Data is a competitive advantage for any organization that knows how to properly extract its insights. But to be successful, organizations must govern both internal and external data to improve its quality, use, trust, and more. When done properly, governance also bridges the technical-to-business divide by engaging all parties to combat the increasingly complex demands around regulations and compliance. Data governance gives organizations the transparency into all aspects of their data assets, from the data available, its owner/steward, lineage and usage, to its associated definitions, synonyms and business attributes.
Full visibility into an organization’s data allows all data users to gain valuable insights into not only the details of their data assets, but the attendant risks associated with its use across business applications. Many organizations across multiple industries are now looking to implement a data governance framework, however, many organizations do not understand the importance of analytics for its success.
Why Analytics are Important to Data Governance
The reason analytics are so important to data governance can be summed up in one word, automation. Analytics can help automate some important tasks that would normally take large teams of people to accomplish. Analytics can also provide additional insights into data that would otherwise go unnoticed. By applying techniques, such as machine learning to data sets, organizations can automatically detect anomalies based on historical patterns, rather than a person setting a rule to look for them.
This is increasingly important as complex demands around regulations and compliance continue to increase. Let’s use the General Data Protection Regulation (GDPR) as an example. The intent of GDPR is to strengthen and unify data protection for all individuals within the European Union (EU). The goal of GDPR is to give control back to citizens and residents over their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.
To ensure GDPR compliance, organizations across the world will need to ensure governance around personal data documentation, identification, tracking, and usage approval. A data governance framework with analytics incorporated into it can deliver enterprise-wide control and visibility into personal data processing risk areas, automatically identify where proper oversight may be lacking, and utilize machine learning to account for any hidden personal data, in order to comply with GDPR. But to achieve compliance requires the right solution. And to do so, using one solution instead of many is the preferential route for many organizations in an effort to increase efficiency and reduce cost.
A Solution for Wrapping Analytics Around Data Governance
A proper data governance solution should deliver an all-inclusive view of an organization’s data landscape allowing organizations to easily define, track, and manage all aspects of their data assets. This enables collaboration, knowledge-sharing, and user empowerment through transparency across an enterprise.
With analytics wrapped around the solution, it should include a self-service, big data analytics platform designed to handle not one, but rather multiple steps from data ingestion and preparation to data analysis and operationalization. Where data quality checks are required, an integrated platform solution must be able to conduct data profiling, completeness, consistency, reconciliation/balancing, timeliness, and value conformity in order to roll up data quality KPI’s alongside data definitions within the data governance business glossary. The solution should be designed with a data preparation visual workflow to empower the business user to aggregate and control data in order to accelerate and improve the subsequent data analysis process, applying analytics to extract value from the data.
Users should find a solution that enables them to source data from multiple data platforms and applications. It should empower users to apply statistical and process controls, as well as machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting. Users can create reports and dashboards to visualize the results and collaborate with other users. Additionally, it should allow users to create automated notifications, manage exception workflows, and develop automated data-processing pipelines to integrate the results of that analysis back into operational applications and business processes.
To learn more about wrapping analytics around data governance, download the white paper below.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.