Data governance is a challenging job. It is most difficult to implement during the initial stages and to overcome the tough steps the organizations are suggested to have a strong data governance team. Below are some of the significant challenges met while implementing data governance:
Demonstrating Business Values
This is the first step and equipped with the real struggle to get the initiative approved. It requires the development of quantifiable metrics and basically for the purpose of data quality improvements. It includes a number of resolved data errors measured quarterly and is evident from cost savings or revenue gains as a result of the outcome of the effort.
Self-Service Analytics Support
This is a new challenge in data governance. Data is put in the hands of several users in the organizations. It is here important to ensure data is accurate and it can be accessible for self-service users such as citizen data scientists, executives and business analysts. Moreover, the streaming data complicates the efforts.
Governing Big Data
Big data systems deployment is the new challenge in data governance programs and a mix of structured, unstructured and semi-structured are to be dealt with. Such big data environments contain more than one data platform like NoSQL database, Hadoop and Spark systems, and cloud object stores. Often the data in the big systems is stored in raw form and thereafter needs to be filtered as per the analytics uses.
Data Governance Pillars
Below are some of the important pillars of data governance and referred from the Profisee master data management system:
Data Stewardship
Usually, data stewardship is accountable for the success or failure of data governance. He looks after the data quality, data usage and data security. Big organizations have teams of such data stewards to execute the data policies correctly.
Data Quality
This is the biggest driving force in perfect data governance activities. The data quality looks after accuracy, completeness and consistency of data across the data systems. Data cleansing is an important element of data quality to fix data inconsistencies and data errors.
Master Data Management
It is a data management discipline to look after data governance. It establishes a master data set on products, customers and other entities.
Data Governance Use Cases
The effectiveness of data governance is based on the data management used in the operational systems as well as the analytics applications that are fed by the data warehouses. The data lakes and data marts also reciprocate.
Data Governance Tools, Vendors
There are several vendors who offer data governance tools and significant ones are IBM, Oracle, SAS Institute, SAP, Information Builders and Informatica.
Some of the data management specialists to name here are Infogix, Talend, Erwin, Collibra, Ataccama, ASC Technologies and Adaptive. The tools incorporated include data lineage functionality and metadata management features. The data catalog software is made available from vendors like Boomi, Cambridge Semantics, Alteryx, Alation and Data World.
