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Business Process Modeling Use Cases and Definition

What is business process modeling (BPM)? A visual representation of what your business does and how it does it. Why is having this picture important?

According to Gartner, BPM links business strategy to IT systems development to ensure business value. It also combines process/ workflow, functional, organizational and data/resource views with underlying metrics such as costs, cycle times and responsibilities to provide a foundation for analyzing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies.

Every organization—particularly those operating in industries where quality, regulatory, health, safety or environmental issues are a concern—must have a complete understanding of its processes. Equally important, employees must fully comprehend and be accountable for appropriately carrying out the processes for which they are responsible.

BPM allows organizations to benefit from an easily digestible visualization of its systems and the associated information. It makes it easier to be agile and responsive to changes in markets and consumer demands,

This is because the visualization process galvanizes an organization’s ability to identify areas of improvement, potential innovation and necessary reorganization.

But a theoretical understanding of business process modeling will only get you so far. The following use cases demonstrate the benefits of business process modeling in real life.

Business process modeling (BPM) is a practice that helps organizations understand how their strategy relates to their IT systems and system development.

Business Process Modeling Use Cases

Compliance:

Regulations like the E.U.’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are requiring businesses across industries to think about their compliance efforts. Business process modeling helps organizations prove what they are doing to meet compliance requirements and understand how changes to their processes impact compliance efforts (and vice versa).

The visualization process can aid in an organization’s ability to understand the security risks associated with a particular process. It also means that should a breach occur, the organization’s greater understanding of its processes and related systems means they can respond with greater agility, mitigate the damage and quickly inform affected parties as required specifically by GDPR.

In the case of an audit, BPM can be used to demonstrate that the organization is cognizant of compliance standards and is doing what is required.

This also extends to industry-specific other compliance mandates  such as those in healthcare, pharmaceutical and the financial services industries.

The Regulatory Rationale for Integrating Data Management & Data Governance

The Democratization of Information:

Increasing an organizations ability to retain knowledge is another cross-industry use case for business process modeling. This use case benefits organizations in two key areas:

1. Democratization of information.

By documenting processes, organizations can ensure that knowledge and information is de-siloed and that the organization as a whole can benefit from it. In this case, a key best practice to consider is the introduction of role/user-based access. This way an organization can ensure only the necessary parties can access such information and ensure they are in keeping with compliance standards.

2. Knowledge retention.

By documenting processes and democratizing information, process-specific knowledge can be retained, even when key employees leave. This is particularly important in the case of an aging workforce, where an organization could suffer a “brain drain” as large numbers of employees retire during a short span of time.

Digital Transformation:

Once in a while, a technological revolution turns the nature of business on its head. The most recent and arguably most significant of which – although at this point it’s hard to argue – is the rise of data-driven businesses.

In a relatively short amount of time, the leaders in data-driven businesses were launched and stormed their way to the forefront of their respective industries – think Amazon, Netflix and Uber.

The result? Data is now considered more valuable than oil and industries across the board are seeing digital transformation en masse.

There’s a clear connection between business process modeling and digital transformation initiatives. With it, an organization can explore models to understand information assets within a business context, from internal operations to full customer experiences.

This practice identifies and drives digital transformation opportunities to increase revenue while limiting risks and avoiding regulatory and compliance gaffes.

Organizations that leverage BPM in their digital transformation efforts can use their greater

understanding of their current processes to make more informed decisions about future implementations.

And the use cases for business process modeling don’t stop there.

A better understanding of your organizations processes can also ease software deployments and make mergers and acquisitions (M&A) far easier to handle. Large organizations grow through M&A activity, and the combining of business processes, software applications and infrastructure when two organizations become one is very complex.

Business process modeling offers visibility into existing processes and helps design new processes that will deliver results in a post-merger environment.

The latest guide from the erwin Experts expands on these use cases and details how best to use business process modeling to tame your organization’s complexity and maximize its potential and profits.

Business Process Modeling Use Cases

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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.