Author: Robert Lutton

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erwin Expert Blog

Defining DG: What Can Data Governance Do for You?

Data governance (DG) is becoming more commonplace because of data-driven business, yet defining DG and putting into sound practice is still difficult for many organizations.

Defining DG

The absence of a standard approach to defining DG could be down to its history of missed expectations, false starts and negative perceptions about it being expensive, intrusive, impeding innovation and not delivering any value. Without success stories to point to, the best way of doing and defining DG wasn’t clear.

On the flip side, the absence of a standard approach to defining DG could be the reason for its history of lacklustre implementation efforts, because those responsible for overseeing it had different ideas about what should be done.

Therefore, it’s been difficult to fully fund a data governance initiative that is underpinned by an effective data management capability. And many organizations don’t distinguish between data governance and data management, using the terms interchangeably and so adding to the confusion.

Defining DG: The Data Governance Conundrum

While research indicates most view data governance as “critically important” or they recognize the value of data, the large percentage without a formal data governance strategy in place indicates there are still significant teething problems.

How Important is Data Governance

And that’s the data governance conundrum. It is essential but unwanted and/or painful.

It is a complex chore, so organizations have lacked the motivation to start and effectively sustain it. But faced with the General Data Protection Regulation (GDPR) and other compliance requirements, they have been doing the bare minimum to avoid the fines and reputational damage.

And arguably, herein lies the problem. Organizations look at data governance as something they have to do rather than seeing what it could do for them.

Data governance has its roots in the structure of business terms and technical metadata, but it has tendrils and deep associations with many other components of a data management strategy and should serve as the foundation of that platform.

With data governance at the heart of data management, data can be discovered and made available throughout the organization for both IT and business stakeholders with approved access. This means enterprise architecture, business process, data modeling and data mapping all can draw from a central metadata repository for a single source of data truth, which improves data quality, trust and use to support organizational objectives.

But this “data nirvana” requires a change in approach to data governance. First, recognizing that Data Governance 1.0 was made for a different time when the volume, variety and velocity of the data an organization had to manage was far lower and when data governance’s reach only extended to cataloging data to support search and discovery. 

Data Governance Evolution

Modern data governance needs to meet the needs of data-driven business. We call this adaptation “Evolving DG.” It is the journey to a cost-effective, mature, repeatable process that permeates the whole organization.

The primary components of Evolving DG are:

  • Evaluate
  • Plan
  • Configure
  • Deliver
  • Feedback

The final step in such an evolution is the implementation of the erwin Enterprise Data Governance Experience (EDGE) platform.

The erwin EDGE places data governance at the heart of the larger data management suite. By unifying the data management suite at a fundamental level, an organization’s data is no longer marred by departmental and software silos. It brings together both IT and the business for data-driven insights, regulatory compliance, agile innovation and business transformation.

It allows every critical piece of the data management and data governance lifecycle to draw from a single source of data truth and ensure quality throughout the data pipeline, helping organizations achieve their strategic objectives including:

  • Operational efficiency
  • Revenue growth
  • Compliance, security and privacy
  • Increased customer satisfaction
  • Improved decision-making

To learn how you can evolve your data governance practice and get an EDGE on your competition, click here.

Solving the Enterprise Data Dilemma

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erwin Expert Blog

Data Discovery Fire Drill: Why Isn’t My Executive Business Intelligence Report Correct?

Executive business intelligence (BI) reporting can be incomplete, inconsistent and/or inaccurate, becoming a critical concern for the executive management team trying to make informed business decisions. When issues arise, it is up to the IT department to figure out what the problem is, where it occurred, and how to fix it. This is not a trivial task.

Take the following scenario in which a CEO receives two reports supposedly from the same set of data, but each report shows different results. Which report is correct?  If this is something your organization has experienced, then you know what happens next – the data discovery fire drill.

A flurry of activities take place, suspending all other top priorities. A special team is quickly assembled to delve into each report. They review the data sources, ETL processes and data marts in an effort to trace the events that affected the data. Fire drills like the above can consume days if not weeks of effort to locate the error.

In the above situation it turns out there was a new update to one ETL process that was implemented in only one report. When you multiply the number of data discovery fire drills by the number of data quality concerns for any executive business intelligence report, the costs continue to mount.

Data can arrive from multiple systems at the same time, often occurring rapidly and in parallel. In some cases, the ETL load itself may generate new data. Through all of this, IT still has to answer two fundamental questions: where did this data come from, and how did it get here?

Accurate Executive Business Intelligence Reporting Requires Data Governance

As the volume of data rapidly increases, BI data environments are becoming more complex. To manage this complexity, organizations invest in a multitude of elaborate and expensive tools. But despite this investment, IT is still overwhelmed trying to track the vast collection of data within their BI environment. Is more technology the answer?

Perhaps the better question we should look to answer is: how can we avoid these data discovery fires in the future?

We believe it’s possible to prevent data discovery fires, and that starts with proper data governance and a strong data lineage capability.

Data Discovery Fire Drill: Executive Business Intelligence

Why is data governance important?

  • Governed data promotes data sharing.
  • Data standards make data more reusable.
  • Greater context in data definitions assist in more accurate analytics.
  • A clear set of data policies and procedures support data security.

Why is data lineage important?

  • Data trust is built by establishing its origins.
  • The troubleshooting process is simplified by enabling data to be traced.
  • The risk of ETL data loss is reduced by exposing potential problems in the process.
  • Business rules, which otherwise would be buried in an ETL process, are visible.

Data Governance Enables Data-Driven Business

In the context of modern, data-driven business in which organizations are essentially production lines of information – data governance is responsible for the health and maintenance of said production line.

It’s the enabling factor of the enterprise data management suite that ensures data quality,  so organizations can have greater trust in their data. It ensures that any data created is properly stored, tagged and assigned the context needed to prevent corruption or loss as it moves through the production line – greatly enhancing data discovery.

Alongside improving data quality, aiding in regulatory compliance, and making practices like tracing data lineage easier, sound data governance also helps organizations be proactive with their data, using it to drive revenue. They can make better decisions faster and negate the likelihood of costly mistakes and data breaches that would eat into their  bottom lines.

For more information about how data governance supports executive business intelligence and the rest of the enterprise data management suite, click here.

Data governance is everyone's business