Tag: idc technology spotlight

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

So most early-stage data governance managers kick off a series of projects to profile data, make inferences about data element structure and format, and store the presumptive metadata in some metadata repository. But are these rampant and often uncontrolled projects to collect metadata properly motivated?

There is rarely a clear directive about how metadata is used. Therefore prior to launching metadata collection tasks, it is important to specifically direct how the knowledge embedded within the corporate metadata should be used.

Managing metadata should not be a sub-goal of data governance. Today, metadata is the heart of enterprise data management and governance/ intelligence efforts and should have a clear strategy – rather than just something you do.

What Is Metadata?

Quite simply, metadata is data about data. It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed. Metadata is valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. It answers these important questions:

• What data do we have?
• Where did it come from?
• Where is it now?
• How has it changed since it was originally created or captured?
• Who is authorized to use it and how?
• Is it sensitive or are there any risks associated with it?

The Role of Metadata in Data Governance

Organizations don’t know what they don’t know, and this problem is only getting worse. As data continues to proliferate, so does the need for data and analytics initiatives to make sense of it all. Here are some benefits of metadata management for data governance use cases:

• Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.
• Quicker Project Delivery: Accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc., by up to 70 percent.
• Faster Speed to Insights: Reverse the current 80/20 rule that keeps high-paid knowledge workers too busy finding, understanding and resolving errors or inconsistencies to actually analyze source data.
• Greater Productivity & Reduced Costs: Being able to rely on automated and repeatable metadata management processes results in greater productivity. Some erwin customers report productivity gains of 85+% for coding, 70+% for metadata discovery, up to 50% for data design, up to 70% for data conversion, and up to 80% for data mapping.
• Regulatory Compliance: Regulations such as GDPR, HIPAA, PII, BCBS and CCPA have data privacy and security mandates, so sensitive data needs to be tagged, its lineage documented, and its flows depicted for traceability.
• Digital Transformation: Knowing what data exists and its value potential promotes digital transformation by improving digital experiences, enhancing digital operations, driving digital innovation and building digital ecosystems.
• Enterprise Collaboration: With the business driving alignment between data governance and strategic enterprise goals and IT handling the technical mechanics of data management, the door opens to finding, trusting and using data to effectively meet organizational objectives.

Giving Metadata Meaning

So how do you give metadata meaning? While this sounds like a deep philosophical question, the reality is the right tools can make all the difference.

erwin Data Intelligence (erwin DI) combines data management and data governance processes in an automated flow.

It’s unique in its ability to automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable within the context of role-based views.

• What Is Data Modeling: Conceptual, Logical and Physical. The Types of Data Model Explained

erwin DI sits on a common metamodel that is open, extensible and comes with a full set of APIs. A comprehensive list of erwin-owned standard data connectors are included for automated harvesting, refreshing and version-controlled metadata management. Optional erwin Smart Data Connectors reverse-engineer ETL code of all types and connect bi-directionally with reporting and other ecosystem tools. These connectors offer the fastest and most accurate path to data lineage, impact analysis and other detailed graphical relationships.

Additionally, erwin DI is part of the larger erwin EDGE platform that integrates data modeling, enterprise architecture, business process modeling, data cataloging and data literacy. We know our customers need an active metadata-driven approach to:

• Understand their business, technology and data architectures and the relationships between them
• Create an automate a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage
• Activate their metadata to drive agile and well-governed data preparation with integrated business glossaries and data dictionaries that provide business context for stakeholder data literacy

erwin was named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”

Click here to get a free copy of the report.

Click here to request a demo of erwin DI.

 

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.

• Download Free GDPR Guide | Step By Step Guide to Data Governance for GDPR‎

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.

• IDC Technology Spotlight, Data Intelligence in Context: Get the report (… it’s free)

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.

• How to automate data mapping

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.

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.

• What is the difference between GDPR and CCPA?

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