Tag: data governance best practices

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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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Why Data Governance Leads to Data-Driven Success

Searching for new ways to generate value and improve execution, organizations of all shapes and sizes are racing to embrace data-driven approaches that are enabled by advances in analytics.

A perfect storm of events that started in the mid-2000s has morphed into a disruptive force in the economy at an accelerating pace. Data-driven analytics has gained mainstream business adoption. Advances in communications, geo-positioning systems, sensors and computing technologies have combined with the rise of social media and the incredible growth of available data sources.

Leadership teams in the boardroom have become acutely aware of the potential opportunities available for driving innovation and growth.

Although opportunities are significant, many challenges exist that make it difficult to successfully adopt data-driven approaches.

We’re going to explore the rationale for becoming data-driven, how to frame success, and some of the critical building blocks required, including data governance.

Framing Data-Driven Success

Organizational impact helps us frame the concept of data-driven success. Impact is related to an outcome. An impact describes a changed condition in measurable terms. A well-defined impact is a proxy for value.

Stating that you want to “move the needle,” implies that the area of impact can be measured with a metric that represents that needle. By achieving impact in the right business area, incremental value is created.

When investments are considered for implementing new data-driven approaches, it’s essential to define the desired areas of impact. Evidence of impact requires knowledge of the condition before and after the data-driven approach has been implemented.

Areas of impact can be tangible or intangible. They might be difficult to measure, but measurement strategies can be developed that measure most areas of impact. It’s important to frame the desired area of impact against the feasibility of gathering useful measurements.

Examples of measurable impact:

  • Increase process efficiency by 5%
  • Reduce product defects by 15%
  • Increase profit margin by 10%
  • Reduce customer attrition by 15%
  • Increase customer loyalty by 20%

Impact measures relative changes in performance over time. The changes are directly related to incremental value creation. Impact can be defined and managed by organizations from all sectors of the economy. Areas of impact are linked to their mission, vision and definition of success.

Data-driven excellence describes the performance that exists when targeted areas of impact are successfully enabled by data-driven approaches.

Building Blocks of Data-Driven Approaches

Successfully becoming data-driven requires that desired impacts are related to and supported by four categories of building blocks.

Data-Driven Building Blocks

The first category describes the business activities that must be created or modified to drive the desired impact. These are called the “business building blocks.”

The second category describes the new information and insights required by the business building blocks based on analytic methods that enable smarter business activities. These are called the “analytics building blocks.”

The third category describes the relevant data to be acquired and delivered to the analytics methods that generate the new information and insights. These are called the “data building blocks.”

Success at an organizational level requires that all critical building blocks are aligned with shared objectives and approaches that ensure cohesion and policy compliance. This responsibility is provided by the fourth category called the “governance building blocks.”

The four categories form a layered model that describes their dependencies. Value-creating impact depends on business activities, which depends on analytics, which depends on data, which depends on governance.

The Governance Imperative

Data-driven approaches touch many areas of the organization. Key touch points are located where:

  • Data is acquired and managed
  • Insights are created and consumed
  • Decision-making is enabled
  • Resulting actions are carried out
  • Results are monitored using feedback

Governance at a broad level develops the policies and standards needed across all touch points to generate value.  As a form of leadership, governance sets policies, defines objectives and assigns accountabilities across the business, analytics and data building blocks.

Business activity governance ensures that proactive management and employee teams respond to new sources of information and change their behaviors accordingly. Policies related to process standards, human skill development, compensation levels and incentives make up the scope of business activity governance.

Analytics governance ensures that all digital assets and activities that generate insights and information using analytics methods actually enable smarter business activities. Policies related to information relevance, security, visualization, data literacy, analytics model calibration and lifecycle management are key areas of focus.

Data governance is focussed on the data building blocks. Effective data governance brings together diverse groups and departments to enable the data-driven capabilities needed to achieve success. Data governance defines accountabilities, policies and responsibilities needed to ensure that data sets are managed as true corporate assets.

This implies that governed data sets are identified, described, cataloged, secured and provisioned to support all appropriate analytics and information use cases required to enable the analytics methods. Data quality and integration are also within the scope of data governance.

Foundation for Success

Companies that are successful with data-driven approaches can rapidly identify and implement new ideas and analytics use cases. This helps them compete, innovate and generate new levels of value for their stakeholders on a sustainable basis.

Data governance provides the foundation for this success. Effective data governance ensures that data is managed as a true corporate asset. This means that it can be used and re-purposed on an on-going basis to support new and existing ideas generated by the organization as it matures and broadens its data-driven capabilities.

As organizations unlock more value by creating a wider analytics footprint, data governance provides the foundation necessary to support their journey.

The next post in this blog series dives deeper into data governance in terms of scope options, organization approaches, objectives, structures and processes. It provides perspectives on how a well-designed data governance program directly supports the desired data-driven approaches that ultimately drive key areas of business impact.

Data governance is everyone's business

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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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The Secret to Data Governance Success

Data governance (DG) 1.0 has struggled to get off the ground, but now DG is required for General Data Protection Regulation (GDPR) compliance, so businesses need a new approach to achieve data governance success.

When properly implemented, data governance is an empowering tool for businesses. But for many organizations just getting started with DG, implementation will be reactionary because of its mandatory status under (GDPR).

As such, businesses might be tempted into doing the bare minimum to meet compliance standards. But done right, data governance is a key enabler for any data-driven business.

The data governance success story

The first step in achieving data governance success is to define what it should look like. With clear goals, businesses can take the collaborative approach data governance requires – with the whole company pulling in the same direction – for proper implementation.

Data governance success typically manifests itself as:

  • Defined data: Consistency in how a business defines data means it can be understood across divisions, enabling greater potential for collaboration.
  • Guaranteed quality: Trusted data eases the decision-making process, allowing a business to make both faster and more assured decisions that lead to fewer false starts.
  • Compliance and security: With data governance, neither are sacrificed even as the volume of data and the accessibility of such data expands when silos are broken down. Of course, this is a key component of any business putting data at the heart of their operations.

With this in mind, your next steps should be to introduce Data Governance 2.0 by addressing the baggage of its predecessor, and learning from it. Two key lessons to take away: 1) treat data like physical assets and 2) treat data governance itself as a strategic initiative.

Treat data like physical assets

This year data went mainstream. In the two years prior, more data was created than in the whole of human history. With more and more businesses acknowledging the value of data insights, analysts correctly predicted that data would be considered “more valuable than oil” in 2017.

Businesses that have already experienced data-driven success recognized data’s potential value early on. Yet for the most part, data typically has been considered separate from physical assets. It has, therefore, been given subdued levels of vigilance compared to physical assets that are often tracked, maintained and updated to maintain peak operational performance.

Take the belt on a production line, for example. Lack of maintenance leads to faults, production delays, increased time to market and ultimately stifled profits and overall performance. Continuous neglect results in more costly repairs not to mention the costs related to down-time. The same is true for data.

If your data isn’t governed with due care, silos and bottlenecks easily develop, shutting off access to employees who need it and slowing down everything from data discovery to analytics.

Persistent neglect means your business will not understand where your most sensitive data is stored, making it more susceptible to breaches. As Equifax and Uber have demonstrated recently, such data breaches are costly enough without the fines that soon will be levied because of  GDPR.

Considering recent revelations surrounding the value of data, plus the imminent regulatory changes, it’s time businesses begin treating data with as much respect and care as their physical assets.

Treat data governance as a strategic initiative

The problem with historical data governance implementation is that it was seen exclusively as an IT-driven project. Therefore, governance was shoehorned through a collection of siloed tools with no input from the wider organization. More specifically, from line managers and C-Level executives to whom governed data is arguably most valuable.

In recent years, the problems with this approach have become further exacerbated by:

  • A demand for big data and analytics-driven growth
  • A need for digital trust in business dealings between organizations or between businesses and consumers
  • Upcoming personal data removal mandates with stronger individual privacy protections

In the current business climate, more than 35 percent of companies use information to identify new business opportunities and predict future trends and behavior. An additional 50 percent agree that information is highly valued for decision-making, and should be treated as an asset (BI-Survey.com).

Clearly, it’s paramount that organizations view their data as a valuable asset, and the governing of their data as a strategic initiative in and of itself.

For more best practices in achieving data governance success, click here.

Data governance is everyone's business