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The Role of An Effective Data Governance Initiative in Customer Purchase Decisions

A data governance initiative will maximize the security, quality and value of data, all of which build customer trust.

Without data, modern business would cease to function. Data helps guide decisions about products and services, makes it easier to identify customers, and serves as the foundation for everything businesses do today. The problem for many organizations is that data enters from any number of angles and gets stored in different places by different people and different applications.

Getting the most out of your data requires that you know what you have, where you have it, and that you understand its quality and value to the organization. This is where data governance comes into play. You can’t optimize your data if it’s scattered across different silos and lurking in various applications.

For about 150 years, manufacturers relied on their machinery and its ability to run reliably, properly and safely, to keep customers happy and revenue flowing. A data governance initiative has a similar role today, except its aim is to maximize the security, quality and value of data instead of machinery.

Customers are increasingly concerned about the safety and privacy of their data. According to a survey by Research+Data Insights, 85 percent of respondents worry about technology compromising their personal privacy. In a survey of 2,000 U.S. adults in 2016, researchers from Vanson Bourne found that 76 percent of respondents said they would move away from companies with a high record of data breaches.

For years, buying decisions were driven mainly by cost and quality, says Danny Sandwell, director of product marketing at erwin, Inc. But today’s businesses must consider their reputations in terms of both cost/quality and how well they protect their customers’ data when trying to win business.

Once the reputation is tarnished because of a breach or misuse of data, customers will question those relationships.

Unfortunately for consumers, examples of companies failing to properly govern their data aren’t difficult to find. Look no further than Under Armour, which announced this spring that 150 million accounts at its MyFitnessPal diet and exercise tracking app were breached, and Facebook, where the data of millions of users was harvested by third parties hoping to influence the 2016 presidential election in the United States.

Customers Hate Breaches, But They Love Data

While consumers are quick to report concerns about data privacy, customers also yearn for (and increasingly expect) efficient, personalized and relevant experiences when they interact with businesses. These experiences are, of course, built on data.

In this area, customers and businesses are on the same page. Businesses want to collect data that helps them build the omnichannel, 360-degree customer views that make their customers happy.

These experiences allow businesses to connect with their customers and demonstrate how well they understand them and know their preferences, like and dislikes – essentially taking the personalized service of the neighborhood market to the internet.

The only way to manage that effectively at scale is to properly govern your data.

Delivering personalized service is also valuable to businesses because it helps turn customers into brand ambassadors, and it’s a fact that it’s much easier to build on existing customer relationships than to find new customers.

Here’s the upshot: If your organization is doing data governance right, it’s helping create happy, loyal customers, while at the same time avoiding the bad press and financial penalties associated with poor data practices.

Putting A Data Governance Initiative Into Action

The good news is that 76 percent of respondents to a November 2017 survey we conducted with UBM said understanding and governing the data assets in the organization was either important or very important to the executives in their organization. Nearly half (49 percent) of respondents said that customer trust/satisfaction was driving their data governance initiatives.

Importance of a data governance initiative

What stops organizations from creating an effective data governance initiative? At some businesses, it’s a cultural issue. Both the business and IT sides of the organization play important roles in data, with the IT side storing and protecting it, and the business side consuming data and analyzing it.

For years, however, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. Our study found signs this is changing. More than half (57 percent) of the respondents said both and IT and the business/corporate teams were responsible for data in their organization.

Who's responsible for a data governance initiative

Once an organization understands that IT and the business are both responsible for data, it still needs to develop a comprehensive, holistic strategy for data governance that is capable of:

  • Reaching every stakeholder in the process
  • Providing a platform for understanding and governing trusted data assets
  • Delivering the greatest benefit from data wherever it lives, while minimizing risk
  • Helping users understand the impact of changes made to a specific data element across the enterprise.

To accomplish this, a modern data governance initiative needs to be interdisciplinary. It should include not only data governance, which is ongoing because organizations are constantly changing and transforming, but other disciples as well.

Enterprise architecture 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 is also vital to modern data governance. It 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 improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Finally, data modeling remains the best way to design and deploy new relational 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.

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

Read the previous post on how compliance concerns and the EU’s GDPR are driving businesses to implement data governance.

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

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

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

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

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

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

Accurate Executive Business Intelligence Reporting Requires Data Governance

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

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

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

Data Discovery Fire Drill: Executive Business Intelligence

Why is data governance important?

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

Why is data lineage important?

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

Data Governance Enables Data-Driven Business

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

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

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

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

Data governance is everyone's business

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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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GDPR, Compliance Concerns Driving Data Governance Strategies

There are many factors driving data governance adoption, as revealed in erwin’s State of Data Governance Report. Over the coming weeks, we’ll be exploring them in detail, starting with regulatory compliance.

By Michael Pastore

Almost every organization views data governance as important, so why don’t they all have it in place?

Modern organizations run on data. Whether from sensors monitoring equipment on a factory floor or a customer’s purchasing history, data enters modern businesses from every angle, gets stored in any number of places, and is used by many different people and applications.

Data governance refers to the practices that help businesses understand where their data comes from, where it resides, how accurate it is, who or what can access it, and how it can be used. The idea of data governance is not new, but putting data governance into practice and reaping the benefits remains a struggle for many organizations.

According to our November 2017 survey with UBM, nearly all (98 percent) respondents said their organizations view data governance as either important or critically important from a business perspective. Despite this, 46 percent of respondents indicated their organizations recognize the value of data, but lack a formal governance strategy.

One of the significant obstacles to data governance for many organizations is the idea of ownership. In many businesses, it’s safe to say that the IT organization has ownership over the network, just as it’s easy to say that the business oversees payroll.

Data is a bit more complicated. The business side of the organization often analyzes the data, but it’s the IT organization that stores and protects it. This data division of labor often leaves data governance in a sort of no-man’s land, with each side expecting the other to pick up the torch.

The results of the erwin-UBM survey indicate that businesses are increasingly treating data governance as an enterprise-wide imperative. At 57 percent of respondents’ organizations, both IT and the business are responsible for data governance. Just 34 percent of the organizations put IT solely in charge.

Strong data governance initiatives will overcome the issue of ownership thanks in part to a new organizational structure that considers the importance of data. The emergence of the chief data officer (CDO) is one sign that businesses recognize the vital role of their data.

Many of the first generation of CDOs reported to the CIO. Now, you’re more likely to see the CDO at forward-thinking organizations sit on the business side, perhaps in the finance department, or even marketing, which is a huge consumer of data in many businesses. Under the CDO, it’s increasingly likely to find a data protection officer (DPO) tasked with overseeing how the business safeguards its information.

What's Driving Data Governance

Driving Data Governance: Compliance Is Leading Organizations to Data Governance

Now is a good time for businesses to re-think their data structure and governance initiatives. Data is central to organizations’ compliance, privacy and security initiatives because it has value — value to the business; value to the customer; and, like anything of value, value to criminals who want to get their hands on it.

The need to protect data and reduce risk is an important factor in driving data governance at many organizations. In fact, our survey found that regulatory compliance, cited by 60 percent of respondents, was the most popular factor driving data governance.

There’s an increased sense of urgency regarding data governance and compliance because of the European Union’s General Data Protection Regulation (GDPR), which goes into effect this month. According to our research, only 6 percent of respondents said their organization was “completely prepared” for the regulation.

Not only does the GDPR protect EU citizens at home, but it extends protections to EU citizens wherever they do business. It really goes much farther than any other legislation ever has.

The GDPR essentially gives rights to the people the data represents, so businesses must:

  • Minimize identifiability in data
  • Report data breaches within 72 hours
  • Give consumers the ability to dispute data and demand data portability
  • Understand the GDPR’s expanded definition of personally identifiable information (PII)
  • Extend to consumers the right to be “forgotten”

And much, much more.

The maximum fine for organizations in breach of the GDPR is up to 4 percent of annual global turnover or €20 million, whichever is greater. And because the GDPR will apply to anyone doing business with EU citizens, and the internet transcends international borders, it’s likely the GDPR will become the standard organizations around the world will need to rise to meet.

The GDPR is a hot topic right now, but it’s not the only data-security regulation organizations have to honor. In addition to Payment Card Industry (PCI) standards for payment processors, industry-specific regulations exist in such areas as financial services, healthcare and education.

This web of regulations brings us back to data governance. Simply put, it’s easier to protect data and mitigate a breach if your organization knows where the data comes from, where it is stored, and what it includes.

Businesses stand to gain a number of advantages by implementing strong data governance. Regulatory compliance is sure to get the attention of C-level executives, the legal team and the board, but it means very little to consumers – until there’s a breach.

With new breaches being reported on a seemingly daily basis, businesses that practice strong data governance can help build a competitive advantage by better protecting their data and gaining a reputation as an organization that can be trusted in a way that firms suffering from high-profile breaches cannot. In this way, data governance helps contribute directly to the bottom line.

Still, compliance is the No. 1 factor driving data governance initiatives for a reason.

Using data governance to drive upside growth is great, but not if you’re going to lose money in fines.

In our next post in this series, we’ll explore how your organization can use data governance to build trust with your customers.

 

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

Learn more about how data governance can help with GDPR compliance by downloading the free white paper: GDPR and Your Business: A Call to Enhance Data Governance Expertise.

Data Governance and GDPR: GDPR and Your Business Whitepaper

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Five Pillars of Data Governance Readiness: Delivery Capability

The five pillars of data governance readiness should be the starting point for implementing or revamping any DG initiative.

In a recent CSO Magazine article, “Why data governance should be corporate policy,” the author states: “Data is like water, and water is a fundamental resource for life, so data is an essential resource for the business. Data governance ensures this resource is protected and managed correctly enabling us to meet our customer’s expectations.”

Over the past few weeks, we’ve been exploring the five pillars of data governance (DG) readiness, and this week we turn our attention to the fifth and final pillar, delivery capability.

Together, the five pillars of data governance readiness work as a step-by-step guide to a successful DG implementation and ongoing initiative.

As a refresher, the first four pillars are:

  1. The starting point is garnering initiative sponsorship from executives, before fostering support from the wider organization.

 

  1. Organizations should then appoint a dedicated team to oversee and manage the initiative. Although DG is an organization-wide strategic initiative, it needs experience and leadership to guide it.

 

  1. Once the above pillars are accounted for, the next step is to understand how data governance fits with the wider data management suite so that all components of a data strategy work together for maximum benefits.

 

  1. And then enterprise data management methodology as a plan of action to assemble the necessary tools.

Once you’ve completed these steps, how do you go about picking the right solution for enterprise-wide data governance?

Five Pillars of Data Governance: Delivery Capability – What’s the Right Solution?

Many organizations don’t think about enterprise data governance technologies when they begin a data governance initiative. They believe that using some general-purpose tool suite like those from Microsoft can support their DG initiative. That’s simply not the case.

Selecting the proper data governance solution should be part of developing the data governance initiative’s technical requirements. However, the first thing to understand is that the “right” solution is subjective.

Data stewards work with metadata rather than data 80 percent of the time. As a result, successful and sustainable data governance initiatives are supported by a full-scale, enterprise-grade metadata management tool.

Additionally, many organizations haven’t implemented data quality products when they begin a DG initiative. Product selections, including those for data quality management, should be based on the organization’s business goals, its current state of data quality and enterprise data management, and best practices as promoted by the data quality management team.

If your organization doesn’t have an existing data quality management product, a data governance initiative can support the need for data quality and the eventual evaluation and selection of the proper data quality management product.

Enterprise data modeling is also important. A component of enterprise data architecture, it’s an enabling force in the performance of data management and successful data governance. Having the capability to manage data architecture and data modeling with the optimal products can have a positive effect on DG by providing the initiative architectural support for the policies, practices, standards and processes that data governance creates.

Finally, and perhaps most important, the lack of a formal data governance team/unit has been cited as a leading cause of DG failure. Having the capability to manage all data governance and data stewardship activities has a positive effect.

Shopping for Data Governance Technology

DG is part of a larger data puzzle. Although it’s a key enabler of data-driven business, it’s only effective in the context of the data management suite in which it belongs.

Therefore when shopping for a data governance solution, organizations should look for DG tools that unify critical data governance domains, leverage role-appropriate interfaces to bring together stakeholders and processes to support a culture committed to acknowledging data as the mission-critical asset that it is, and orchestrate the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

Data Governance Readiness: Delivery Capability

Here’s an initial checklist of questions to ask in your evaluation of a DG solution. Does it support:

  • Relational, unstructured, on-premise and cloud data?
  • Business-friendly environment to build business glossaries with taxonomies of data standards?
  • Unified capabilities to integrate business glossaries, data dictionaries and reference data, data quality metrics, business rules and data usage policies?
  • Regulating data and managing data collaboration through assigned roles, business rules and responsibilities, and defined governance processes and workflows?
  • Viewing data dashboards, KPIs and more via configurable role-based interfaces?
  • Providing key integrations with enterprise architecture, business process modeling/management and data modeling?
  • A SaaS model for rapid deployment and low TCO?

To assess your data governance readiness, especially with the General Data Protection Regulation about to take effect, click here.

You also can try erwin DG for free. Click here to start your free trial.

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A New Wave in Application Development

Application development is new again.

The ever-changing business landscape – fueled by digital transformation initiatives indiscriminate of industry – demands businesses deliver innovative customer – and partner – facing solutions, not just tactical apps to support internal functions.

Therefore, application developers are playing an increasingly important role in achieving business goals. The financial services sector is a notable example, with companies like JPMorgan Chase spending millions on emerging fintech like online and mobile tools for opening accounts and completing transactions, real-time stock portfolio values, and electronic trading and cash management services.

But businesses are finding that creating market-differentiating applications to improve the customer experience, and subsequently customer satisfaction, requires some significant adjustments. For example, using non-relational database technologies, building another level of development expertise, and driving optimal data performance will be on their agendas.

Of course, all of this must be done with a focus on data governance – backed by data modeling – as the guiding principle for accurate, real-time analytics and business intelligence (BI).

Evolving Application Development Requirements

The development organization must identify which systems, processes and even jobs must evolve to meet demand. The factors it will consider include agile development, skills transformation and faster querying.

Rapid delivery is the rule, with products released in usable increments in sprints as part of ongoing, iterative development. Developers can move from conceptual models for defining high-level requirements to creating low-level physical data models to be incorporated directly into the application logic. This route facilitates dynamic change support to drive speedy baselining, fast-track sprint development cycles and quick application scaling. Logical modeling then follows.

Application Development

Agile application development usually goes hand in hand with using NoSQL databases, so developers can take advantage of more pliable data models. This technology has more dynamic and flexible schema design than relational databases and supports whatever data types and query options an application requires, processing efficiency, and scalability and performance suiting Big Data and new-age apps’ real-time requirements. However, NoSQL skills aren’t widespread so specific tools for modeling unstructured data in NoSQL databases can help staff used to RDBMS ramp up.

Finally, the shift to agile development and NoSQL technology as part of more complex data architectures is driving another shift. Storage-optimized models are moving to the backlines because a new format is available to support real-time app development. It is one that understands what’s being asked of the data and enables schemes to be structured to support application data access requirements for speedy responses to complex queries.

The NoSQL Paradigm

erwin DM NoSQL takes into account all the requirements for the new application development era. In addition to its modeling tools, the solution includes patent-pending Query-Optimized ModelingTM that replaces storage-optimized modeling, giving users guidance to build schemas for optimal performance for NoSQL applications.

erwin DM NoSQL also embraces an “any-squared” approach to data management, so “any data” from “anywhere” can be visualized for greater understanding. And the solution now supports the Couchbase Data Platform in addition to MongoDB. Used in conjunction with erwin DG, businesses also can be assured that agility, speed and flexibility will not take precedence over the equally important need to stringently manage data.

With all this in place, enterprises will be positioned to deliver unique, real-time and responsive apps to enhance the customer experience and support new digital-transformation opportunities. At the same time, they’ll be able to preserve and extend the work they’ve already done in terms of maintaining well-governed data assets.

For more information about how to realize value from app development in the age of digital transformation with the help of data modeling and data governance, you can download our new e-book: Application Development Is New Again.

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Pillars of Data Governance Readiness: Enterprise Data Management Methodology

Facebook’s data woes continue to dominate the headlines and further highlight the importance of having an enterprise-wide view of data assets. The high-profile case is somewhat different than other prominent data scandals as it wasn’t a “breach,” per se. But questions of negligence persist, and in all cases, data governance is an issue.

This week, the Wall Street Journal ran a story titled “Companies Should Beware Public’s Rising Anxiety Over Data.” It discusses an IBM poll of 10,000 consumers in which 78% of U.S. respondents say a company’s ability to keep their data private is extremely important, yet only 20% completely trust organizations they interact with to maintain data privacy. In fact, 60% indicate they’re more concerned about cybersecurity than a potential war.

The piece concludes with a clear lesson for CIOs: “they must make data governance and compliance with regulations such as the EU’s General Data Protection Regulation [GDPR] an even greater priority, keeping track of data and making sure that the corporation has the ability to monitor its use, and should the need arise, delete it.”

With a more thorough data governance initiative and a better understanding of data assets, their lineage and useful shelf-life, and the privileges behind their access, Facebook likely could have gotten ahead of the problem and quelled it before it became an issue.  Sometimes erasure is the best approach if the reward from keeping data onboard is outweighed by the risk.

But perhaps Facebook is lucky the issue arose when it did. Once the GDPR goes into effect, this type of data snare would make the company non-compliant, as the regulation requires direct consent from the data owner (as well as notification within 72 hours if there is an actual breach).

Five Pillars of DG: Enterprise Data Management Methodology

Considering GDPR, as well as the gargantuan PR fallout and governmental inquiries Facebook faced, companies can’t afford such data governance mistakes.

During the past few weeks, we’ve been exploring each of the five pillars of data governance readiness in detail and how they come together to provide a full view of an organization’s data assets. In this blog, we’ll look at enterprise data management methodology as the fourth key pillar.

Enterprise Data Management in Four Steps

Enterprise data management methodology addresses the need for data governance within the wider data management suite, with all components and solutions working together for maximum benefits.

A successful data governance initiative should both improve a business’ understanding of data lineage/history and install a working system of permissions to prevent access by the wrong people. On the flip side, successful data governance makes data more discoverable, with better context so the right people can make better use of it.

This is the nature of Data Governance 2.0 – helping organizations better understand their data assets and making them easier to manage and capitalize on – and it succeeds where Data Governance 1.0 stumbled.

Enterprise Data Management: So where do you start?

  1. Metadata management provides the organization with the contextual information concerning its data assets. Without it, data governance essentially runs blind.

The value of metadata management is the ability to govern common and reference data used across the organization with cross-departmental standards and definitions, allowing data sharing and reuse, reducing data redundancy and storage, avoiding data errors due to incorrect choices or duplications, and supporting data quality and analytics capabilities.

  1. Your organization also needs to understand enterprise data architecture and enterprise data modeling. Without it, enterprise data governance will be hard to support

Enterprise data architecture supports data governance through concepts such as data movement, data transformation and data integration – since data governance develops policies and standards for these activities.

Data modeling, a vital component of data architecture, is also critical to data governance. By providing insights into the use cases satisfied by the data, organizations can do a better job of proactively analyzing the required shelf-life and better measure the risk/reward of keeping that data around.

Data stewards serve as SMEs in the development and refinement of data models and assist in the creation of data standards that are represented by data models. These artifacts allow your organization to achieve its business goals using enterprise data architecture.

  1. Let’s face it, most organizations implement data governance because they want high quality data. Enterprise data governance is foundational for the success of data quality management.

Data governance supports data quality efforts through the development of standard policies, practices, data standards, common definitions, etc. Data stewards implement these data standards and policies, supporting the data quality professionals.

These standards, policies, and practices lead to effective and sustainable data governance.

  1. Finally, without business intelligence (BI) and analytics, data governance will not add any value. The value of data governance to BI and analytics is the ability to govern data from its sources to destinations in warehouses/marts, define standards for data across those stages, and promote common algorithms and calculations where appropriate. These benefits allow the organization to achieve its business goals with BI and analytics.

Gaining an EDGE on the Competition

Old-school data governance is one-sided, mainly concerned with cataloging data to support search and discovery. The lack of short-term value here often caused executive support to dwindle, so the task of DG was siloed within IT.

These issues are circumvented by using the collaborative Data Governance 2.0 approach, spreading the responsibility of DG among those who use the data. This means that data assets are recorded with more context and are of greater use to an organization.

It also means executive-level employees are more aware of data governance working as they’re involved in it, as well as seeing the extra revenue potential in optimizing data analysis streams and the resulting improvements to times to market.

We refer to this enterprise-wide, collaborative, 2.0 take on data governance as the enterprise data governance experience (EDGE). But organizational collaboration aside, the real EDGE is arguably the collaboration it facilitates between solutions. The EDGE platform recognizes the fundamental reliance data governance has on the enterprise data management methodology suite and unifies them.

By existing on one platform, and sharing one repository, organizations can guarantee their data is uniform across the organization, regardless of department.

Additionally, it drastically improves workflows by allowing for real-time updates across the platform. For example, a change to a term in the data dictionary (data governance) will be automatically reflected in all connected data models (data modeling).

Further, the EDGE integrates enterprise architecture to define application capabilities and interdependencies within the context of their connection to enterprise strategy, enabling technology investments to be prioritized in line with business goals.

Business process also is included so enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Essentially, it’s the approach data governance needs to become a value-adding strategic initiative instead of an isolated effort that peters out.

To learn more about enterprise data management and getting an EDGE on GDPR and the competition, click here.

To assess your data governance readiness ahead of the GDPR, 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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Five Pillars of Data Governance Readiness: Organizational Support

It’s important that business leaders foster organizational support for their data governance efforts.

The clock is counting down to the May 25 effective date for the General Data Protection Regulation (GDPR). With the deadline just a stone’s throw away, organizations need to ensure they are data governance-ready.

We’re continuing our blog series on the Five Pillars of Data Governance (DG). Today, we’ll explore the second pillar of data governance, organizational support, and why it’s essential to ensuring DG success.

In the modern, data-driven business world, data is an organization’s most valuable asset, and successful organizations treat it as such. In this respect, we can see data governance as a form of asset maintenance.

Take a production line in a manufacturing facility, for example. Organizations understand that equipment maintenance is an important and on-going process. They require employees using the equipment to be properly trained, ensuring it is clean, safe and working accordingly with no misuse.

They do this because they know that maintenance can prevent, or at the very least postpone repair that can be costly and lead to lost revenue during downtime.

Organizational Support: Production Lines of Information

Data Governance: Organizational Support

Despite the intangible nature of data, the same ideas for maintaining physical assets can and should be applied. After all, data-driven businesses are essentially data production lines of information. Data is created and moved through the pipeline/organization, eventually driving revenue.

In that respect – as with machinery on a production line and those who use it – everybody that uses data should be involved in maintaining and governing it.

Poor data governance leads to similar problems as poor maintenance of a production line. If it’s not well-kept, the fallout can permeate throughout the whole business.

If a DG initiative is failing, data discovery becomes more difficult, slowing down data’s journey through the pipeline.

Inconsistencies in a business glossary lead to data units with poor or no context. This in turn leads to data units that the relevant users don’t know how to put together to create information worth using.

Additionally, and perhaps most damning, if an organization has poorly managed systems of permissions, the wrong people can access data. This could lead to unapproved changes, or in light of GDPR, serious fines – and ultimately diminished customer trust, falling stock prices and tarnished brands.

Facebook has provided a timely reminder of the importance of data governance and the potential scale of fallout should its importance be understated. Facebook’s lack of understanding as to how third-party vendors could use and were using its data landed them in hot PR water (to put it lightly).

Reports indicate 50 million users were affected, and although this is nowhere near the biggest leak in history (or even in recent history, see: Equifax), it’s proof that the reputational damage of a data breach is extensive. And with GDPR fast approaching, that cost will only escalate.

At the very least, organization’s need to demonstrate that they’ve taken the necessary steps to prevent such breaches. This requires understanding what data they currently have, where it is, and also how it may be used by any third parties with access. This is where data governance comes in, but for it to work, many organizations need a culture change.

A Change in Culture

Fostering organizational support for data governance might require a change in organizational culture.

This is especially apparent in organizations that have only adopted the Data Governance 1.0 approach in which DG is siloed from the wider organization and viewed as an “IT-problem.” Such an approach denies data governance initiatives the business contexts needed to function in a data-driven organization.

Data governance is based primarily on three bodies of knowledge: the data dictionary, business glossary and data usage catalog. For these three bodies of knowledge to be complete, they need input from the wider business.

In fact, countless past cases of failed DG implementations can be attributed to organizations lacking organizational support for data governance.

For example, leaving IT to document and assemble a business glossary naturally leads to inconsistencies. In this case, IT departments are tasked with creating a business glossary for terms they often aren’t aware of, don’t understand the context of, or don’t recognize the applications or implications for.

This approach preemptively dooms the initiative, ruling out the value-adding benefits of mature data governance initiatives from the onset.

In erwin’s 2018 State of Data Governance Report, it found that IT departments continue to foot the bill for data governance at 40% of organizations. Budget for data governance comes from the audit and compliance function at 20% of organizations, while the business covers the bill at just 8% of the companies surveyed.

To avoid the aforementioned pitfalls, business leaders need to instill a culture of data governance throughout the organization. This means viewing DG as a strategic initiative and investing in it with inherent organizational and financial support as an on-going practice.

To that end, organizations tend to overvalue the things that can be measured and undervalue the things that cannot. Most organizations want to quantify the value of data governance. As part of a culture shift, organizations should develop a business case for an enterprise data governance initiative that includes calculations for ROI.

By limiting its investment to departmental budgets, data governance must contend with other departmental priorities. As a long-term initiative, it often will lose out to short-term gains.

Of course, this means business leaders need to be heavily invested and involved in data governance themselves – a pillar of data governance readiness in its own right.

Ideally, organizations should implement a collaborative data governance solution to facilitate the organization-wide effort needed to make DG work.

Collaborative in the sense of enabling inter-departmental collaboration so the whole organization’s data assets can be accounted for, but also  in the sense that it works with the other tools that make data governance effective and sustainable – e.g., enterprise architecture, data modeling and business process.

We call this all-encompassing approach to DG an ‘enterprise data governance experience’ or ‘EDGE.’ It’s the Data Governance 2.0 approach, made to reflect how data can be used within the modern enterprise for greater control, context, collaboration and value creation.

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

To learn more about the erwin EDGE, reserve your seat for this webinar.

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Five Pillars of Data Governance Readiness: Initiative Sponsorship

“Facebook at the center of global reckoning on data governance.” This headline from a March 19 article in The Wall Street Journal sums up where we are. With only two months until the General Data Protection Regulation (GDPR) goes into effect, we’re going to see more headlines about improper data governance (DG) – leading to major fines and tarnished brands.

Since the news of the Facebook data scandal broke, the company’s stock has dropped and Nordea, the largest bank in the Nordic region, put a stop to Facebook investments for three months because “we see that the risks related to governance around data protection may have been severely compromised,” it said in a statement.

Last week, we began discussing the five pillars of data governance readiness to ensure the data management foundation is in place for mitigating risks, as well as accomplishing other organizational goals. There can be no doubt that data governance is central to an organization’s customer relationships, reputation and financial results.

So today, we’re going to explore the first pillar of DG readiness: initiative sponsorship. Without initiative sponsorship, organizations will struggle to obtain the funding, resources, support and alignment necessary for successful implementation and subsequent performance.

A Common Roadblock

Data governance isn’t a one-off project with a defined endpoint. It’s an on-going initiative that requires active engagement from executives and business leaders. But unfortunately, the 2018 State of Data Governance Report finds lack of executive support to be the most common roadblock to implementing DG.

This is historical baggage. Traditional DG has been an isolated program housed within IT, and thus, constrained within that department’s budget and resources. More significantly, managing DG solely within IT prevented those in the organization with the most knowledge of and investment in the data from participating in the process.

This silo created problems ranging from a lack of context in data cataloging to poor data quality and a sub-par understanding of the data’s associated risks. Data Governance 2.0 addresses these issues by opening data governance to the whole organization.

Its collaborative approach ensures that those with the most significant stake in an organization’s data are intrinsically involved in discovering, understanding, governing and socializing it to produce the desired outcomes. In this era of data-driven business, C-level executives and department leaders are key stakeholders.

But they must be able to trust it and then collaborate based on their role-specific insights to make informed decisions about strategy, identify new opportunities, address redundancies and improve processes.

So, it all comes back to modern data governance: the ability to understand critical enterprise data within a business context, track its physical existence and lineage, and maximize its value while ensuring quality and security.

Initiative Sponsorship: Encouraging Executive Involvement

This week’s headlines about Facebook have certainly gotten Mark Zuckerberg’s attention, as there are calls for the CEO to appear before the U.S. Congress and British Parliament to answer for his company’s data handling – or mishandling as it is alleged.

Public embarrassment, Federal Trade Commission and GDPR fines, erosion of customer trust/loyalty, revenue loss and company devaluation are real risks when it comes to poor data management and governance practices. Facebook may have just elevated your case for implementing DG 2.0 and involving your executives.

Initiative Sponsorship Data Governance GDPR

Business heads and their teams, after all, are the ones who have the knowledge about the data – what it is, what it means, who and what processes use it and why, and what rules and policies should apply to it. Without their perspective and participation in data governance, the enterprise’s ability to intelligently lock down risks and enable growth will be seriously compromised.

Appropriately implemented – with business data stakeholders driving alignment between DG and strategic enterprise goals and IT handling the technical mechanics of data management – the door opens to trusting data and using it effectively.

Also, a chief data officer (CDO) can serve as the bridge between IT and the business to remove silos in the drive toward DG and subsequent whole-of-business outcomes. He or she would be the ultimate sponsor, leading the charge for the necessary funding, resources, and support for a successful, ongoing initiative.

Initiative Sponsorship with an ‘EDGE’

Once key business leaders understand and buy into the vital role they play in a Data Governance 2.0 strategy, the work of building the infrastructure enabling the workforce and processes to support actively governing data assets and their alignment to the business begins.

To find it, map it, make sure it’s under control, and promote it to appropriate personnel requires a technology- and business-enabling platform that covers the entire data governance lifecycle across all data producer and consumer roles.

The erwin EDGE delivers an ‘enterprise data governance experience’ to unify critical DG domains, use role-appropriate interfaces to bring together stakeholders and processes to support a culture committed to acknowledging data as the mission-critical asset that it is, and orchestrate the key mechanisms that are required to discover, fully understand, actively govern and effectively socialize and align data to the business.

To assess your organizations current data governance readiness, take the erwin DG RediChek.

To learn more about the erwin EDGE, reserve your seat for this webinar.

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