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Business Architecture and Process Modeling for Digital Transformation

At a fundamental level, digital transformation is about further synthesizing an organization’s operations and technology, so involving business architecture and process modeling is a best practice organizations cannot ignore.

This post outlines how business architecture and process modeling come together to facilitate efficient and successful digital transformation efforts.

Business Process Modeling: The First Step to Giving Customers What They Expect

Salesforce recently released the State of the Connected Customer report, with 75 percent of customers saying they expect companies to use new technologies to create better experiences. So the business and digital transformation playbook has to be updated.

These efforts must be carried out with continuous improvement in mind. Today’s constantly evolving business environment totally reinforces the old adage that change is the only constant.

Even historically reluctant-to-change banks now realize they need to innovate, adopting digital transformation to acquire and retain customers. Innovate or die is another adage that holds truer than ever before.

Fidelity International is an example of a successful digital transformation adopter and innovator. The company realized that different generations want different information and have distinct communication preferences.

For instance, millennials are adept at using digital channels, and they are the fastest-growing customer base for financial services companies. Fidelity knew it needed to understand customer needs and adapt its processes around key customer touch points and build centers of excellence to support them.

Business Architecture and Process Modeling

Planning and working toward a flexible, responsive and adaptable future is no longer enough – the modern organization must be able to visualize not only the end state (the infamous and so-elusive “to-be”) but also perform detailed and comprehensive impact analysis on each scenario, often in real time. This analysis also needs to span multiple departments, extending beyond business and process architecture to IT, compliance and even HR and legal.

The ability of process owners to provide this information to management is central to ensuring the success of any transformation initiative. And new requirements and initiatives need to be managed in new ways. Digital and business transformation is about being able to do three things at the same time, all working toward the same goals:

Collect, document and analyze requirements
Establish all information layers impacted by the requirements
Develop and test the impact of multiple alternative scenarios

Comprehensive business process modeling underpins all of the above, providing the central information axis around which initiatives are scoped, evaluated, planned, implemented and ultimately managed.

Because of its central role, business process modeling must expand to modeling information from other layers within the organization, including:

System and application usage information
Supporting and reference documentation
Compliance, project and initiative information
Data usage

All these information layers must be captured and modeled at the appropriate levels, then connected to form a comprehensive information ecosystem that enables parts of the organization running transformation and other initiatives to instantly access and leverage it for decision-making, simulation and scenario evaluation, and planning, management and maintenance.

No Longer a Necessary Evil

Traditionally, digital and business transformation initiatives relied almost exclusively on human knowledge and experience regarding processes, procedures, how things worked, and how they fit together to provide a comprehensive and accurate framework. Today, technology can aggregate and manage all this information – and more – in a structured, organized and easily accessible way.

Business architecture extends beyond simple modeling; it also incorporates automation to reduce manual effort, remove potential for error, and guarantee effective data governance – with visibility from strategy all the way down to data entry and the ability to trace and manage data lineage. It requires robotics to cross-reference mass amounts of information, never before integrated to support effective decision-making.

The above are not options that are “nice to have,” but rather necessary gateways to taking business process management into the future. And the only way to leverage them is through systemic, organized and comprehensive business architecture modeling and analysis.

Therefore, business architecture and process modeling are no longer a necessary evil. They are critical success factors to any digital or business transformation journey.

A Competitive Weapon

Experts confirm the need to rethink and revise business processes to incorporate more digital automation. Forrester notes in its report, The Growing Importance of Process to Digital Transformation, that the changes in how business is conducted are driving the push “to reframe organizational operational processes around digital transformation efforts.” In a dramatic illustration of the need to move in this direction, the research firm writes that “business leaders are looking to use process as a competitive weapon.”

If a company hasn’t done a good job of documenting its processes, it can’t realize a future in which digital transformation is part of everyday operations. It’s never too late to start, though. In a fast-moving and pressure cooker business environment, companies need to implement business process models that make it possible to visually and analytically represent the steps that will add value to the company – either around internal operations or external ones, such as product or service delivery.

erwin BP, part of the erwin EDGE Platform, enables effective business architecture and process modeling. With it, any transformation initiative becomes a simple, streamlined exercise to support distributed information capture and management, object-oriented modeling, simulation and collaboration.

To find out about how erwin can help in empowering your transformation initiatives, please click here.

Why Data vs. Process is dead, and why we should look at the two working together

So What is Process Modeling?

“Process modeling is the documentation of an organization’s processes designed to enhance company performance,” said Martin Owen, erwin’s VP of Product Management.

It does this by enabling a business to understand what they do, and how they do it.

As is commonplace for disciplines of this nature, there are multiple industry standards that provide the basis of the approach to how this documentation is handled.

The most common of which, is the “business process modeling notation” (BPMN) standard. With BPMN, businesses can analyze their processes from different perspectives, such as a human capital perspective, shining a light on the roles and competencies required for a process to perform.

Where does Data Modeling tie in with Process Modeling?

Historically, industry analysts have viewed Data and Process Modeling as two competing approaches. However, it’s time that notion was cast aside, as the benefits of the two working in tandem are too great to just ignore.

The secret behind making the most out of data, is being able to see the full picture, as well as drill down – or rather, zoom in – on what’s important in the given context.

From a process perspective, you will be able to see what data is used in the process and architecture models. And from a data perspective, users can see the context of the data and the impact of all the places it is used in processes across the enterprise. This provides a more well-rounded view of the organization and the data. Data modelers will benefit from this, enabling them to create and manage better data models, as well as implement more context specific data deployments.

It could be that the former approach to Data and Process Modeling was born out of the cost to invest in both (for some businesses) being too high, aligning the two approaches being too difficult, or a cocktail of both.

The latter is perhaps the more common culprit, though. This is evident when we consider the many companies already modeling both their data and processes. But the problem with the current approach is that the two model types are siloed, severing the valuable connections between the data and meaning alignment is difficult to achieve. Additionally, although all the data is there, the aforementioned severed connections are just as useful as the data itself, and so denying them means a business isn’t seeing the full picture.

However, there are now examples of both Data and Process Modeling being united under one banner.

“By bringing both data and process together, we are delivering more value to different stakeholders in the organization by providing more visibility of each domain,” suggested Martin. “Data isn’t locked into the database administrator or architect, it’s now expressed to the business by connections to process models.”

The added visibility provided by a connected data and process modeling approach is essential to a Big Data strategy. And there are further indications this approach will soon be (or already is), more crucial than ever before. The Internet of Things (IoT), for example, continues to gain momentum, and with it will come more data, at quicker speeds, from more disparate sources. Businesses will need to adopt this sort of approach to govern how this data is moved and united, and to identify/tackle any security issues that arise.