DG 2.0 Archives - erwin, Inc.

Data governance (DG) is one of the fastest growing disciplines, yet when it comes to defining data governance many organizations struggle.

Dataversity says DG is “the practices and processes which help to ensure the formal management of data assets within an organization.” These practices and processes can vary, depending on an organization’s needs. Therefore, when defining data governance for your organization, it’s important to consider the factors driving its adoption.

The General Data Protection Regulation (GDPR) has contributed significantly to data governance’s escalating prominence. In fact, erwin’s 2018 State of Data Governance Report found that 60% of organizations consider regulatory compliance to be their biggest driver of data governance.

Other significant drivers include improving customer trust/satisfaction and encouraging better decision-making, but they trail behind regulatory compliance at 49% and 45% respectively. Reputation management (30%), analytics (27%) and Big Data (21%) also are factors.

But data governance’s adoption is of little benefit without understanding how DG should be applied within these contexts. This is arguably one of the issues that’s held data governance back in the past.

With no set definition, and the historical practice of isolating data governance within IT, organizations often have had different ideas of what data governance is, even between departments. With this inter-departmental disconnect, it’s not hard to imagine why data governance has historically left a lot to be desired.

However, with the mandate for DG within GDPR, organizations must work on defining data governance organization-wide to manage its successful implementation, or face GDPR’s penalties.

Defining Data Governance: Desired Outcomes

A great place to start when defining an organization-wide DG initiative is to consider the desired business outcomes. This approach ensures that all parties involved have a common goal.

Past examples of Data Governance 1.0 were mainly concerned with cataloging data to support search and discovery. The nature of this approach, coupled with the fact that DG initiatives were typically siloed within IT departments without input from the wider business, meant the practice often struggled to add value.

Without input from the wider business, the data cataloging process suffered from a lack of context. By neglecting to include the organization’s primary data citizens – those that manage and or leverage data on a day-to-day basis for analysis and insight – organizational data was often plagued by duplications, inconsistencies and poor quality.

The nature of modern data-driven business means that such data citizens are spread throughout the organization. Furthermore, many of the key data citizens (think value-adding approaches to data use such as data-driven marketing) aren’t actively involved with IT departments.

Because of this, Data Governance 1.0 initiatives fizzled out at discouraging frequencies.

This is, of course, problematic for organizations that identify regulatory compliance as a driver of data governance. Considering the nature of data-driven business – with new data being constantly captured, stored and leveraged – meeting compliance standards can’t be viewed as a one-time fix, so data governance can’t be de-prioritized and left to fizzle out.

Even those businesses that manage to maintain the level of input data governance needs on an indefinite basis, will find the Data Governance 1.0 approach wanting. In terms of regulatory compliance, the lack of context associated with data governance 1.0, and the inaccuracies it leads to mean that potentially serious data governance issues could go unfounded and result in repercussions for non-compliance.

We recommend organizations look beyond just data cataloging and compliance as desired outcomes when implementing DG. In the data-driven business landscape, data governance finds its true potential as a value-added initiative.

Organizations that identify the desired business outcome of data governance as a value-added initiative should also consider data governance 1.0’s shortcomings and any organizations that hasn’t identified value-adding as a business outcome, should ask themselves, “why?”

Many of the biggest market disruptors of the 21^st Century have been digital savvy start-ups with robust data strategies – think Airbnb, Amazon and Netflix. Without high data governance standards, such companies would not have the level of trust in their data to confidently action such digital-first strategies, making them difficult to manage.

Therefore, in the data-driven business era, organizations should consider a Data Governance 2.0 strategy, with DG becoming an organization-wide, strategic initiative that de-silos the practice from the confines of IT.

This collaborative take on data governance intrinsically involves data’s biggest beneficiaries and users in the governance process, meaning functions like data cataloging benefit from greater context, accuracy and consistency.

It also means that organizations can have greater trust in their data and be more assured of meeting the standards set for regulatory compliance. It means that organizations can better respond to customer needs through more accurate methods of profiling and analysis, improving rates of satisfaction. And it means that organizations are less likely to suffer data breaches and their associated damages.

Defining Data Governance: The Enterprise Data Governance Experience (EDGE)

The EDGE is the erwin approach to Data Governance 2.0, empowering an organization to:

Manage any data, anywhere (Any²)
Instil a culture of collaboration and organizational empowerment
Introduce an integrated ecosystem for data management that draws from one central repository and ensures data (including real-time changes) is consistent throughout the organization
Have visibility across domains by breaking down silos between business and IT and introducing a common data vocabulary
Have regulatory peace of mind through mitigation of a wide range of risks, from GDPR to cybersecurity.

To learn more about implementing data governance, click here.

In the past, justifying data governance was notoriously difficult. The siloed nature of Data Governance 1.0, and its lack of focus on adding value meant buy-in was low.

While housing data governance (DG) within IT might have made sense in its early stage, data and data governance has evolved.

Today, we generate a staggering 2.5 quintillion bytes of data per day. With growing regulatory demands and the opportunities of infonomics, data search and discovery from an IT silo aren’t enough.

Data governance as a practice, and the solutions that power it, must be part of an organization’s culture to ensure the people and departments that use data are involved in its discovery, understanding, governance and socialization for peak performance.

So, how do you go about justifying data governance as an enterprise-wide initiative?

Justifying Data Governance – The Roadblocks

First, we must look at the shortcomings of the Data Governance 1.0 approach that are clearly reflected in the 2018 State of Data Governance Report. The lack of executive support is cited as the most common roadblock to implementing data governance at 42%, with a lack of organizational support closely following at 39%.

For data-driven enterprises, executives arguably have the biggest stake in improving DG practices. Decisions surrounding strategic direction – e.g., emerging markets to target, insights into operational efficiency, performance of marketing campaigns – are best made with accurate data.

By implementing a sound data governance initiative, data availability and context improves so employees – from executives to the front line – can make better and faster decisions. Additionally, decisions will be made with more confidence, knowing the data can be trusted. As a result, there will be fewer risks, false starts and wasted budgets on projects doomed to fail because they were based on faulty premises.

The State of DG Report also found a lack of effective tools to be another roadblock to successfully implementing data governance. This is no surprise because they weren’t built with collaboration in mind.

As mentioned, the data produced by modern society – and business – is staggering, and it permeates the whole business. Furthermore, data regulations – such as GDPR – demand that organizations understand their data lineage, being able to show who has access to what.

Governing massive volumes of data and being able to demonstrate its lineage from department to department and employee to employee fundamentally requires a collaborative approach.

Another area in which Data Governance 1.0 fell short was in articulating a business case. Of the organizations surveyed for the State of DG Report, 27% say this as a roadblock to successful data governance.

Those frustrations are understandable, as DG 1.0 wasn’t conceived for proactively adding value. But DG 2.0 has opened significant opportunities for organizations to add value, so data governance is easier to justify as a means of identifying and implementing new ideas and improvements more quickly.

For example, financial services companies stand to generate $30 billion in extra revenue through better governance of their data.

Justifying Data Governance – A New Direction

Data Governance 2.0 ploughs through the roadblocks associated with old-school DG.

It takes an enterprise-wide approach to ensure data governance really works, meaning “data owners” and “data stakeholders” are involved in the cataloging process. Everyone benefits from having access to data in context to their roles with a better grasp of its history and lineage.

Of course, regulatory compliance is the main driver for revisiting or implementing a DG initiative. However, the benefits of data governance go well beyond GDPR compliance. Better data quality, context and lineage lead to greater customer satisfaction, improved decision-making and the ability to maintain or even enhance an organization’s reputation – all mentioned as reasons to implement DG in the State of DG Report.

Indeed, understanding and governing enterprise assets has become more important to the C-suite. And DG 2.0 presupposes that CTOs in addition to CFOs, CMOs and other business executives are involved in data management on a day-to day basis. Therefore, they have to be part of the initiative and enabled to share information for agile innovation and business transformation.

It’s clear this new, proactive take on data governance is catching on. The hyper-competitive nature of data-driven business demands it – with or without the threat of GDPR penalties. Organizations reluctant or slow to adopt Data Governance 2.0 will be left behind.

To get the full State of DG Report, including survey results and insights, click here.