Tag: mergers and acquisitions

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erwin Expert Blog Enterprise Architecture

Using Enterprise Architecture for Integration After Mergers and Acquisitions

Because of its holistic view of an organization, enterprise architecture and mergers & acquisitions (M&A) go hand-in-hand.

M&A activity, despite or in light of COVID-19, are on an upswing. The Financial Times reported Google, Amazon, Apple, Facebook and Microsoft have made 19 deals so far this year, according to Refinitiv, the London-based global provider of financial market data. This represents the fastest pace of acquisitions and strategic investments since 2015.

Let’s face it, company mergers, even once approved, can be daunting affairs. Depending on the size of the businesses involved, hundreds of systems and processes need to be accounted for, which can be difficult and often impossible to do in advance.

Following these transactions, businesses typically find themselves with a plethora of duplicate applications and business capabilities that eat into overhead and complicate inter-departmental alignment.

These drawbacks mean businesses have to ensure their systems are fully documented and rationalized. This way the organization can comb through its inventory and make more informed decisions on which systems can and should be cut or phased out, so it can operate closer to peak efficiency and deliver the roadmap to enable the necessary change.

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Enterprise Architecture Needs a Seat at the Table

IT professionals have the inside track about the connection that already exists across applications and data – and they’ll be the ones tasked with carrying out whatever technical requirements are in order post-acquisition.

But despite this, they’re rarely part of M&A tech strategy discussions and the synergy between enterprise architecture and mergers & acquisitions is overlooked. That should change.

With IT leaders involved from the start, they can work with the CFO and COO teams on assessing systems and providing advice on costs that might not otherwise be fully accounted for, such as systems and data integration.

Additionally, by leveraging mergers and acquisitions tools in the beginning, IT can provide a collaborative platform for business and technical stakeholders to get a complete view of their data and quickly visualize and assess what’s in place across companies, as well as what integrations, overlaps or other complexities exist.

This is why enterprise architecture for mergers and acquisitions is essential.

EA helps organizational alignment, providing a business-outcome perspective for IT and guiding transformation. It also helps a business define strategy and models, improving interdepartmental cohesion and communication.

Enterprise Architecture roadmaps can also be leveraged to provide a common focus throughout the company, and if existing roadmaps are in place, they can be modified to fit the new landscape.

EA aids in rooting out duplications in processes and operations, making the business more cost efficient on-the-whole.

Two Approaches to Enterprise Architecture

The Makeshift Approach

The first approach is more common in businesses with either no or a low-maturity enterprise architecture initiative. Smaller businesses often start out with this approach, as their limited operations and systems aren’t enough to justify real EA investment. Instead, businesses opt to repurpose tools they already have, such as the Microsoft Office Suite.

This comes with its advantages that mainly play out on a short-term basis, with the disadvantages only becoming apparent as the EA develops. For a start, the learning curve is typically smaller, as many people are already familiar with software, and the cost per license is relatively low when compared with built-for-purpose EA tools.

These short-term advantages will be eclipsed overtime as the organization’s EA grows. The adhoc Office tools approach to EA requires juggling a number of applications and formats that can stifle effectiveness.

Not only do the operations and systems become too numbered to manage this way, the disparity between formats prevents deep analysis. It also creates more work for the enterprise architect, as the disparate parts of the Office tools must be maintained separately when changes are made, to make sure everything is up to date.

This method also increases the likelihood that data is overlooked as key information is siloed, and it isn’t always clear which data set is behind any given door, disrupting efficiency and time to market.

It isn’t just data that siloed, though. The Office tools approach can isolate the EA department itself, from the wider business as the aforementioned disparities owed to the mis-matching formats can make collaborating with the wider business more difficult.

The Dedicated Approach

As an organization’s enterprise architecture grows, investing in dedicated EA tools becomes a necessity, making the transition just a matter of timing.

With a dedicated enterprise architecture tool, EA management is much easier. The data is all stored in one place, allowing for faster, deeper and more comprehensive analysis and comparison.

See also: Getting Started with Enterprise Architecture Tools

Collaboration also benefits from this approach, as having everything housed under one roof makes it far easier to share with stakeholders, decision-makers, C-level executives and other relevant parties.

Benefits of Enterprise Architecture for Mergers & Acquisitions

While organizational mergers can be fraught with many challenges. they don’t have to be so hard.

Enterprise architecture is essential to successful M&A. EA helps document and manage this complexity, turning all this data into meaningful insights.

It helps alignment by providing a business-outcome perspective for IT and guiding transformation. It also helps define strategy and models, improving interdepartmental cohesion and communication.

Roadmaps can be used to provide a common focus throughout the new company, and if existing roadmaps are in place, they can be modified to fit the new landscape.

erwin Evolve is a full-featured, configurable set of enterprise architecture and business process modeling and analysis tools. Use erwin Evolve to effectively tame complexity, manage change, and increase operational efficiency.

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erwin Expert Blog

The Importance of EA/BP for Mergers and Acquisitions

Over the past few weeks several huge mergers and acquisitions (M&A) have been announced, including Raytheon and United Technologies, the Salesforce acquisition of Tableau and the Merck acquisition of Tilos Therapeutics.

According to collated research and a Harvard Business Review report, the M&A failure rate sits between 70 and 90 percent. Additionally, McKinsey estimates that around 70 percent of mergers do not achieve their expected “revenue synergies.”

Combining two organizations into one is complicated. And following a merger or acquisition, businesses typically find themselves with duplicate applications and business capabilities that are costly and obviously redundant, making alignment difficult.

Enterprise architecture is essential to successful mergers and acquisitions. It helps alignment by providing a business- outcome perspective for IT and guiding transformation. It also helps define strategy and models, improving interdepartmental cohesion and communication. Roadmaps can be used to provide a common focus throughout the new company, and if existing roadmaps are in place, they can be modified to fit the new landscape.

Additionally, an organization must understand both sets of processes being brought to the table. Without business process modeling, this is near impossible.

In an M&A scenario, businesses need to ensure their systems are fully documented and rationalized. This way, they can comb through their inventories to make more informed decisions about which systems to cut or phase out to operate more efficiently and then deliver the roadmap to enable those changes.

Mergers and Acquisitions

Getting Rid of Duplications Duplications

Mergers and acquisitions are daunting. Depending on the size of the businesses, hundreds of systems and processes need to be accounted for, which can be difficult, and even impossible to do in advance.

Enterprise architecture aids in rooting out process and operational duplications, making the new entity more cost efficient. Needless to say, the behind-the-scenes complexities are many and can include discovering that the merging enterprises use the same solution but under different names in different parts of the organizations, for example.

Determinations also may need to be made about whether particular functions, that are expected to become business-critical, have a solid, scalable base to build upon. If an existing application won’t be able to handle the increased data load and processing, then those previously planned investments don’t need to be made.

Gaining business-wide visibility of data and enterprise architecture all within a central repository enables relevant parties across merging companies to work from a single source of information. This provides insights to help determine whether, for example, two equally adept applications of the same nature can continue to be used as the companies merge, because they share common underlying data infrastructures that make it possible for them to interoperate across a single source of synched information.

Or, in another scenario, it may be obvious that it is better to keep only one of the applications because it alone serves as the system of record for what the organization has determined are valuable conceptual data entities in its data model.

At the same time, it can reveal the location of data that might otherwise have been unwittingly discharged with the elimination of an application, enabling it to be moved to a lower-cost storage tier for potential future use.

Knowledge Retention – Avoiding Brain Drain

When employees come and go, as they tend to during mergers and acquisitions, they take critical institutional knowledge with them.

Unlocking knowledge and then putting systems in place to retain that knowledge is one key benefit of business process modeling. Knowledge retention and training has become a pivotal area in which businesses will either succeed or fail.

Different organizations tend to speak different languages. For instance, one company might refer to a customer as “customer,” while another might refer to them as a “client.” Business process modeling is a great way to get everybody in the organization using the same language, referring to things in the same way.

Drawing out this knowledge then allows a centralized and uniform process to be adopted across the company. In any department within any company, individuals and teams develop processes for doing things. Business process modeling extracts all these pieces of information from individuals and teams so they can be turned into centrally adopted processes.

 

[FREE EBOOK] Application Portfolio Management For Mergers & Acquisitions 

 

Ensuring Compliance

Industry and government regulations affect businesses that work in or do business with any number of industries or in specific geographies. Industry-specific regulations in areas like healthcare, pharmaceuticals and financial services have been in place for some time.

Now, broader mandates like the European Union’s Generation Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require 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).

In highly regulated industries like financial services and pharmaceuticals, where mergers and acquisitions activity is frequent, identifying and standardizing business processes meets the scrutiny of regulatory compliance.

Business process modeling makes it easier to document processes, align documentation within document control and learning management systems, and give R&D employees easy access and intuitive navigation so they can find the information they need.

Introducing Business Architecture

Organizations often interchange the terms “business process” and “enterprise architecture” because both are strategic functions with many interdependencies.

However, business process architecture defines the elements of a business and how they interact with the aim of aligning people, processes, data, technologies and applications. Enterprise architecture defines the structure and operation of an organization with the purpose of determining how it can achieve its current and future objectives most effectively, translating those goals into a blueprint of IT capabilities.

Although both disciplines seek to achieve the organization’s desired outcomes, both have largely operated in silos.

To learn more about how erwin provides modeling and analysis software to support both business process and enterprise architecture practices and enable their broader collaboration, click here.

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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.

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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.

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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.

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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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erwin Expert Blog

Six Reasons Business Glossary Management Is Crucial to Data Governance

A business glossary is crucial to any data governance strategy, yet it is often overlooked.

Consider this – no one likes unpleasant surprises, especially in business. So when it comes to objectively understanding what’s happening from the top of the sales funnel to the bottom line of finance, everyone wants – and needs – to trust the data they have.

That’s why you can’t underestimate the importance of a business glossary. Sometimes the business folks say IT or marketing speaks a different language. Or in the case of mergers and acquisitions, different companies call the same thing something else.

A business glossary solves this complexity by creating a common business vocabulary. Regardless of the industry you’re in or the type of data initiative you’re undertaking, the ability for an organization to have a unified, common language is a key component of data governance, ensuring you can trust your data.

Are we speaking the same language?

How can two reports show different results for the same region? A quick analysis of invoices will likely reveal that some of the data fed into the report wasn’t based on a clear understanding of business terms.

Business Glossary Management is Crucial to Data Governance

In such embarrassing scenarios, a business glossary and its ongoing management has obvious significance. And with the complexity of today’s business environment, organizations need the right solution to make sense out of their data and govern it properly.

Here are six reasons a business glossary is vital to data governance:

  1. Bridging the gap between Business & IT

A sound data governance initiative bridges the gap between the business and IT. By understanding the underlying metadata associated with business terms and the associated data lineage, a business glossary helps bridge this gap to deliver greater value to the organization.

  1. Integrated search

The biggest appeal of business glossary management is that it helps establish relationships between business terms to drive data governance across the entire organization. A good business glossary should provide an integrated search feature that can find context-specific results, such as business terms, definitions, technical metadata, KPIs and process areas.

  1. The ability to capture business terms and all associated artifacts

What good is a business term if it can’t be associated with other business terms and KPIs? Capturing relationships between business terms as well as between technical and business entities is essential in today’s regulatory and compliance-conscious environment. A business glossary defines the relationship between the business terms and their underlying metadata for faster analysis and enhanced decision-making.

  1. Integrated project management and workflow

When the business and cross-functional teams operate in silos, users start defining business terms according to their own preferences rather than following standard policies and best practices. To be effective, a business glossary should enable a collaborative workflow management and approval process so stakeholders have visibility with established data governance roles and responsibilities. With this ability, business glossary users can provide input during the entire data definition process prior to publication.

  1. The ability to publish business terms

Successful businesses not only capture business terms and their definitions, they also publish them so that the business-at-large can access it. Business glossary users, who are typically members of the data governance team, should be assigned roles for creating, editing, approving and publishing business glossary content. A workflow feature will show which users are assigned which roles, including those with publishing permissions.

After initial publication, business glossary content can be revised and republished on an ongoing basis, based on the needs of your enterprise.

  1. End-to-end traceability

Capturing business terms and establishing relationships are key to glossary management. However, it is far from a complete solution without traceability. A good business glossary can help generate enterprise-level traceability in the form of mind maps or tabular reports to the business community once relationships have been established.

Business Glossary, the Heart of Data Governance

With a business glossary at the heart of your regulatory compliance and data governance initiatives, you can help break down organizational and technical silos for data visibility, context, control and collaboration across domains. It ensures that you can trust your data.

Plus, you can unify the people, processes and systems that manage and protect data through consistent exchange, understanding and processing to increase quality and trust.

By building a glossary of business terms in taxonomies with synonyms, acronyms and relationships, and publishing approved standards and prioritizing them, you can map data in all its forms to the central catalog of data elements.

That answers the vital question of “where is our data?” Then you can understand who and what is using your data to ensure adherence to usage standards and rules.

Value of Data Intelligence IDC Report

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erwin Expert Blog

Why Enterprise Architecture is Essential in Facilitating Mergers

Company mergers, even once approved, can often be daunting affairs. Depending on the size of the business, there can be hundreds of systems and processes that need to be accounted for, which can be difficult, and even impossible to do in advance.

Therefore, following a merger, businesses typically find themselves with a plethora of duplicate applications and business capabilities that eat into overheads, and make inter-departmental alignment difficult.

These drawbacks mean businesses have to ensure their systems are fully documented and rationalized. This way, the organization can comb through their inventory and make more informed decisions on which systems can and should be cut or phased out, in order for the business to operate closer to peak efficiency and deliver the roadmap to enable that change.

This is why enterprise architecture (EA) is essential in facilitating company mergers.

EA helps a business’ alignment throughout the organization, providing a business outcome perspective for IT, and guiding transformation. It also helps a business define strategy and models, improving interdepartmental cohesion and communication. Roadmaps can be leveraged in order to provide a common focus throughout the company, and if existing roadmaps are in place, they can be modified in order to fit the new landscape.

Finally, as alluded to above, EA will aid in rooting out duplications in process and operations, making the business more cost efficient on the whole.

The Two Approaches

The makeshift approach:

The first approach is more common in businesses with either no, or a low maturity enterprise architecture initiative. Smaller businesses often start out with this approach, as their limited operations and systems aren’t enough to justify real EA investment. Instead, businesses opt to repurpose tools they already have, such as the Office Suite.

This comes with it’s advantages that mainly play out on a short term basis, with the disadvantages only becoming apparent as the EA develops. For a start, the learning curve is typically smaller, as many people are already familiar with software, and the cost per license is relatively low when compared with built-for-purpose EA tools.

But as alluded to earlier, these short term advantages will be eclipsed overtime as the organizations EA grows.  The adhoc, Office Tools approach to EA requires juggling a number of applications and formats, that can stifle its effectiveness. Not only do the operations and systems become too numbered to manage this way, the disparity between formats stops a business from performing any deep analysis. It also creates more work for the Enterprise Architect, as the disparate parts of the Office Tools must be maintained separately when changes are made, in order to make sure everything is up to date.

This method also increases the likelihood that data is overlooked as key information is siloed, and it isn’t always clear which data set is behind any given door, disrupting efficiency and time to market. It isn’t just data that siloed, though. The Office Tools approach can isolate the EA department itself, from the wider business. The aforementioned disparities aided to the mis-matching formats can make collaborating with the wider business more difficult.

The EA tool approach:

In essence, the EA tool approach is the polar opposite to Office Tools based EA. The disadvantages of implementing a dedicated EA tool tend to be uncovered in the short term. Such disadvantages include the cost and ease (or lack thereof) of installation.

But as an organization’s Enterprise Architecture grows, investing in dedicated EA tools becomes a necessity, making the transition just a matter of timing.

When implemented though, management of an organization’s EA becomes much easier. The data is all stored in one place, allowing for faster, deeper, and more comprehensive analysis and comparison. Collaboration also benefits from this approach, as having everything housed under one roof makes it far easier to share with stakeholders, decision makers, C-Level executives and other relevant parties.

I’ve Decided I Need an EA Tool, But What About the Cost?

Considering all of this, the up side to investing in dedicated EA tools become more apparent. A dedicated EA tool will help an organization achieve the benefits of enterprise architecture to their full extent. Some organizations may still have reservations about cost, but thanks to SaaS-based EA offerings, the financial and time costs of implementing a new EA tool are minimized too.

The SaaS approach eliminates initial installation costs in favor of a more affordable, less binding, agility enabling pricing plan. This decreases the likelihood that the investment will become another piece of expensive shelfware. There are other benefits to the SaaS model too, including more frequent and less intrusive updates, and a global EA that’s updated for everybody in real time, and is accessible to all approved parties from anywhere in the world – as long as there’s an internet connection.