Category: erwin Expert Blog

Categories
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.

 

Automate Data Mapping

Categories
erwin Expert Blog

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

Categories
erwin Expert Blog

The Unified Data Platform – Connecting Everything That Matters

Businesses stand to gain a lot from a unified data platform.

This decade has seen data-driven leaders dominate their respective markets and inspire other organizations across the board to use data to fuel their businesses, leveraging this strategic asset to create more value below the surface. It’s even been dubbed “the new oil,” but data is arguably more valuable than the analogy suggests.

Data governance (DG) is a key component of the data value chain because it connects people, processes and technology as they relate to the creation and use of data. It equips organizations to better deal with  increasing data volumes, the variety of data sources, and the speed in which data is processed.

But for an organization to realize and maximize its true data-driven potential, a unified data platform is required. Only then can all data assets be discovered, understood, governed and socialized to produce the desired business outcomes while also reducing data-related risks.

Benefits of a Unified Data Platform

Data governance can’t succeed in a bubble; it has to be connected to the rest of the enterprise. Whether strategic, such as risk and compliance management, or operational, like a centralized help desk, your data governance framework should span and support the entire enterprise and its objectives, which it can’t do from a silo.

Let’s look at some of the benefits of a unified data platform with data governance as the key connection point.

Understand current and future state architecture with business-focused outcomes:

A unified data platform with a single metadata repository connects data governance to the roles, goals strategies and KPIs of the enterprise. Through integrated enterprise architecture modeling, organizations can capture, analyze and incorporate the structure and priorities of the enterprise and related initiatives.

This capability allows you to plan, align, deploy and communicate a high-impact data governance framework and roadmap that sets manageable expectations and measures success with metrics important to the business.

Document capabilities and processes and understand critical paths:

A unified data platform connects data governance to what you do as a business and the details of how you do it. It enables organizations to document and integrate their business capabilities and operational processes with the critical data that serves them.

It also provides visibility and control by identifying the critical paths that will have the greatest impacts on the business.

Realize the value of your organization’s data:

A unified data platform connects data governance to specific business use cases. The value of data is realized by combining different elements to answer a business question or meet a specific requirement. Conceptual and logical schemas and models provide a much richer understanding of how data is related and combined to drive business value.

2020 Data Governance and Automation Report

Harmonize data governance and data management to drive high-quality deliverables:

A unified data platform connects data governance to the orchestration and preparation of data to drive the business, governing data throughout the entire lifecycle – from creation to consumption.

Governing the data management processes that make data available is of equal importance. By harmonizing the data governance and data management lifecycles, organizations can drive high-quality deliverables that are governed from day one.

Promote a business glossary for unanimous understanding of data terminology:

A unified data platform connects data governance to the language of the business when discussing and describing data. Understanding the terminology and semantic meaning of data from a business perspective is imperative, but most business consumers of data don’t have technical backgrounds.

A business glossary promotes data fluency across the organization and vital collaboration between different stakeholders within the data value chain, ensuring all data-related initiatives are aligned and business-driven.

Instill a culture of personal responsibility for data governance:

A unified data platform is inherently connected to the policies, procedures and business rules that inform and govern the data lifecycle. The centralized management and visibility afforded by linking policies and business rules at every level of the data lifecycle will improve data quality, reduce expensive re-work, and improve the ideation and consumption of data by the business.

Business users will know how to use (and how not to use) data, while technical practitioners will have a clear view of the controls and mechanisms required when building the infrastructure that serves up that data.

Better understand the impact of change:

Data governance should be connected to the use of data across roles, organizations, processes, capabilities, dashboards and applications. Proactive impact analysis is key to efficient and effective data strategy. However, most solutions don’t tell the whole story when it comes to data’s business impact.

By adopting a unified data platform, organizations can extend impact analysis well beyond data stores and data lineage for true visibility into who, what, where and how the impact will be felt, breaking down organizational silos.

Getting the Competitive “EDGE”

The erwin EDGE delivers an “enterprise data governance experience” in which every component of the data value chain is connected.

Now with data mapping, it unifies data preparation, enterprise modeling and data governance to simplify the entire data management and governance lifecycle.

Both IT and the business have access to an accurate, high-quality and real-time data pipeline that fuels regulatory compliance, innovation and transformation initiatives with accurate and actionable insights.

Categories
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.

Automate Data Mapping

Categories
erwin Expert Blog Data Governance

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

 

Categories
erwin Expert Blog

Data Modeling and Data Mapping: Results from Any Data Anywhere

A unified approach to data modeling and data mapping could be the breakthrough that many data-driven organizations need.

In most of the conversations I have with clients, they express the need for a viable solution to model their data, as well as the ability to capture and document the metadata within their environments.

Data modeling is an integral part of any data management initiative. Organizations use data models to tame “data at rest” for business use, governance and technical management of databases of all types.

However, once an organization understands what data it has and how it’s structured via data models, it needs answers to other critical questions: Where did it come from? Did it change along the journey? Where does it go from here?

Data Mapping: Taming “Data in Motion”

Knowing how data moves throughout technical and business data architectures is key for true visibility, context and control of all data assets.

Managing data in motion has been a difficult, time-consuming task that involves mapping source elements to the data model, defining the required transformations, and/or providing the same for downstream targets.

Historically, it either has been outsourced to ETL/ELT developers who often create a siloed, technical infrastructure opaque to the business, or business-friendly mappings have been kept in an assortment of unwieldy spreadsheets difficult to consolidate and reuse much less capable of accommodating new requirements.

What if you could combine data at rest and data in motion to create an efficient, accurate and real-time data pipeline that also includes lineage? Then you can spend your time finding the data you need and using it to produce meaningful business outcomes.

Good news … you can.

erwin Mapping Manager: Connected Data Platform

Automated Data Mapping

Your data modelers can continue to use erwin Data Modeler (DM) as the foundation of your database management system, documenting, enforcing and improving those standards. But instead of relying on data models to disseminate metadata information, you can scan and integrate any data source and present it to all interested parties – automatically.

erwin Mapping Manager (MM) shifts the management of metadata away from data models to a dedicated, automated platform. It can collect metadata from any source, including JSON documents, erwin data models, databases and ERP systems, out of the box.

This functionality underscores our Any2 data approach by collecting any data from anywhere. And erwin MM can schedule data collection and create versions for comparison to clearly identify any changes.

Metadata definitions can be enhanced using extended data properties, and detailed data lineages can be created based on collected metadata. End users can quickly search for information and see specific data in the context of business processes.

To summarize the key features current data modeling customers seem to be most excited about:

  • Easy import of legacy mappings, plus share and reuse mappings and transformations
  • Metadata catalog to automatically harvest any data from anywhere
  • Comprehensive upstream and downstream data lineage
  • Versioning with comparison features
  • Impact analysis

And all of these features support and can be integrated with erwin Data Governance. The end result is knowing what data you have and where it is so you can fuel a fast, high-quality and complete pipeline of any data from anywhere to accomplish your organizational objectives.

Want to learn more about a unified approach to data modeling and data mapping? Join us for our weekly demo to see erwin MM in action for yourself.

erwin Mapping Manager

Categories
erwin Expert Blog

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.

Data Mapping Demo CTA

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

Categories
erwin Expert Blog

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.

Data Mapping Demo CTA

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.

Categories
erwin Expert Blog Data Governance Data Intelligence

Demystifying Data Lineage: Tracking Your Data’s DNA

Getting the most out of your data requires getting a handle on data lineage. That’s knowing what data you have, where it is, and where it came from – plus understanding its quality and value to the organization.

But you can’t understand your data in a business context much less track data lineage, its physical existence and maximize its security, quality and value if it’s scattered across different silos in numerous applications.

Data lineage provides a way of tracking data from its origin to destination across its lifespan and all the processes it’s involved in. It also plays a vital role in data governance. Beyond the simple ability to know where the data came from and whether or not it can be trusted, there’s an element of statutory reporting and compliance that often requires a knowledge of how that same data (known or unknown, governed or not) has changed over time.

A platform that provides insights like data lineage, impact analysis, full-history capture, and other data management features serves as a central hub from which everything can be learned and discovered about the data – whether a data lake, a data vault or a traditional data warehouse.

In a traditional data management organization, Excel spreadsheets are used to manage the incoming data design, what’s known as the “pre-ETL” mapping documentation, but this does not provide any sort of visibility or auditability. In fact, each unit of work represented in these ‘mapping documents’ becomes an independent variable in the overall system development lifecycle, and therefore nearly impossible to learn from much less standardize.

The key to accuracy and integrity in any exercise is to eliminate the opportunity for human error – which does not mean eliminating humans from the process but incorporating the right tools to reduce the likelihood of error as the human beings apply their thought processes to the work.

Data Lineage

Data Lineage: A Crucial First Step for Data Governance

Knowing what data you have and where it lives and where it came from is complicated. The lack of visibility and control around “data at rest” combined with “data in motion,” as well as difficulties with legacy architectures, means organizations spend more time finding the data they need rather than using it to produce meaningful business outcomes.

Organizations need to create and sustain an enterprise-wide view of and easy access to underlying metadata, but 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, resulting in faulty analyses.

These issues can be addressed with a strong data management strategy underpinned by technology that enables the data quality the business requires, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

An automated, metadata-driven framework for cataloging data assets and their flows across the business provides an efficient, agile and dynamic way to generate data lineage from operational source systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture; construct business glossaries; assess what data aligns with specific business rules and policies; and inform how that data is transformed, integrated and federated throughout business processes – complete with full documentation.

Centralized design, immediate lineage and impact analysis, and change-activity logging means you will always have answers readily available, or just a few clicks away. Subsets of data can be identified and generated via predefined templates, generic designs generated from standard mapping documents, and pushed via ETL process for faster processing via automation templates.

With automation, data quality is systemically assured and the data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders. Without such automation, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by a manual approach. And outsourcing these data management efforts to professional services firms only increases costs and schedule delays.

With erwin Mapping Manager, organizations can automate enterprise data mapping and code generation for faster time-to-value and greater accuracy when it comes to data movement projects, as well as synchronize “data in motion” with data management and governance efforts.

Map data elements to their sources within a single repository to determine data lineage, deploy data warehouses and other Big Data solutions, and harmonize data integration across platforms. The web-based solution reduces the need for specialized, technical resources with knowledge of ETL and database procedural code, while making it easy for business analysts, data architects, ETL developers, testers and project managers to collaborate for faster decision-making.

Data Lineage

Categories
erwin Expert Blog

Mind the Gap: Aligning Business and IT

Aligning business and IT is a serious goal for modern organizations. The rapid pace of technological innovation requires the introduction of new tools and processes to cope.

The importance of business and IT alignment is widely understood and  reflected in the concept’s prevalence in IT-maturity and best-practice conversations. What’s less understood is the substance:  what does “aligning business and IT” really mean and how do organizations make it a reality.

Aligning Business and IT: Collaboration

All together now … strength in unity

To understand and solve any problem, we need to uncover the source.

Broadly speaking, experience tells us that the lack of business and IT alignment is a cultural issue on both sides of the equation. IT teams that classify and treat projects as “IT projects” instead of “business projects” demonstrate the IT cultural bias, as does  referring to a project as an “IT project,” which isolates it from the wider business from the start, making it easy for IT to lose sight of the desired business outcome.

In the end, there is no such thing as just an IT project as all projects should aim to improve the business as a whole.

But cultural issues aren’t easy to overcome. They require a shift in employee mindset, which in turn requires strong leadership. This means IT leadership educating IT teams about  the business strategy behind every project and continuously reinforcing it until that approach becomes second nature.

Today’s IT leader must be a business leader first and a technical specialist second.  Organizations with this type of IT leadership will be in a better position, with business and IT alignment already prioritized, automatic and systemic.

The three best practices for aligning business and IT

1. Understand business strategy and objectives

A business strategy is the vision an enterprise is trying to realize, while objectives are the steps it takes to achieve it. Every department needs to understand  the organization’s strategy and objectives and what will be required  to accomplish them. Then every department and every employee is working toward the same goals.

For IT, business strategy and objectives always should factor into how projects are prioritized and planned.

2. Know your capabilities and map them to enterprise needs to identify gaps

First, you need to understand your current IT environment and the capabilities you have so you can  map them to enterprise requirements. Enterprise architecture enables this understanding with an assessment of the “current state,” including assets at the organization’s disposal and the connections between them. Then you can map the current-state enterprise architecture to business goals, identifying the gaps between the current state and the desired “future state.”

IT’s goal then becomes helping the organization achieve the future state so its goals can be achieved.

3. Use a structured approach to prioritize investments 

An easily understood, repeatable process supported by the appropriate technology provides a rigorous and systematic approach to IT strategy development and delivery. While employing a hodgepodge of tools that work poorly with one another – or not at all – will grind progress to a halt.

A comprehensive data management suite with collaborative features circumvents this issue, so organizations suffer less false starts when attempting to introduce change.

Collaboration ensures mission-critical information is discussed within a business context so IT plans can be implemented with greater understanding and less push-back.

Automatic Cohesion

The ultimate state – current and future – is one in which IT and the enterprise aren’t just aligned; they’ve become one and the same.

The business drives IT, and IT enables or powers the enterprise. In modern enterprises meeting and exceeding customer expectations, IT folks not only “get” the business, they “are” the business.

Sandhill’s Jog Raj will explore aligning business and IT further in a FREE WEBINAR on November 13. Register now.

Mind the Gap: Aligning Business and IT Webinar