Tag: data governance 1.0

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Top 7 Data Governance Blog Posts of 2018

The driving factors behind data governance adoption vary.

Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent.

Historically most organizations have approached data governance in isolation and from the former category. But as data’s value to the enterprise has grown, so has the need for a holistic, collaborative means of discovering, understanding and governing data.

So with the impetus of the General Data Protection Regulation (GDPR) and the opportunities presented by data-driven transformation, many organizations are re-evaluating their data management and data governance practices.

With that in mind, we’ve compiled a list of the very best, best-practice blog posts from the erwin Experts in 2018.

Defining data governance: DG Drivers

Defining Data Governance

www.erwin.com/blog/defining-data-governance/

Data governance’s importance has become more widely understood. But for a long time, the discipline was marred with a poor reputation owed to consistent false starts, dogged implementations and underwhelming ROI.

The evolution from Data Governance 1.0 to Data Governance 2.0 has helped shake past perceptions, introducing a collaborative approach. But to ensure the collaborative take on data governance is implemented properly, an organization must settle on a common definition.

The Top 6 Benefits of Data Governance

www.erwin.com/blog/top-6-benefits-of-data-governance/

GDPR went into effect for businesses trading with the European Union, including hefty fines for noncompliance with its data collection, storage and usage standards.

But it’s important for organizations to understand that the benefits of data governance extend beyond just GDPR or compliance with any other internal or external regulations.

Data Governance Readiness: The Five Pillars

www.erwin.com/blog/data-governance-readiness/

GDPR had organizations scrambling to implement data governance initiatives by the effective date, but many still lag behind.

Enforcement and fines will increase in 2019, so an understanding of the five pillars of data governance readiness are essential: initiative sponsorship, organizational support, allocation of team resources, enterprise data management methodology and delivery capability.

Data Governance and GDPR: How the Most Comprehensive Data Regulation in the World Will Affect Your Business

www.erwin.com/blog/data-governance-and-gdpr/

Speaking of GDPR enforcement, this post breaks down how the regulation affects business.

From rules regarding active consent, data processing and the tricky “right to be forgotten” to required procedures for notifying afflicted parties of a data breach and documenting compliance, GDPR introduces a lot of complexity.

The Top Five Data Governance Use Cases and Drivers

www.erwin.com/blog/data-governance-use-cases/

An erwin-UBM study conducted in late 2017 sought to determine the biggest drivers for data governance.

In addition to compliance, top drivers turned out to be improving customer satisfaction, reputation management, analytics and Big Data.

Data Governance 2.0 for Financial Services

www.erwin.com/blog/data-governance-2-0-financial-services/

Organizations operating within the financial services industry were arguably the most prepared for GDPR, given its history. However, the huge Equifax data breach was a stark reminder that organizations still have work to do.

As well as an analysis of data governance for regulatory compliance in financial services, this article examines the value data governance can bring to these organizations – up to $30 billion could be on the table.

Understanding and Justifying Data Governance 2.0

www.erwin.com/blog/justifying-data-governance/

For some organizations, the biggest hurdle in implementing a new data governance initiative or strengthening an existing one is support from business leaders. Its value can be hard to demonstrate to those who don’t work directly with data and metadata on a daily basis.

This article examines this data governance roadblock and others in addition to advice on how to overcome them.

 

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The Data Governance (R)Evolution

Data governance continues to evolve – and quickly.

Historically, Data Governance 1.0 was siloed within IT and mainly concerned with cataloging data to support search and discovery. However, it fell short in adding value because it neglected the meaning of data assets and their relationships within the wider data landscape.

Then the push for digital transformation and Big Data created the need for DG to come out of IT’s shadows – Data Governance 2.0 was ushered in with principles designed for  modern, data-driven business. This approach acknowledged the demand for collaborative data governance, the tearing down of organizational silos, and spreading responsibilities across more roles.

But this past year we all witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Aetna. The list goes on and on. And then, the European Union’s General Data Protection Regulation (GDPR) took effect, with many organizations scrambling to become compliant.

So where are we today?

Simply put, data governance needs to be a ubiquitous part of your company’s culture. Your stakeholders encompass both IT and business users in collaborative relationships, so that makes data governance everyone’s business.

Data Governance is Everyone's Business

Data governance underpins data privacy, security and compliance. Additionally, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to grow revenue, achieve regulatory compliance, or make strategic decisions. They face a data dilemma: not knowing what data they have or where some of it is—plus integrating known data in various formats from numerous systems without a way to automate that process.

To accelerate the transformation of business-critical information into accurate and actionable insights, organizations need an automated, real-time, high-quality data pipeline. Then every stakeholder—data scientist, ETL developer, enterprise architect, business analyst, compliance officer, CDO and CEO—can fuel the desired outcomes based on reliable information.

Connecting Data Governance to Your Organization

  1. Data Mapping & Data Governance

The automated generation of the physical embodiment of data lineage—the creation, movement and transformation of transactional and operational data for harmonization and aggregation—provides the best route for enabling stakeholders to understand their data, trust it as a well-governed asset and use it effectively. Being able to quickly document lineage for a standardized, non-technical environment brings business alignment and agility to the task of building and maintaining analytics platforms.

  1. Data Modeling & Data Governance

Data modeling discovers and harvests data schema, and analyzes, represents and communicates data requirements. It synthesizes and standardizes data sources for clarity and consistency to back up governance requirements to use only controlled data. It benefits from the ability to automatically map integrated and cataloged data to and from models, where they can be stored in a central repository for re-use across the organization.

  1. Business Process Modeling & Data Governance

Business process modeling reveals the workflows, business capabilities and applications that use particular data elements. That requires that these assets be appropriately governed components of an integrated data pipeline that rests on automated data lineage and business glossary creation.

  1. Enterprise Architecture & Data Governance

Data flows and architectural diagrams within enterprise architecture benefit from the ability to automatically assess and document the current data architecture. Automatically providing and continuously maintaining business glossary ontologies and integrated data catalogs inform a key part of the governance process.

The EDGE Revolution

 By bringing together enterprise architecture, business process, data mapping and data modeling, erwin’s approach to data governance enables organizations to get a handle on how they handle their data and realize its maximum value. With the broadest set of metadata connectors and automated code generation, data mapping and cataloging tools, the erwin EDGE Platform simplifies the total data management and data governance lifecycle.

This single, integrated solution makes it possible to gather business intelligence, conduct IT audits, ensure regulatory compliance and accomplish any other organizational objective by fueling an automated, high-quality and real-time data pipeline.

The erwin EDGE creates an “enterprise data governance experience” that facilitates collaboration between both IT and the business to discover, understand and unlock the value of data both at rest and in motion.

With the erwin EDGE, data management and data governance are unified and mutually supportive of business stakeholders and IT to:

  • Discover data: Identify and integrate metadata from various data management silos.
  • Harvest data: Automate the collection of metadata from various data management silos and consolidate it into a single source.
  • Structure data: Connect physical metadata to specific business terms and definitions and reusable design standards.
  • Analyze data: Understand how data relates to the business and what attributes it has.
  • Map data flows: Identify where to integrate data and track how it moves and transforms.
  • Govern data: Develop a governance model to manage standards and policies and set best practices.
  • Socialize data: Enable stakeholders to see data in one place and in the context of their roles.

If you’ve enjoyed this latest blog series, then you’ll want to request a copy of Solving the Enterprise Data Dilemma, our new e-book that highlights how to answer the three most important data management and data governance questions: What data do we have? Where is it? And how do we get value from it?

Solving the Enterprise Data Dilemma

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Defining Data Governance: What Is Data Governance?

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.

Defining data governance: DG Drivers

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 21st 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.

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Five Pillars of Data Governance Readiness: Team Resources

The Facebook scandal has highlighted the need for organizations to understand and apply the five pillars of data governance readiness.

All eyes were on Mark Zuckerberg this week as he testified before the U.S. Senate and Congress on Facebook’s recent data drama.

A statement from Facebook indicates that the data snare was created due to permission settings leveraged by the Facebook-linked third-party app ‘thisisyourdigitallife.’

Although the method used by Cambridge Analytica to amass personal data from 87 million Facebook users didn’t constitute a “data breach,” it’s still a major data governance (DG) issue that is now creating more than a headache for the company.

The #DeleteFacebook movement is gaining momentum, not to mention the company’s stock dip.

With Facebook’s DG woes a mainstay in global news cycles, and the General Data Protection Regulation’s (GDPR) implementation just around the corner, organizations need to get DG-ready.

During the past few weeks, the erwin Expert Blog has been exploring the five pillars of data governance readiness. So far, we’ve covered initiative sponsorship and organizational support. Today, we talk team resources.

Facebook and the Data Governance Awakening

Most organizations lack the enterprise-level experience required to advance a data governance initiative.

This function may be called by another name (e.g., data management, information management, enterprise data management, etc.), a successful organization recognizes the need for managing data as an enterprise asset.

Data governance, as a foundational component of enterprise data management, would reside within such a group.

You would think an organization like Facebook would have this covered. However, it doesn’t appear that they did.

The reason Facebook is in hot water is because the platform allowed ‘thisisyourdigitallife’ to capture personal data from the Facebook friends of those who used the app, increasing the scope of the data snare by an order of magnitude.

Pillars of Data Governance; Facebook

For context, it took only 53 Australian ‘thisisyourdigitallife’ users to capture 310,000 Australian citizens’ data.

Facebook’s permission settings essentially enabled ‘thisisyourdigitallife’ users to consent on behalf of their friends. Had GDPR been in effect, Facebook would have been non-compliant.

Even so, the extent of the PR fallout demonstrates that regulatory compliance shouldn’t be the only driver for implementing data governance.

Understanding who has access to data and what that data can be used for is a key use case for data governance. This considered, it’s not difficult to imagine how a more robust DG program could have covered Facebook’s back.

Data governance is concerned with units of data – what are they used for, what are the associated risks, and what value do they have to the business? In addition, DG asks who is responsible for the data – who has access? And what is the data lineage?

It acts as the filter that makes data more discoverable to those who need it, while shutting out those without the required permissions.

The Five Pillars of Data Governance: #3 Team Resources

Data governance can’t be executed as a short-term fix. It must be an on-going, strategic initiative that the entire organization supports and is part of. But ideally, a fixed and formal data management group needs to oversee it.

As such, we consider team resources one of the key pillars of data governance readiness.

Data governance requires leadership with experience to ensure the initiative is a value-adding success, not the stifled, siloed programs associated with data governance of old (Data Governance 1.0).

Without experienced leadership, different arms of the organization will likely pull in different directions, undermining the uniformity of data that DG aims to introduce. If such experience doesn’t exist within the organization, then outside consultants should be tapped for their expertise.

As the main technical enabler of the practice, IT should be a key DG participant and even house the afore-mentioned data management group to oversee it. The key word here is “participant,” as the inclination to leave data governance to IT and IT alone has been a common reason for Data Governance 1.0’s struggles.

With good leadership, organizations can implement Data Governance 2.0: the collaborative, outcome-driven approach more suited to the data-driven business landscape. DG 2.0 avoids the pitfalls of its predecessor by expanding the practice beyond IT and traditional data stewards to make it an enterprise-wide responsibility.

By approaching data governance in this manner, organizations ensure those with a stake in data quality (e.g., anyone who uses data) are involved in its discovery, understanding, governance and socialization.

This leads to data with greater context, accuracy and trust. It also hastens decision-making and times to market, resulting in fewer bottlenecks in data analysis.

We refer to this collaborative approach to data governance as the enterprise data governance experience (EDGE).

Back to Facebook. If they had a more robust data governance program, the company could have discovered the data snare exploited by Cambridge Analytica and circumvented the entire scandal (and all its consequences).

But for data governance to be successful, organizations must consider team resources as well as enterprise data management methodology and delivery capability (we’ll cover the latter two in the coming weeks).

To determine your organization’s current state of data governance readiness, take the erwin DG RediChek.

To learn more about how to leverage data governance for GDPR compliance and an EDGE on the competition, click here.

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Understanding and Justifying Data Governance 2.0

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.

State of DG: Get the full report

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State of DG: Shocking Number of Organizations Unprepared for GDPR, Is Yours?

The General Data Protection Regulation (GDPR) goes into effect in May, but a new study reveals that most organizations are overwhelmingly unprepared.

The State of Data Governance Report finds that only 6% of respondents consider themselves completely prepared for GDPR. That means a shocking 94% of the organizations surveyed are not ready for what is one of the most important data privacy and security regulations passed in recent years.

Failure to implement data governance (DG) to comply with GDPR will leave these organizations liable for fines of up to €20 million or 4% annual global turnover – whichever is greater.

But the news isn’t all bad; promising signs can be found. Although 46% of those surveyed indicate having “no formal strategy” in place for DG, 42% describe their data governance initiatives as a “work in progress.”

State of DG: Regulatory Compliance Driving Data Governance

Historically, data governance has left a lot to be desired. The value and ROI were insignificant to non-existent, and so executive buy-in and funding also has been low.

Business leaders usually left DG to their IT departments, but that created silos that cut off DG from it’s day to day “data owners” and “data stakeholders,” – in essence, everybody that uses data to drive business. With poor data discovery, lineage and context, data governance was largely abandoned or at least out of sight, out of mind.

Forty-two percent of the organizations participating in the State of DG Report survey indicate that lack of executive support is still a roadblock. But GDPR is spurring new interest in DG because companies must articulate what their data is, where it resides, what controls are in place to protect it, and the measures they will use to address mistakes/breaches.

An effective data governance initiative is critical for the data visibility and categorization needed to comply with GDPR. It also will help assess and prioritize data risks and enable easier verification of GDPR compliance to auditors.

Perhaps this is why 66% of those surveyed for the State of DG Report say understanding and governing enterprise assets has become more important or very important for their executives. And regulatory compliance is in fact the No. 1 driver for data governance.

State of DG: Implementing Data Governance for GDPR

It’s safe to say that organizations should be much further along with GDPR than they are.

The biggest challenge is to establish compliance with their current data architectures and then to build GDPR compliance into the processes for designing and deploying new data sources.

This requires visibility into the strategic roadmap and well-defined processes to govern new data deployments so that constant GDPR retrofits aren’t required.

Thankfully data governance has evolved from a siloed, IT-owned program primarily for data cataloging to support search and discovery. It has given way to proactive, enterprise-wide data governance to support regulatory compliance in addition to data-driven insights for achieving other organizational objectives.

Data Governance 2.0 understands that CTOs, CMOs and other C-level executives and business leaders across the enterprise are involved in data creation, management and use on a day-to-day basis. And GDPR compliance requires that all stakeholders be aware and empowered so that data governance is built in, and part of the culture.

By integrating data governance with enterprise architecture, business process and data modeling, you’ll have a GDPR compliance framework to:

  • Discover and harvest data assets
  • Classify data and create a GDPR inventory
  • Perform GDPR risk analysis
  • Define GDPR controls and standard operating procedures
  • Socialize and apply GDPR requirements across the organization
  • Implement GDPR controls into IT and business roadmaps for “compliance by design”
  • Prove compliance/respond to audits

Is your organization GDPR-ready?

Click here to get your State of DG Report to see how your organization compares to those we surveyed.

Of if you’d like to discuss how to improve your GDPR readiness with one of our solution specialists, click here.

State of DG: Get the full report

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The Top 6 Benefits of Data Governance

It’s important we recognize the data governance benefits (DG) beyond General Data Protection Regulation (GDPR) compliance.

Data governance is mandatory for GDPR, so the incentive in implementing it before the May 2018 deadline is clear. However, the timeline’s pressures could also be viewed as somewhat of a double-edged sword.

On the one hand, introducing a mandate shines a spotlight on a practice many businesses have neglected. A First San Francisco Partners (FSFP) study found that only 47.9% of respondents have a DG program in place.

We are beginning to see the shift, though. The FSFP study also found that 29% of businesses are in the early stages of a DG roll-out, with an additional 19% at the research and planning stage.

The sword’s other edge is that much of this swing is reactionary, encouraged by the fast-approaching GDPR deadline.

By introducing a mandate for data governance on a timeline, many businesses will be tempted to do the bare minimum just to meet the standards for compliance.

Unfortunately, that means the following data governance benefits will be left on the table.

Data Governance

Data Governance Benefits

Better Decision-Making

One of the key benefits of data governance is better decision-making. This applies to both the decision-making process, as well as the decisions themselves.

Well-governed data is more discoverable, making it easier for the relevant parties to find useful insights. It also means decisions will be based on the right data, ensuring greater accuracy and trust.

Operational Efficiency

Data is incredibly valuable in the age of data-driven business. Therefore, it should be treated as the asset it is.

Consider a manufacturing business’ physical assets, for example. Well-run manufacturing businesses ensure their production-line machinery undergoes regular inspections, maintenance and upgrades so the line operates smoothly with limited down-time.

The same approach should apply to data.

Improved Data Understanding and Lineage

Data governance is about understanding what your data is and where it is stored. When implemented well, data governance provides a comprehensive view of all data assets.

It also provides greater accountability. By assigning permissions, it is far easier to determine who’s responsible for specific data.

Greater Data Quality

As data governance aids in discoverability, businesses with effective data governance programs also benefit from improved data quality. Although technically two separate initiatives, some of their goals overlap.

These include, but are not limited to, the standardization of data and its consistency. One way to clearly differentiate the two programs is to consider the questions posed by each field.

Data quality wants to know how useful and complete data is, whereas data governance wants to know where the data is and who is responsible for it.

Data governance improves data quality, because answering the latter makes it easier to tackle the former.

Regulatory Compliance

As mentioned in the introduction, if you haven’t yet adopted a data governance program, compliance is perhaps the best reason to do so. Hefty fines with an upper limit of €20 million or 4% or annual global turnover – whichever is greater – are nothing to baulk at.

That said, GDPR fines are only incentivising something you should already be keen to do. Data-driven businesses that aren’t enjoying the aforementioned benefits are fundamentally stifling their own performance.

It could even be argued that to be truly data-driven, data governance is a must.

Increased Revenue

Driving revenue should, in fact, be higher on the DG benefit list. However, it’s positioned here because the aforementioned benefits cumulatively influence it.

All the benefits of data governance addressed above help businesses make better, faster decisions with more certainty.

It means that less costly errors – in the form of false starts and even data breaches – are made. It means that you spend less money by managing risk, and closing the most vulnerable holes in your business’ security, instead of more money retrospectively, dealing with PR and financial fallout.

What You Need to Do

Considering the benefits and their accumulative real-term value , data-driven organizations can’t afford to leave data governance to IT alone. This is why Data Governance 1.0 has ultimately failed.

But even now, 23% of businesses in the FSFP study said information technology leads their data governance efforts.

In the current climate, this mind-set is inherently flawed. We’ve reached a new business age in which data is considered more valuable than oil. Yet many businesses are still reluctant in treating data with the same care as their physical assets.

This needs to change. If data is indeed this valuable, we need to treat data governance as a strategic initiative.

Data Governance 2.0 involves the entire enterprise, including department heads and C-level executives, who stand to benefit from data insights gained throughout the process.

For more data governance best practices and useful statistics, download our resource: Data Governance Is Everyone’s Business.

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