various sources are  captured, brought into a common staging area where data undergoes battery of adjustments  and consolidation including quality checks,  validations, standardization, eliminating duplicates, merge and transformation. The end results are then either accumulated in a Data Warehouse or simply distributed to various  participating systems and applications thus  providing immense value. 

Data Quality is critical for Business Confidence!!!  

Information that you can Trust!

Often businesses spend as much time and effort gathering new customers as they do on anything else. It’s also one of the most costly functions of doing business. So it’s important to make sure you don’t lose the customers you’ve spent so much energy to acquire. To support this cause, businesses end of acquiring various third-party applications and tools and also develop many home-grown applications thus creating a huge wealth of data across various systems within the enterprise. 

The quality of data within the enterprise is measured according to its reliability and validity; the completeness and accuracy of a data set. It is usually measured by comparing the data set to another data set identified as the "gold standard", and assessing the level of agreement, thus creating a Master Record.