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Metadata Management, Data Governance and Automation

by Mariann McDonagh       • November 6, 2019

Can the 80/20 Rule Be Reversed?

erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect.

This research showed that the majority of responding organizations weren’t actually prepared for GDPR, nor did they have the understanding, executive support and budget for data governance – although they recognized the importance of it.

Of course, data governance has evolved with astonishing speed, both in response to data privacy and security regulations and because organizations see the potential for using it to accomplish other organizational objectives.

But many of the world’s top brands still seem to be challenged in implementing and sustaining effective data governance programs (hello, Facebook).

We wonder why.

Too Much Time, Too Few Insights

According to IDC’s “Data Intelligence in Context” Technology Spotlight sponsored by erwin, “professionals who work with data spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analytics.”

Specifically, 80 percent of data professionals’ time is spent on data discovery, preparation and protection, and only 20 percent on analysis leading to insights.

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.

Often these enterprises are heavily regulated, so they need a well-defined data integration model that will help avoid data discrepancies and remove barriers to enterprise business intelligence and other meaningful use.

IT teams need the ability to smoothly generate hundreds of mappings and ETL jobs. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and data lineage.

But most organizations, especially those competing in the digital economy, don’t have enough time or money for data management using manual processes. Outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too.

The Role of Data Automation

Data governance maturity includes the ability to rely on automated and repeatable processes.

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with automatically generated and meaningful documentation of the mappings, is a powerful way to support governance while providing real insight into data movement — for data lineage and impact analysis — without interrupting system developers’ normal work methods.

GDPR compliance, for instance, requires a business to discover source-to-target mappings with all accompanying transactions, such as what business rules in the repository are applied to it, to comply with audits.

When data movement has been tracked and version-controlled, it’s possible to conduct data archeology — that is, reverse-engineering code from existing XML within the ETL layer — to uncover what has happened in the past and incorporating it into a mapping manager for fast and accurate recovery.

With automation, data professionals can meet the above needs at a fraction of the cost of the traditional, manual way. To summarize, just some of the benefits of data automation are:

• Centralized and standardized code management with all automation templates stored in a governed repository
• Better quality code and minimized rework
• Business-driven data movement and transformation specifications
• Superior data movement job designs based on best practices
• Greater agility and faster time-to-value in data preparation, deployment and governance
• Cross-platform support of scripting languages and data movement technologies

One global pharmaceutical giant reduced costs by 70 percent and generated 95 percent of production code with “zero touch.” With automation, the company improved the time to business value and significantly reduced the costly re-work associated with error-prone manual processes.

Gartner Magic Quadrant Metadata Management

Help Us Help You by Taking a Brief Survey

With 2020 just around the corner and another data regulation about to take effect, the California Consumer Privacy Act (CCPA), we’re working with Dataversity on another research project.

And this time, you guessed it – we’re focusing on data automation and how it could impact metadata management and data governance.

We would appreciate your input and will release the findings in January 2020.

Click here to take the brief survey

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