Day to day operations and decisions of the contemporary enterprise are driven by information in operational data stores. To facilitate business efficiencies, information such as customer and product data is collected, consolidated, quality-assured, and distributed throughout the company. Master data management is the set of tools, policies and processes to define and manage this data.
Obstacles: Managing Evolving Data Sources
Mastering data is difficult as it resides in multiple sources with varying levels of quality. Ownership of the data presents political challenges and it is typically a moving target. In order to provide reliable master data to the enterprise, firms need to address data consolidation and completion issues, improve data accuracy, and leverage a generic model for consistency.
With transactional data there are also concerns such as:
- State - transactional data undergoes a lifecycle of “states”. Typically we think of these states as new, pending, approved or terminated but they are native to the real life concept the data is modeling. Regardless, the state is central to the transaction the data supports and is a key trait.
- Collaboration - most transactions require interaction or collaboration between two operators (systems or people) to create or modify it and each has his role and rights in the transaction. The lifecycle of the data describes this flow.
- Complexity - typical reference data serves a single purpose as a lookup table; however, transactional reference data is not this simple. It is naturally complex in its valid forms, disparate in where it resides and often changes.
Challenges vary by industry, for example:
A logistics company cannot reliably deliver customer freight without accurate route, vessel and location data.
An oil and gas company requires complete information about oil wells, facilities and transport to fuel their operations and set production targets.
Telecom companies analyze a mixed catalog of multi attribute equipment and service plan data to segment markets and decrease churn.
High tech firms employ tiered and regional sales force data to effectively service customers and to optimize revenues in geographies or in accounts.
Insurers maintain a wide range of policy data and need to understand their tiered distribution model to reach their customers and incent sales.
Solution: Talend for MDM
Making sense of data and creating a reliable master that can be shared across the organization requires that you first agree upon a model, outline the process and lifecycle of the data and then use a MDM tool to model and enforce policy. Talend provides the tooling to simplify planning, building, deploying and managing master data combining complete functionality for data integration, data quality, data profiling, data mastering, and data governance. Talend provides collaborative workflow enabling teams to build and enforce data governance policies. It provides a system of record and ensures that master data stays clean and is made available to those who need it.
The unique Active Data Model allows you to modify the model in real time and have the various owners of the data review changes to the model, validations, workflow, user rights and translations though a collaborative interface and in real time. This iterative approach to data governance is unique to Talend.
Talend Master Data Management
Talend provides a powerful and flexible master data management (MDM) solution to model and master any domain turning disparate, inconsistent information across your business into a single unified view of information.
Unlike other solutions where you need to integrate products to make a solution, Talend’s products improve your productivity through a unified platform - a common code repository and tooling for scheduling, metadata management, data processing and service enablement.