Tag: GDPR

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erwin Expert Blog

Who to Follow in 2019 for Big Data, Data Governance and GDPR Advice

Experts are predicting a surge in GDPR enforcement in 2019 as regulators begin to crackdown on organizations still lagging behind compliance standards.

With this in mind, the erwin team has compiled a list of the most valuable data governance, GDPR and Big data blogs and news sources for data management and data governance best practice advice from around the web.

From regulatory compliance (GDPR, HIPPA, etc.,) to driving revenue through proactive data governance initiatives and Big Data strategies, these accounts cover it all.

Top 7 Data Governance, GDPR and Big Data Blogs and News Sources from Around the Web

Honorable Mention: @BigDataBatman

The Twitter account data professionals deserve, but probably not the one you need right now.

This quirky Twitter bot trawls the web for big data tweets and news stories, and substitutes “big data” for “Batman”. If data is the Bane of your existence, this account will serve up some light relief.

 

1. The erwin Expert Network

Twitter| LinkedIn | Facebook | Blog

For anything data management and data governance related, the erwin Experts should be your first point of call.

The team behind the most connected data management and data governance solutions on the market regularly share best practice advice in guide, whitepaper, blog and social media update form.

 

2. GDPR For Online Entrepreneurs (UK, US, CA, AU)

This community-driven Facebook group is a consistent source of insightful information for data-driven businesses.

In addition to sharing data and GDPR-focused articles from around the web, GDPR For Online Entrepreneurs encourages members to seek GDPR advice from its community’s members.

 

3. GDPR General Data Protection Regulation Technology

LinkedIn also has its own community-driven GDPR advice groups. The most active of these is the, “GDPR General Data Protection Regulation Technology”.

The group aims to be an information hub for anybody responsible for company data, including company leaders, GDPR specialists and consultants, business analysts and process experts. 

Data governance, GDPR, big data blogs

 

 

4. DBTA

Twitter | LinkedIn | Facebook

Database Trends and Applications is a publication that should be on every data professionals’ radar. Alongside news and editorials covering big data, database management, data integrations and more, DBTA is also a great source of advice for professionals looking to research buying options.

Their yearly “Trend-Setting Products in Data and Information Management” list and Product Spotlight featurettes can help data professionals put together proposals, and help give decision makers piece of mind.

 

5. Dataversity

Twitter | LinkedIn

Dataversity is another excellent source for data management and data governance related best practices and think-pieces.

In addition to hosting and sponsoring a number of live events throughout the year, the platform is a regular provider of data leadership webinars and training with a library full of webinars available on-demand.

 

6. WIRED

Twitter | LinkedIn | Facebook

Wired is a physical and digital tech magazine that covers all the bases.

For data professionals that are after the latest news and editorials pertaining to data security and a little extra – from innovations in transport to the applications of Blockchain – Wired is a great publication to keep on your radar.

 

7. TDAN

Twitter | LinkedIn | Facebook

For those looking for something a little more focused, check out TDAN. A subsidiary of Dataversity, TDAN regularly publish new editorial content covering data governance, data management, data modeling and Big Data.

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erwin Expert Blog

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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Top 10 Data Governance Predictions for 2019

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

So what’s on the horizon for data governance in the year ahead? We’re making the following data governance predictions for 2019:

Data Governance Predictions

Top 10 Data Governance Predictions for 2019

1. GDPR-esque regulation for the United States:

GDPR has set the bar and will become the de facto standard across geographies. Look at California as an example with California Consumer Privacy Act (CCPA) going into effect in 2020. Even big technology companies like Apple, Google, Amazon and Twitter are encouraging more regulations in part because they realize that companies that don’t put data privacy at the forefront will feel the wrath from both the government and the consumer.

2. GDPR fines are coming and they will be massive:

Perhaps one of the safest data governance predictions for 2019 is the coming clamp down on GDPR enforcement. The regulations weren’t brought in for show and so it’s likely the fine-free streak for GDPR will be ending … and soon. The headlines will resemble data breaches or hospitals with Health Information Portability Privacy Act (HIPAA) violations in the U.S. healthcare sector. Lots of companies will have an “oh crap” moment and realize they have a lot more to do to get their compliance house in order.

3. Data policies as a consumer buying criteria:

The threat of “data trauma” will continue to drive visibility for enterprise data in the C-suite. How they respond will be the key to their long-term success in transforming data into a true enterprise asset. We will start to see a clear delineation between organizations that maintain a reactive and defensive stance (pain avoidance) versus those that leverage this negative driver as an impetus to increase overall data visibility and fluency across the enterprise with a focus on opportunity enablement. The latter will drive the emergence of true data-driven entities versus those that continue to try to plug the holes in the boat.

4. CDOs will rise, better defined role within the organization:

We will see the chief data officer (CDO) role elevated from being a lieutenant of the CIO to taking a proper seat at the table beside the CIO, CMO and CFO.  This will give them the juice needed to create a sustainable vision and roadmap for data. So far, there’s been a profound lack of consensus on the nature of the role and responsibilities, mandate and background that qualifies a CDO. As data becomes increasingly more vital to an organization’s success from a compliance and business perspective, the role of the CDO will become more defined.

5. Data operations (DataOps) gains traction/will be fully optimized:

Much like how DevOps has taken hold over the past decade, 2019 will see a similar push for DataOps. Data is no longer just an IT issue. As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices. DataOps will enable organizations to better democratize their data and ensure that all business stakeholders work together to deliver quality, data-driven insights.

Data Management and Data Governance

6. Business process will move from back office to center stage:

Business process management will make its way out of the back office and emerge as a key component to digital transformation. The ability for an organization to model, build and test automated business processes is a gamechanger. 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.

7. Turning bad AI/ML data good:

Artificial Intelligence (AI) and Machine Learning (ML) are consumers of data. The risk of training AI and ML applications with bad data will initially drive the need for data governance to properly govern the training data sets. Once trained, the data they produce should be well defined, consistent and of high quality. The data needs to be continuously governed for assurance purposes.

8. Managing data from going over the edge:

Edge computing will continue to take hold. And while speed of data is driving its adoption, organizations will also need to view, manage and secure this data and bring it into an automated pipeline. The internet of things (IoT) is all about new data sources (device data) that often have opaque data structures. This data is often integrated and aggregated with other enterprise data sources and needs to be governed like any other data. The challenge is documenting all the different device management information bases (MIBS) and mapping them into the data lake or integration hub.

9. Organizations that don’t have good data harvesting are doomed to fail:

Research shows that data scientists and analysts spend 80 percent of their time preparing data for use and only 20 percent of their time actually analyzing it for business value. Without automated data harvesting and ingesting data from all enterprise sources (not just those that are convenient to access), data moving through the pipeline won’t be the highest quality and the “freshest” it can be. The result will be faulty intelligence driving potentially disastrous decisions for the business.

10. Data governance evolves to data intelligence:

Regulations like GDPR are driving most large enterprises to address their data challenges. But data governance is more than compliance. “Best-in-breed” enterprises are looking at how their data can be used as a competitive advantage. These organizations are evolving their data governance practices to data intelligence – connecting all of the pieces of their data management and data governance lifecycles to create actionable insights. Data intelligence can help improve the customer experiences and enable innovation of products and services.

The erwin Expert Blog will continue to follow data governance trends and provide best practice advice in the New Year so you can see how our data governance predictions pan out for yourself. To stay up to date, click here to subscribe.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

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erwin Expert Blog

Massive Marriott Data Breach: Data Governance for Data Security

Organizations have been served yet another reminder of the value of data governance for data security.

Hotel and hospitality powerhouse Marriott recently revealed a massive data breach that led to the theft of personal data for an astonishing 500 million customers of its Starwood hotels. This is the second largest data breach in recent history, surpassed only by Yahoo’s breach of 3 billion accounts in 2013 for which it has agreed to pay a $50 million settlement to more than 200 million customers.

Now that Marriott has taken a major hit to its corporate reputation, it has two moves:

  1. Respond: Marriott’s response to its data breach so far has not received glowing reviews. But beyond how it communicates to effected customers, the company must examine how the breach occurred in the first place. This means understanding the context of its data – what assets exist and where, the relationship between them and enterprise systems and processes, and how and by what parties the data is used – to determine the specific vulnerability.
  2. Fix it: Marriott must fix the problem, and quickly, to ensure it doesn’t happen again. This step involves a lot of analysis. A data governance solution would make it a lot less painful by providing visibility into the full data landscape – linkages, processes, people and so on. Then more context-sensitive data security architectures can put in place to for corporate and consumer data privacy.

The GDPR Factor

It’s been six months since the General Data Protection Regulation (GDPR) took effect. While fines for noncompliance have been minimal to date, we anticipate them to dramatically increase in the coming year. Marriott’s bad situation could potentially worsen in this regard, without holistic data governance in place to identify whose and what data was taken.

Data management and data governance, together, play a vital role in compliance, including GDPR. It’s easier to protect sensitive data when you know what it is, where it’s stored and how it needs to be governed.

FREE GUIDE: THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE 

Truly understanding an organization’s data, including the data’s value and quality, requires a harmonized approach embedded in business processes and enterprise architecture. Such an integrated enterprise data governance experience helps organizations understand what data they have, where it is, where it came from, its value, its quality and how it’s used and accessed by people and applications.

Data Governance for Data Security

Data Governance for Data Security: Lessons Learned

Other companies should learn (like pronto) that they need to be prepared. At this point it’s not if, but when, a data breach will rear its ugly head. Preparation is your best bet for avoiding the entire fiasco – from the painstaking process of identifying what happened and why to notifying customers their data and trust in your organization have been compromised.

A well-formed security architecture that is driven by and aligned by data intelligence is your best defense. However, if there is nefarious intent, a hacker will find a way. So being prepared means you can minimize your risk exposure and the damage to your reputation.

Multiple components must be considered to effectively support a data governance, security and privacy trinity. They are:

  1. Data models
  2. Enterprise architecture
  3. Business process models

What’s key to remember is that these components act as links in the data governance chain by making it possible to understand what data serves the organization, its connection to the enterprise architecture, and all the business processes it touches.

THE EXPERT GUIDE TO DATA GOVERNANCE, SECURITY AND PRIVACY

Creating policies for data handling and accountability and driving culture change so people understand how to properly work with data are two important components of a data governance initiative, as is the technology for proactively managing data assets.

Without the ability to harvest metadata schemas and business terms; analyze data attributes and relationships; impose structure on definitions; and view all data in one place according to each user’s role within the enterprise, businesses will be hard pressed to stay in step with governance standards and best practices around security and privacy.

As a consequence, the private information held within organizations will continue to be at risk. Organizations suffering data breaches will be deprived of the benefits they had hoped to realize from the money spent on security technologies and the time invested in developing data privacy classifications. They also may face heavy fines and other financial, not to mention PR, penalties.

Less Pain, More Gain

Most organizations don’t have enough time or money for data management using manual processes. And outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too. Furthermore, manual processes require manual analysis and auditing, which is always more expensive and time consuming.

So the more processes an organization can automate, the less risk of human error, which is actually the primary cause of most data breaches. And automated processes are much easier to analyze and audit because everything is captured, versioned and available for review in a log somewhere. You can read more about automation in our 10 Reasons to Automate Data Mapping and Data Preparation.

And to learn more about how data governance underpins data security and privacy, click here.

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For Pharmaceutical Companies Data Governance Shouldn’t Be a Hard Pill to Swallow

Using data governance in the pharmaceutical industry is a critical piece of the data management puzzle.

Pharmaceutical and life sciences companies face many of the same digital transformation pressures as other industries, such as financial services and healthcare that we have explored previously.

In response, they are turning to technologies like advanced analytics platforms and cloud-based resources to help better inform their decision-making and create new efficiencies and better processes.

Among the conditions that set digital transformation in pharmaceuticals and life sciences apart from other sectors are the regulatory environment and the high incidence of mergers and acquisitions (M&A).

Data Governance, GDPR and Your Business

Protecting sensitive data in these industries is a matter of survival, in terms of the potential penalties for failing to comply with any number of industry and government regulations and because of the near-priceless value of data around research and development (R&D).

The high costs and huge potential of R&D is one of the driving factors of M&A activity in the pharmaceutical and life sciences space. With roughly $156 billion in M&A deals in healthcare in the first quarter of 2018 alone – many involving drug companies – the market is the hottest it’s been in more than a decade. Much of the M&A activity is being driven by companies looking to buy competitors, acquire R&D, and offset losses from expiring drug patents.

 

[GET THE FREE E-BOOK]: APPLICATION PORTFOLIO MANAGEMENT FOR MERGERS & ACQUISITIONS IN THE FINANCIAL SERVICES SECTOR

 

With M&A activity comes the challenge of integrating two formerly separate companies into one. That means integrating technology platforms, business processes, and, of course, the data each organization brings to the deal.

Data Integrity for Risk Management and More

As in virtual every other industry, data is quickly becoming one of the most valuable assets within pharmaceutical and life science companies. In its 2018 Global Life Sciences Outlook, Deloitte speaks to the importance of “data integrity,” which it defines as data that is complete, consistent and accurate throughout the data lifecycle.

Data integrity helps manage risk in pharmaceutical and life sciences by making it easier to comply with a complex web of regulations that touch many different parts of these organizations, from finance to the supply chain and beyond. Linking these cross-functional teams to data they can trust eases the burden of compliance by supplying team members with what many industries now refer to as “a single version of truth” – which is to say, data with integrity.

Data integrity also helps deliver insights for important initiatives in the pharmaceutical and life sciences industries like value-based pricing and market access.

Developing data integrity and taking advantage of it to reduce risk and identify opportunities in pharmaceuticals and life sciences isn’t possible without a holistic approach to data governance that permeates every part of these companies, including business processes and enterprise architecture.

Healthcare Data

Data Governance in the Pharmaceutical Industry Maximizes Value

Data governance gives businesses the visibility they need to understand where their data is, where it came from, its value, its quality and how it can be used by people and software applications. This type of understanding of your data is, of course, essential to compliance. In fact, according to a 2017 survey by erwin, Inc. and UBM, 60 percent of organizations said compliance is driving their data governance initiatives.

Using data governance in the pharmaceutical industry helps organizations contemplating M&A, not only by helping them understand the data they are acquiring, but also by informing decisions around complex IT infrastructures and applications that need to be integrated. Decisions about application rationalization and business processes are easier to make when they are viewed through the lens of a pervasive data governance strategy.

Data governance in the pharmaceutical industry can be leveraged to hone data integrity and move toward what Deloitte refers to as end-to-end evidence management (E2E), which unifies the data in pharmaceuticals and life sciences from R&D to clinical trials and through commercialization.

Once implemented, Deloitte predicts E2E will help organizations maximize the value of their data by:

  • Providing a better understanding of emerging risks
  • Enabling collaboration with health systems, patient advocacy groups, and other constituents
  • Streamlining the development of new therapies
  • Driving down costs

If that list of benefits sounds familiar, it’s because it matches up nicely with the goals of digital transformation at many organizations – more efficient processes, better collaboration, improved visibility and better cost management. And it’s all built on a foundation of data and data governance.

To learn more, download our free whitepaper on the Regulatory Rationale for Integrating Data Management & Data Governance.

Data Modeling Data Goverance

 

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Healthcare Data Governance: What’s the Prognosis?

Healthcare data governance has far more applications than just meeting compliance standards. Healthcare costs are always a topic of discussion, as is the state of health insurance and policies like the Affordable Care Act (ACA).

Costs and policy are among a number of significant trends called out in the executive summary of the Stanford Medicine 2017 Health Trend Report. But the summary also included a common thread that connects them all:

“Behind these trends is one fundamental force driving health care transformation: the power of data.”

Indeed, data is essential to healthcare in areas like:

  • Medical research – Collecting and reviewing increasingly large data sets has the potential to introduce new levels of speed and efficiency into what has been an often slow and laborious process.
  • Preventative care – Wearable devices help consumers track exercise, diet, weight and nutrition, as well as clinical applications like genetic sequencing.
  • The patient experience – Healthcare is not immune to issues of customer service and the need to provide timely, accurate responses to questions or complaints.
  • Disease and outbreak prevention – Data and analysis can help spot patterns, so clinicians get ahead of big problems before they become epidemics.

Data Management and Data Governance

Data Vulnerabilities in Healthcare

Data is valuable to the healthcare industry. But it also carries risks because of the volume and velocity with which it is collected and stored. Foremost among these are regulatory compliance and security.

Because healthcare data is so sensitive, the ways in which it is secured and shared are watched closely by regulators. HIPAA (Health Information Portability and Accountability Act) is probably the most recognized regulation governing data in healthcare, but it is not the only one.

In addition to privacy and security policies, other challenges that prevent the healthcare industry from maximizing the ways it puts data to work include:

  • High costs, which are further exacerbated by expected lower commercial health insurance payouts and higher payouts from low-margin services like Medicare, as well as rising labor costs. Data and analytics can potentially help hospitals better plan for these challenges, but thin margins might prevent the investments necessary in this area.
  • Electronic medical records, which the Stanford report cited as a cause of frustration that negatively impacts relationships between patients and healthcare providers.
  • Silos of data, which often are caused by mergers and acquisitions within the industry, but that are also emblematic of the number of platforms and applications used by providers, insurers and other players in the healthcare market.

Early 2018 saw a number of mergers and acquisitions in the healthcare industry, including hospital systems in New England, as well as in the Philadelphia area of the United States. The $69 billion dollar merger of Aetna and CVS also was approved by shareholders in early 2018, making it one of the most significant deals of the past decade.

Each merger and acquisition requires careful and difficult decisions concerning the application portfolio and data of each organization. Redundancies need to identified, as do gaps, so the patient experience and care continues without serious disruption.

Truly understanding healthcare data requires a holistic approach to data governance that is embedded in business processes and enterprise architecture. When implemented properly, data governance initiatives help healthcare organizations understand what data they have, where it is, where it came from, its value, its quality and how it’s used and accessed by people and applications.

Healthcare Data Governance

Improving Healthcare Analytics and Patient Care with Healthcare Data Governance

Data governance plays a vital role in compliance because data is easier to protect when you know where it is stored, what it is, and how it needs to be governed. According to a 2017 survey by erwin, Inc. and UBM, 60 percent of organizations said compliance was driving their data governance initiatives.

With a solid understand of their data and the ways it is collected and consumed throughout their organizations, healthcare players are better positioned to reap the benefits of analytics. As Deloitte pointed out in a perspectives piece about healthcare analytics, the shift to value-based care makes analytics within the industry more essential than ever.

With increasing pressure on margins, the combination of data governance and analytics is critical to creating value and finding efficiencies. Investments in analytics are only as valuable as the data they are fed, however.

Poor decisions based on poor data will lead to bad outcomes, but they also diminish trust in the analytics platform, which will ruin the ROI as it is used less and less.

Most important, healthcare data governance plays a critical role in helping improve patient outcomes and value. In healthcare, the ability to make timely, accurate decisions based on quality data can be a matter of life or death.

In areas like preventative care and the patient experience, good data can mean better advice to patients, more accurate programs for follow-up care, and the ability to meet their medical and lifestyle needs within a healthcare facility or beyond.

As healthcare organizations look to improve efficiencies, lower costs and provide quality, value-based care, healthcare data governance will be essential to better outcomes for patients, providers and the industry at large.

For more information, please download our latest whitepaper, The Regulatory Rationale for Integrating Data Management and Data Governance.

If you’re interested in healthcare data governance, or evaluating new data governance technologies for another industry, you can schedule a demo of erwin’s data mapping and data governance solutions.

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Michael Pastore is the Director, Content Services at QuinStreet B2B Tech.

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Financial Services Data Governance: Helping Value ‘the New Currency’

For organizations operating in financial services data governance is becoming increasingly more important. When financial services industry board members and executives gathered for EY’s Financial Services Leadership Summit in early 2018, data was a major topic of conversation.

Attendees referred to data as “the new oil” and “the new currency,” and with good reason. Financial services organizations, including banks, brokerages, insurance companies, asset management firms and more, collect and store massive amounts of data.

But data is only part of the bigger picture in financial services today. Many institutions are investing heavily in IT to help transform their businesses to serve customers and partners who are quickly adopting new technologies. For example, Gartner research expects the global banking industry will spend $519 billion on IT in 2018.

The combination of more data and technology and fewer in-person experiences puts a premium on trust and customer loyalty. Trust has long been at the heart of the financial services industry. It’s why bank buildings in a bygone era were often erected as imposing stone structures that signified strength at a time before deposit insurance, when poor management or even a bank robbery could have devastating effects on a local economy.

Trust is still vital to the health of financial institutions, except today’s worst-case scenario often involves faceless hackers pillaging sensitive data to use or re-sell on the dark web. That’s why governing all of the industry’s data, and managing the risks that comes with collecting and storing such vast amounts of information, is increasingly a board-level issue.

The boards of modern financial services institutions understand three important aspects of data:

  1. Data has a tremendous amount of value to the institution in terms of helping identify the wants and needs of customers.
  2. Data is central to security and compliance, and there are potentially severe consequences for organizations that run afoul of either.
  3. Data is central to the transformation underway at many financial institutions as they work to meet the needs of the modern customer and improve their own efficiencies.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

Data governance helps organizations in financial services understand their data. It’s essential to protecting that data and to helping comply with the many government and industry regulations in the industry. But financial services data governance – all data governance in fact – is about more than security and compliance; it’s about understanding the value and quality of data.

When done right and deployed in a holistic manner that’s woven into the business processes and enterprise architecture, data governance helps financial services organizations better understand where their data is, where it came from, its value, its quality, and how the data is accessed and used by people and applications.

Financial Services Data Governance: It’s Complicated

Financial services data governance is getting increasingly complicated for a number of reasons.

Mergers & Acquisitions

Deloitte’s 2018 Banking and Securities M&A Outlook described 2017 as “stuck in neutral,” but there is reason to believe the market picks up steam in 2018 and beyond, especially when it comes to financial technology (or fintech) firms. Bringing in new sets of data, new applications and new processes through mergers and acquisitions creates a great deal of complexity.

The integrations can be difficult, and there is an increased likelihood of data sprawl and data silos. Data governance not only helps organizations better understand the data, but it also helps make sense of the application portfolios of merging institutions to discover gaps and redundancies.

Regulatory Environment

There is a lengthy list of regulations and governing bodies that oversee the financial services industry, covering everything from cybersecurity to fraud protection to payment processing, all in an effort to minimize risk and protect customers.

The holistic view of data that results from a strong data governance initiative is becoming essential to regulatory compliance. According to a 2017 survey by erwin, Inc. and UBM, 60 percent of organizations said compliance drives their data governance initiatives.

More Partnerships and Networks

According to research by IBM, 45 percent of bankers say partnerships and alliances help improve their agility and competitiveness. Like consumers, today’s financial institutions are more connected than ever before, and it’s no longer couriers and cash that are being transferred in these partnerships; it’s data.

Understanding the value, quality and risk of the data shared in these alliances is essential – not only to be a good partner and derive a business benefit from the relationship, but also to evaluate whether or not an alliance or partnership makes good business sense.

Financial Services Data Governance

More Sources of Data, More Touch Points

Financial services institutions are at the forefront of the multi-channel customer experience and have been for years. People do business with institutions by phone, in person, via the Web, and using mobile devices.

All of these touch points generate data, and it is essential that organizations can tie them all together to understand their customers. This information is not only important to customer service, but also to finding opportunities to grow relationships with customers by identifying where it makes sense to upsell and cross-sell products and services.

Grow the Business, Manage the Risk

In the end, financial services organizations need to understand the ways their data can help grow the business and manage risk. Data governance plays an important role in both.

Financial services data governance can better enable:

  • The personalized, self-service, applications customers want
  • The machine learning solutions that automate decision-making and create more efficient business processes
  • Faster and more accurate identification of cross-sell and upsell opportunities
  • Better decision-making about the application portfolio, M&A targets, M&A success and more

If you’re interested in financial services data governance, or evaluating new data governance technologies for another industry, you can schedule a demo of erwin’s data mapping and data governance solutions.

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And you also might want to download our latest e-book, Solving the Enterprise Data Dilemma.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech.

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Defining DG: What Can Data Governance Do for You?

Data governance (DG) is becoming more commonplace because of data-driven business, yet defining DG and putting into sound practice is still difficult for many organizations.

Defining DG

The absence of a standard approach to defining DG could be down to its history of missed expectations, false starts and negative perceptions about it being expensive, intrusive, impeding innovation and not delivering any value. Without success stories to point to, the best way of doing and defining DG wasn’t clear.

On the flip side, the absence of a standard approach to defining DG could be the reason for its history of lacklustre implementation efforts, because those responsible for overseeing it had different ideas about what should be done.

Therefore, it’s been difficult to fully fund a data governance initiative that is underpinned by an effective data management capability. And many organizations don’t distinguish between data governance and data management, using the terms interchangeably and so adding to the confusion.

Defining DG: The Data Governance Conundrum

While research indicates most view data governance as “critically important” or they recognize the value of data, the large percentage without a formal data governance strategy in place indicates there are still significant teething problems.

How Important is Data Governance

And that’s the data governance conundrum. It is essential but unwanted and/or painful.

It is a complex chore, so organizations have lacked the motivation to start and effectively sustain it. But faced with the General Data Protection Regulation (GDPR) and other compliance requirements, they have been doing the bare minimum to avoid the fines and reputational damage.

And arguably, herein lies the problem. Organizations look at data governance as something they have to do rather than seeing what it could do for them.

Data governance has its roots in the structure of business terms and technical metadata, but it has tendrils and deep associations with many other components of a data management strategy and should serve as the foundation of that platform.

With data governance at the heart of data management, data can be discovered and made available throughout the organization for both IT and business stakeholders with approved access. This means enterprise architecture, business process, data modeling and data mapping all can draw from a central metadata repository for a single source of data truth, which improves data quality, trust and use to support organizational objectives.

But this “data nirvana” requires a change in approach to data governance. First, recognizing that Data Governance 1.0 was made for a different time when the volume, variety and velocity of the data an organization had to manage was far lower and when data governance’s reach only extended to cataloging data to support search and discovery. 

Data Governance Evolution

Modern data governance needs to meet the needs of data-driven business. We call this adaptation “Evolving DG.” It is the journey to a cost-effective, mature, repeatable process that permeates the whole organization.

The primary components of Evolving DG are:

  • Evaluate
  • Plan
  • Configure
  • Deliver
  • Feedback

The final step in such an evolution is the implementation of the erwin Enterprise Data Governance Experience (EDGE) platform.

The erwin EDGE places data governance at the heart of the larger data management suite. By unifying the data management suite at a fundamental level, an organization’s data is no longer marred by departmental and software silos. It brings together both IT and the business for data-driven insights, regulatory compliance, agile innovation and business transformation.

It allows every critical piece of the data management and data governance lifecycle to draw from a single source of data truth and ensure quality throughout the data pipeline, helping organizations achieve their strategic objectives including:

  • Operational efficiency
  • Revenue growth
  • Compliance, security and privacy
  • Increased customer satisfaction
  • Improved decision-making

To learn how you can evolve your data governance practice and get an EDGE on your competition, click here.

Solving the Enterprise Data Dilemma

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The Data Governance (R)Evolution

Data governance continues to evolve – and quickly.

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

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

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

So where are we today?

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

Data Governance is Everyone's Business

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

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

Connecting Data Governance to Your Organization

  1. Data Mapping & Data Governance

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

  1. Data Modeling & Data Governance

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

  1. Business Process Modeling & Data Governance

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

  1. Enterprise Architecture & Data Governance

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

The EDGE Revolution

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

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

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

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

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

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

Solving the Enterprise Data Dilemma

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Healthy Co-Dependency: Data Management and Data Governance

Data management and data governance are now more important than ever before. The hyper competitive nature of data-driven business means organizations need to get more out of their data than ever before – and fast.

A few data-driven exemplars have led the way, turning data into actionable insights that influence everything from corporate structure to new products and pricing. “Few” being the operative word.

It’s true, data-driven business is big business. Huge actually. But it’s dominated by a handful of organizations that realized early on what a powerful and disruptive force data can be.

The benefits of such data-driven strategies speak for themselves: Netflix has replaced Blockbuster, and Uber continues to shake up the taxi business. Organizations indiscriminate of industry are following suit, fighting to become the next big, disruptive players.

But in many cases, these attempts have failed or are on the verge of doing so.

Now with the General Data Protection Regulation (GDPR) in effect, data that is unaccounted for is a potential data disaster waiting to happen.

So organizations need to understand that getting more out of their data isn’t necessarily about collecting more data. It’s about unlocking the value of the data they already have.

Data Management and Data Governance Co-Dependency

The Enterprise Data Dilemma

However, most organizations don’t know exactly what data they have or even where some of it is. And some of the data they can account for is going to waste because they don’t have the means to process it. This is especially true of unstructured data types, which organizations are collecting more frequently.

Considering that 73 percent of company data goes unused, it’s safe to assume your organization is dealing with some if not all of these issues.

Big picture, this means your enterprise is missing out on thousands, perhaps millions in revenue.

The smaller picture? You’re struggling to establish a single source of data truth, which contributes to a host of problems:

  • Inaccurate analysis and discrepancies in departmental reporting
  • Inability to manage the amount and variety of data your organization collects
  • Duplications and redundancies in processes
  • Issues determining data ownership, lineage and access
  • Achieving and sustaining compliance

To avoid such circumstances and get more value out of data, organizations need to harmonize their approach to data management and data governance, using a platform of established tools that work in tandem while also enabling collaboration across the enterprise.

Data management drives the design, deployment and operation of systems that deliver operational data assets for analytics purposes.

Data governance delivers these data assets within a business context, tracking their physical existence and lineage, and maximizing their security, quality and value.

Although these two disciplines approach data from different perspectives (IT-driven and business-oriented), they depend on each other. And this co-dependency helps an organization make the most of its data.

The P-M-G Hub

Together, data management and data governance form a critical hub for data preparation, modeling and data governance. How?

It starts with a real-time, accurate picture of the data landscape, including “data at rest” in databases, data warehouses and data lakes and “data in motion” as it is integrated with and used by key applications. That landscape also must be controlled to facilitate collaboration and limit risk.

But knowing what data you have and where it lives is complicated, so you need to create and sustain an enterprise-wide view of and easy access to underlying metadata. That’s a tall order with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. So the applications and initiatives that depend on a solid data infrastructure may be compromised, and data analysis based on faulty insights.

However, these issues can be addressed with a strong data management strategy and technology to enable the data quality required by the business, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Being able to pinpoint what data exists and where must be accompanied by an agreed-upon business understanding of what it all means in common terms that are adopted across the enterprise. Having that consistency is the only way to assure that insights generated by analyses are useful and actionable, regardless of business department or user exploring a question. Additionally, policies, processes and tools that define and control access to data by roles and across workflows are critical for security purposes.

These issues can be addressed with a comprehensive data governance strategy and technology to determine master data sets, discover the impact of potential glossary changes across the enterprise, audit and score adherence to rules, discover risks, and appropriately and cost-effectively apply security to data flows, as well as publish data to people/roles in ways that are meaningful to them.

Data Management and Data Governance: Play Together, Stay Together

When data management and data governance work in concert empowered by the right technology, they inform, guide and optimize each other. The result for an organization that takes such a harmonized approach is automated, real-time, high-quality data pipeline.

Then all stakeholders — data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access the data they’re authorized to use and base strategic decisions on what is now a full inventory of reliable information.

The erwin EDGE creates an “enterprise data governance experience” through integrated data mapping, business process modeling, enterprise architecture modeling, data modeling and data governance. No other software platform on the market touches every aspect of the data management and data governance lifecycle to automate and accelerate the speed to actionable business insights.