Tag: state of data governance report

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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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Big Data Posing Challenges? Data Governance Offers Solutions

Big Data is causing complexity for many organizations, not just because of the volume of data they’re collecting, but because of the variety of data they’re collecting.

Big Data often consists of unstructured data that streams into businesses from social media networks, internet-connected sensors, and more. But the data operations at many organizations were not designed to handle this flood of unstructured data.

Dealing with the volume, velocity and variety of Big Data is causing many organizations to re-think how they store and govern their data. A perfect example is the data warehouse. The people who built and manage the data warehouse at your organization built something that made sense to them at the time. They understood what data was stored where and why, as well how it was used by business units and applications.

The era of Big Data introduced inexpensive data lakes to some organizations’ data operations, but as vast amounts of data pour into these lakes, many IT departments found themselves managing a data swamp instead.

In a perfect world, your organization would treat Big Data like any other type of data. But, alas, the world is not perfect. In reality, practicality and human nature intervene. Many new technologies, when first adopted, are separated from the rest of the infrastructure.

“New technologies are often looked at in a vacuum, and then built in a silo,” says Danny Sandwell, director of product marketing for erwin, Inc.

That leaves many organizations with parallel collections of data: one for so-called “traditional” data and one for the Big Data.

There are a few problems with this outcome. For one, silos in IT have a long history of keeping organizations from understanding what they have, where it is, why they need it, and whether it’s of any value. They also have a tendency to increase costs because they don’t share common IT resources, leading to redundant infrastructure and complexity. Finally, silos usually mean increased risk.

But there’s another reason why parallel operations for Big Data and traditional data don’t make much sense: The users simply don’t care.

At the end of the day, your users want access to the data they need to do their jobs, and whether IT considers it Big Data, little data, or medium-sized data isn’t important. What’s most important is that the data is the right data – meaning it’s accurate, relevant and can be used to support or oppose a decision.

Reputation Management - What's Driving Data Governance

How Data Governance Turns Big Data into Just Plain Data

According to a November 2017 survey by erwin and UBM, 21 percent of respondents cited Big Data as a driver of their data governance initiatives.

In today’s data-driven world, data governance can help your business understand what data it has, how good it is, where it is, and how it’s used. The erwin/UBM survey found that 52 percent of respondents said data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) said they recognize the value of data to their organization but don’t have a formal governance strategy.

A holistic approach to data governance includes thesekey components.

  • An enterprise architecture component is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable. By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes.
  • A business process and analysis component defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible. Enterprises can clearly define, map and analyze workflows and build models to drive process improvements, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.
  • A data modeling component is the best way to design and deploy new databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

When data governance is done right, and it’s woven into the structure and architecture of your business, it helps your organization accept new technologies and the new sources of data they provide as they come along. This makes it easier to see ROI and ROO from your Big Data initiatives by managing Big Data in the same manner your organization treats all of its data – by understanding its metadata, defining its relationships, and defining its quality.

Furthermore, businesses that apply sound data governance will find themselves with a template or roadmap they can use to integrate Big Data throughout their organizations.

If your business isn’t capitalizing on the Big Data it’s collecting, then it’s throwing away dollars spent on data collection, storage and analysis. Just as bad, however, is a situation where all of that data and analysis is leading to the wrong decisions and poor business outcomes because the data isn’t properly governed.

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Data Governance Helps Build a Solid Foundation for Analytics

If your business is like many, it’s heavily invested in analytics. We’re living in a data-driven world. Data drives the recommendations we get from retailers, the coupons we get from grocers, and the decisions behind the products and services we’ll build and support at work.

None of the insights we draw from data are possible without analytics. We routinely slice, dice, measure and (try to) predict almost everything today because data is available to be analyzed. In theory, all this analysis should be helping the business. It should ensure we’re creating the right products and services, marketing them to the right people, and charging the right price. It should build a loyal base of customers who become brand ambassadors, amplifying existing marketing efforts to fuel more sales.

We hope all these things happen because all this analysis is expensive. It’s not just the cost of software licenses for the analytics software, but it’s also the people. Estimates for the average salary of data scientists, for example, can be upwards of $118,000 (Glassdoor) to $131,000 (Indeed). Many businesses also are exploring or already use next-generation analytics technology like predictive analytics or analytics supported by artificial intelligence or machine learning, which require even more investment.

If the underlying data your business is analyzing is bad, you’re throwing all this investment away. There’s a saying that scares everyone involved in analytics today: “Garbage in, garbage out.” When bad data is used to drive your strategic and operational decisions, your bad data suddenly becomes a huge problem for the business.

The goal, when it comes to the data you feed your analytics platforms, is what’s often referred to as the “single source of truth,” otherwise known as the data you can trust to analyze and create conclusions that drive your business forward.

“One source of truth means serving up consistent, high-quality data,” says Danny Sandwell, director of product marketing at erwin, Inc.

Despite all of the talk in the industry about data and analytics in recent years, many businesses still fail to reap the rewards of their analytics investments. In fact, Gartner reports that more than 60 percent of data and analytics projects fail. As with any software deployment, there are a number of reasons these projects don’t turn out the way they were planned. Among analytics, however, bad data can turn even a smooth deployment on the technology side into a disaster for the business.

What is bad data? It’s data that isn’t helping your business make the right decisions because it is:

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

More than one-quarter of the respondents to a November 2017 survey by erwin Inc. and UBM said analytics was one of the factors driving their data governance initiatives.

Reputation Management - What's Driving Data Governance

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin-UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organizations but don’t have a formal governance strategy.

Data-driven Analytics: How Important is Data Governance

When data governance helps your organization develop high-quality data with demonstrated value, your IT organizations can build better analytics platforms for the business. Data governance helps enable self-service, which is an important part of analytics for many businesses today because it puts the power of data and analysis into the hands of the people who use the data on a daily basis. A well-functioning data governance program creates that single version of the truth by helping IT organizations identify and present the right data to users and eliminate confusion about the source or quality of the data.

Data governance also enables a system of best practices, subject matter experts, and collaboration that are the hallmarks of today’s analytics-driven businesses.

Like analytics, many early attempts at instituting data governance failed to deliver the expected results. They were narrowly focused, and their advocates often had difficulty articulating the value of data governance to the organization, which made it difficult to secure budget. Some organizations even viewed data governance as part of data security, securing their data to the point where the people who wanted to use it had trouble getting access.

Issues of ownership also hurt early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve these issues of ownership because many are adopting a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy weaves itself into the business and its infrastructure. It is present in the enterprise architecture, the business processes, and it helps organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing because organizations and their data are constantly changing and transforming, so their approach to data governance needs to adjust as they go.

When it comes to analytics, data governance is the best way to ensure you’re using the right data to drive your strategic and operational decisions. It’s easier said than done, especially when you consider all the data that’s flowing into a modern organization and how you’re going to sort through it all to find the good, the bad, and the ugly. But once you do, you’re on the way to using analytics to draw conclusions you can trust.

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Why Data Governance is the Key to Better Decision-Making

The ability to quickly collect vast amounts of data, analyze it, and then use what you’ve learned to help foster better decision-making is the dream of many a business executive. But like any number of things that can be summarized in a single sentence, it’s much harder to execute on such a vision than it might first appear.

According to Forrester, 74 percent of firms say they want to be “data-driven,” but only 29 percent say they are good at connecting analytics to action. Consider this: Forrester found that business satisfaction with analytics dropped by 21 percent between 2014 and 2015 – a period of great promise and great investment in Big Data. In other words, the more data businesses were collecting and mining, the less happy they were with their analytics.

A number of factors are potentially at play here, including the analytics software, the culture of the business, and the skill sets of the people using the data. But your analytics applications and the conclusions you draw from your analysis are only as good as the data that is collected and analyzed. Collecting, safeguarding and mining large amounts of data isn’t an inexpensive exercise, and as the saying goes, “garbage in, garbage out.”

“It’s a big investment and if people don’t trust data, they won’t use things like business intelligence tools because they won’t have faith in what they tell them,” says Danny Sandwell, director of product marketing at erwin, Inc.

Using data to inform business decisions is hardly new, of course. The modern idea of market research dates back to the 1920s, and ever since businesses have collected, analyzed and drawn conclusions from information they draw from customers or prospective customers.

The difference today, as you might expect, is the amount of data and how it’s collected. Data is generated by machines large and small, by people, and by old-fashioned market research. It enters today’s businesses from all angles, at lightning speed, and can, in many cases, be available for instant analysis.

As the volume and velocity of data increases, overload becomes a potential problem. Unless the business has a strategic plan for data governance, decisions around where the data is stored, who and what can access it, and how it can be used, becomes increasingly difficult to understand.

Not every business collects massive amounts of data like Facebook and Yahoo, but recent headlines demonstrate how those companies’ inability to govern data is harming their reputations and bottom lines. For Facebook, it was the revelation that the data of 87 million users was improperly obtained to influence the 2016 U. S. presidential election. For Yahoo, the U.S. Securities and Exchange Commission (SEC) levied a $35 million fine for failure to disclose a data breach in a timely manner.

In both the Facebook and Yahoo cases, the misuse or failure to protect data was one problem. Their inability to quickly quantify the scope of the problem and disclose the details made a big issue even worse – and kept it in the headlines even longer.

The issues of data security, data privacy and data governance may not be top of mind for some business users, but these issues manifest themselves in a number of ways that affect what they do on a daily basis. Think of it this way: somewhere in all of the data your organization collects, a piece of information that can support or refute a decision you’re about to make is likely there. Can you find it? Can you trust it?

If the answer to these questions is “no,” then it won’t be easy for your organization to make data-driven decisions.

Better Decision-Making - Data Governance

Powering Better Decision-Making with Data Governance

Nearly half (45 percent) of the respondents to a November 2017 survey by erwin and UBM said better decision-making was one of the factors driving their data governance initiatives.

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin/UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organization but don’t have a formal governance strategy.

Many early attempts at instituting data governance failed to deliver results. They were narrowly focused, and their proponents often had difficulty articulating the value of data governance to the organization, making it difficult to secure budget. Some organizations even understood data governance as a type of data security, locking up data so tightly that the people who wanted to use it to foster better decision-making had trouble getting access.

Issues of ownership also stymied early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve issues of ownership, thanks in large part to a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy works a lot like data itself – it permeates the business and its infrastructure. It is part of the enterprise architecture, the business processes, and it help organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing, because organizations and their data are constantly changing and transforming, so their approach to data governance can’t sit still.

As you might expect, better visibility into your data goes a long way toward using that data to make more informed decisions. There is, however, another advantage to the visibility offered by a holistic data governance strategy: it helps you better understand what you don’t know.

By helping businesses understand the areas where they can improve their data collection, data governance helps organizations continually work to create better data, which manifests itself in real business advantages, like better decision-making and top-notch customer experiences, all of which will help grow the business.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech. This content originally appeared as a sponsored post on http://www.eweek.com/.

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The Top Five Data Governance Use Cases and Drivers

As the applications for data have grown, so too have the data governance use cases. And the legacy, IT-only approach to data governance, Data Governance 1.0, has made way for the collaborative, enterprise-wide Data Governance 2.0.

In addition to increasing data applications, Data Governance 1.0’s decline is being hastened by recurrent failings in its implementation. Leaving it to IT, with no input from the wider business, ignores the desired business outcomes and the opportunities to contribute to and speed their accomplishment. Lack of input from the departments that use the data also causes data quality and completeness to suffer.

So Data Governance 1.0 was destined to fail in yielding a significant return. But changing regulatory requirements and mega-disruptors effectively leveraging data has spawned new interest in making data governance work.

The 2018 State of Data Governance Report indicates that 98% of organizations consider data governance important. Furthermore, 66% of respondents say that understanding and governing enterprise assets has become more or very important for their executives.

Below, we consider the primary data governance use cases and drivers as outlined in this report.

The Top 5 Data Governance Use Cases

1. Changing Regulatory Requirements

Changing regulations are undoubtedly the biggest driver for data governance. The European Union’s General Data Protection Regulation (GDPR) will soon take effect, and it’s the first attempt at a near-global, uniform approach to regulating the way organizations use and store data.

Data governance is mandatory under the new law, and failure to comply will leave organizations liable for huge fines – up to €20 million or 4% of the company’s global annual turnover. For context, GDPR fines could wipe off two percentage points of revenue from Google parent company, Alphabet.

Although 60% of the organizations surveyed for the State of DG Report indicate that regulatory compliance is the key driver for implementing data governance, only 6% of enterprises are prepared for GDPR with less than four months to go.

But data governance use cases go beyond just compliance.

2. Customer Satisfaction

Another primary driver for data governance is improving customer satisfaction, with 49% of our survey respondents citing it.

A Data Governance 2.0 approach is paramount to this use case and should be strong justification to secure C-level buy-in. In fact, the correlation between effective data governance and customer satisfaction is clear. A 2017 report from Aberdeen Group shows that the user-base of organizations with more effective data governance programs are far happier with:

  • The business’ ability to share data (66% – Data Governance Leaders vs. 21% Data Governance followers)
  • Data systems’ ease of use (64% vs. 24%)
  • Speed of information delivery (61% vs. 18%)

3. Decision-Making

Another data governance use case as indicated by the State of DG Report is improved decision-making. Forty-five percent of respondents identify it as the third key driver, and for good reason.

Data governance success manifests itself as well-defined data that is consistent throughout the business, understood across departments, and used to pull the business in the desired direction. It also improves the quality of the data.

By moving data governance out of its IT silo, the employees responsible for business outcomes are part of its governance. This collaboration makes data both more discoverable, more insightful and more contextual.

The decision-making process becomes more efficient, as the velocity at which data can be interpreted increases. The organization can also better interpret and trust the information it is using to determine course.

4. Reputation Management

In the survey behind the State of DG Report, 30% of respondents name reputation management as a driver for DG’s implementation.

We’ve seen it time and time again with high-profile data breaches inflicting the likes of Equifax, Uber and Yahoo. All were met with costly PR fallout. For example, Equifax’s breach had a price tag of $90 million, as of November 2017.

So the discrepancy between the 60% who cite regulatory compliance as a key driver and the 30% who cite reputation management as DG drivers is interesting. One could argue they are the same; both call for data governance to help prevent or at least limit damaging breaches.

The difference might come down to smaller businesses that believe they have less brand equity to maintain. They, as well as some of their larger counterparts, have taken a reactionary approach to data governance. But GDPR should now encourage more proactive data governance across the board.

In terms of data governance use cases for managing the risk of data breaches, consider that data governance, at a fundamental level, is about knowing where your data is, who’s responsible for it, and what it is supposed to be used for.

This understanding enables organizations to focus security spending on the areas of highest risk. Thus, they can take a more cost-effective but thorough approach to risk management.

5. Analytics and Big Data

Analytics and Big Data also were identified as key drivers for data governance among 27% and 20% of respondents, respectively.

The need for data governance in these cases is largely driven by the amount of data businesses are now tasked with overseeing. In terms of volume, Big Data speaks for itself. Twenty-two percent of respondents in the State of DG Report manage more than 10 petabytes of data, which lines up closely with those who identify Big Data as a key driver.

However, the amount of data the average organization without a Big Data strategy consumes, stores and processes has climbed considerably in recent years.

Research indicates that 90% of the world’s data has been created just in the last two years. Globally, we generate 2.5 quintillion bytes a day. Other studies equate data’s value to that of oil, so clearly there’s a lot of value to be found.

However, the “three Vs of data” (volume, velocity, variety) tend to be positively correlated. When one increases, so do the other two. Higher volumes of data mean higher velocities of data that must be processed faster for worthwhile, valuable insights. It also means an increase in the data types – both structured and unstructured – which makes processing more difficult.

A Strong DG Foundation

A strong data governance foundation ensures data is more manageable, and therefore more valuable.

With Data Governance 2.0, data governance use cases shift from reactionary to proactive with a clear focus on business outcomes.

Although new regulations can be seen as bureaucratic and cumbersome, GDPR actually presents organizations with great opportunity – at least for those that choose to take the evolved Data Governance 2.0 path. They will benefit from an outcome-focused DG initiative that adds value beyond just regulatory compliance.

To learn more, download the complete State of Data Governance Report.

2020 Data Governance and Automation Report