erwin Expert Blog

Enterprise Architecture vs. Data Architecture vs. Business Process Architecture

Despite the nomenclature, enterprise architecture, data architecture and business process architecture are very different disciplines. Despite this, organizations that combine the disciplines enjoy much greater success in data management.

Both an understanding of the differences between the three and an understanding of how the three work together, has to start with understanding the disciplines individually:

What is Enterprise Architecture?

Enterprise architecture defines the structure and operation of an organization. Its desired outcome is to determine current and future objectives and translate those goals into a blueprint of IT capabilities.

A useful analogy for understanding enterprise architecture is city planning. A city planner devises the blueprint for how a city will come together, and how it will be interacted with. They need to be cognizant of regulations (zoning laws) and understand the current state of city and its infrastructure.

A good city planner means less false starts, less waste and a faster, more efficient carrying out of the project.

In this respect, a good enterprise architect is a lot like a good city planner.

What is Data Architecture?

The Data Management Body of Knowledge (DMBOK), define data architecture as  “specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy.”

So data architecture involves models, policy rules or standards that govern what data is collected and how it is stored, arranged, integrated and used within an organization and its various systems. The desired outcome is enabling stakeholders to see business-critical information regardless of its source and relate to it from their unique perspectives.

There is some crossover between enterprise and data architecture. This is because data architecture is inherently an offshoot of enterprise architecture. Where enterprise architects take a holistic, enterprise-wide view in their duties, data architects tasks are much more refined, and focussed. If an enterprise architect is the city planner, then a data architect is an infrastructure specialist – think plumbers, electricians etc.

For a more in depth look into enterprise architecture vs data architecture, see: The Difference Between Data Architecture and Enterprise Architecture

What is Business Process Architecture?

Business process architecture describes an organization’s business model, strategy, goals and performance metrics.

It provides organizations with a method of representing the elements of their business and how they interact with the aim of aligning people, processes, data, technologies and applications to meet organizational objectives. With it, organizations can paint a real-world picture of how they function, including opportunities to create, improve, harmonize or eliminate processes to improve overall performance and profitability.

Enterprise, Data and Business Process Architecture in Action

A successful data-driven business combines enterprise architecture, data architecture and business process architecture. Integrating these disciplines from the ground up ensures a solid digital foundation on which to build. A strong foundation is necessary because of the amount of data businesses already have to manage. In the last two years, more data has been created than in all of humanity’s history.

And it’s still soaring. Analysts predict that by 2020, we’ll create about 1.7 megabytes of new information every second for every human being on the planet.

While it’s a lot to manage, the potential gains of becoming a data-driven enterprise are too high to ignore. Fortune 1000 companies could potentially net an additional $65 million in income with access to just 10 percent more of their data.

To effectively employ enterprise architecture, data architecture and business process architecture, it’s important to know the differences in how they operate and their desired business outcomes.Enterprise Architecture, Data Architecture and Business Process Architecture

Combining Enterprise, Data and Business Process Architecture for Better Data Management

Historically, these three disciplines have been siloed, without an inherent means of sharing information. Therefore, collaboration between the tools and relevant stakeholders has been difficult.

To truly power a data-driven business, removing these silos is paramount, so as not to limit the potential analysis your organization can carry out. Businesses that understand and adopt this approach will benefit from much better data management when it comes to the ‘3 Vs.’

They’ll be better able to cope with the massive volumes of data a data-driven business will introduce; be better equipped to handle increased velocity of data, processing data accurately and quickly in order to keep time to markets low; and be able to effectively manage data from a growing variety of different sources.

In essence, enabling collaboration between enterprise architecture, data architecture and business process architecture helps an organization manage “any data, anywhere” – or Any2. This all-encompassing view provides the potential for deeper data analysis.

However, attempting to manage all your data without all the necessary tools is like trying to read a book without all the chapters. And trying to manage data with a host of uncollaborative, disparate tools is like trying to read a story with chapters from different books. Clearly neither approach is ideal.

Unifying the disciplines as the foundation for data management provides organizations with the whole ‘data story.’

The importance of getting the whole data story should be very clear considering the aforementioned statistic – Fortune 1000 companies could potentially net an additional $65 million in income with access to just 10 percent more of their data.

Download our eBook, Solving the Enterprise Data Dilemma to learn more about data management tools, particularly enterprise architecture, data architecture and business process architecture, working in tandem.

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Data-Driven Business Transformation: the Data Foundation

In light of data’s prominence in modern business, organizations need to ensure they have a strong data foundation in place.

The ascent of data’s value has been as steep as it is staggering. In 2016, it was suggested that more data would be created in 2017 than in the previous 5000 years of humanity.

But what’s even more shocking is that the peak still not may not even be in sight.

To put its value into context, the five most valuable businesses in the world all deal in data (Alphabet/Google, Amazon, Apple, Facebook and Microsoft). It’s even overtaken oil as the world’s most valuable resource.

Yet, even with data’s value being as high as it is, there’s still a long way to go. Many businesses are still getting to grips with data storage, management and analysis.

Fortune 1000 companies, for example, could earn another $65 million in net income, with access to just 10 percent more of their data (from Data-Driven Business Transformation 2017).

We’re already witnessing the beginnings of this increased potential across various industries. Data-driven businesses such as Airbnb, Uber and Netflix are all dominating, disrupting and revolutionizing their respective sectors.

Interestingly, although they provide very different services for the consumer, the organizations themselves all identify as data companies. This simple change in perception and outlook stresses the importance of data to their business models. For them, data analysis isn’t just an arm of the business… It’s the core.

Data foundation

The dominating data-driven businesses use data to influence almost everything. How decisions are made, how processes could be improved, and where the business should focus its innovation efforts.

However, simply establishing that your business could (and should) be getting more out of data, doesn’t necessarily mean you’re ready to reap the rewards.

In fact, a pre-emptive dive into a data strategy could in fact, slow your digital transformation efforts down. Hurried software investments in response to disruption can lead to teething problems in your strategy’s adoption, and shelfware, wasting time and money.

Additionally, oversights in the strategy’s implementation will stifle the very potential effectiveness you’re hoping to benefit from.

Therefore, when deciding to bolster your data efforts, a great place to start is to consider the ‘three Vs’.

The three Vs

The three Vs of data are volume, variety and velocity. Volume references the amount of data; variety, its different sources; and velocity, the speed in which it must be processed.

When you’re ready to start focusing on the business outcomes that you hope data will provide, you can also stretch those three Vs, to five. The five Vs include the aforementioned, and also acknowledge veracity (confidence in the data’s accuracy) and value, but for now we’ll stick to three.

As discussed, the total amount of data in the world is staggering. But the total data available to any one business can be huge in its own right (depending on the extent of your data strategy).

Unsurprisingly, vast volumes of data are sourced from a vast amount of potential sources. It takes dedicated tools to be processed. Even then, the sources are often disparate, and very unlikely to offer worthwhile insight in a vacuum.

This is why it’s so important to have an assured data foundation upon which to build a data platform on.

A solid data foundation

The Any2 approach is a strategy for housing, sorting and analysing data that aims to be that very foundation on which you build your data strategy.

Shorthand for Any Data, Anywhere, Anycan help clean up the disparate noise, and let businesses drill down on, and effectively analyze the data in order to yield more reliable and informative results.

It’s especially important today, as data sources are becoming increasingly unstructured, and so more difficult to manage.

Big data for example, can consist of click stream data, Internet of Things data, machine data and social media data. The sources need to be rationalized and correlated so they can be analyzed more effectively.

When it comes to actioning an Anyapproach, a fluid relationship between the various data initiative involved is essential. Those being, Data ModelingEnterprise ArchitectureBusiness Process, and Data Governance.

It also requires collaboration, both in between the aforementioned initiatives, and with the wider business to ensure everybody is working towards the same goal.

With a solid data foundation platform in place, your business can really begin to start realizing data’s potential for itself. You also ensure you’re not left behind as new disruptors enter the market, and your competition continues to evolve.

For more data advice and best practices, follow us on Twitter, and LinkedIn to stay up to date with the blog.

For a deeper dive into best practices for data, its benefits, and its applications, get the FREE whitepaper below.

Data-Driven Business Transformation

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Managing Any Data, Anywhere with Any2

The amount of data in the world is staggering. And as more and more organizations adopt digitally orientated business strategies the total keeps climbing. Modern organizations need to be equipped to manage Any2 – any data, anywhere.

Analysts predict that the total amount of data in the world will reach 44 zettabytes by 2020 – one zettabyte = 44 trillion gigabytes. That’s an incredible feat in and of itself. But considering the fact that the total had only reached 4.4 zettabytes in 2013, the rate at which data is collected and stored becomes even more astonishing.

However, it is equally incredible that less than 0.5% of that data is currently analyzed and/or utilized effectively by the business.

What does this mean for business?

Perhaps the most obvious answer is opportunity. You likely wouldn’t be reading this blog if you weren’t at least passively aware of the potential insight that can be derived from a series of ones and zeros.

Start-ups such as Uber, Netflix and Airbnb are perhaps some of the best examples of data’s potential being realized. It’s even more apparent when you consider these three organizations refer to themselves as technology companies, as opposed to the fields their services fall under.

But with data’s potential, potentially open for any business to invest in, action, and benefit from, competition is more fierce than ever, which brings us to what else this new wave of data means for business. That being effective data management.

All of this new data is being created, or even stored, under one manageable umbrella. It’s disparate, it’s noisy, and in its raw form it’s often useless. So to uncover data’s aforementioned potential, businesses must take the necessary steps to “clean it up”.

That’s what the Any2 concept is all about. Allowing businesses to manage, govern and analyse any data, anywhere.

Any2 - Data Management Platform

Any2 – Any Data

The first part of the Any2 equation, pertains to Any Data.

Managing data requires facing the challenges that come with the ‘three Vs of data’: volume, variety and velocity, with volume referring the amount of data, variety to its different sources, and velocity the speed in which it must be processed.

We can stretch these three Vs to five when we include veracity (confidence in the accuracy of the data), and value.

Generally, any data concerns the variety ‘V’, referring to the numbered and disparate potential sources data can be derived from. But as we need to be able to incorporate all of the varying forms of data to accurately analyze it, we can also say any data concerns the volume, and velocity too – especially where Big Data is considered.

Big Data initiatives increase the volume of data businesses have to manage exponentially, and to achieve desired time to market, it must be processed quickly (albeit thoroughly), too.

Additionally, data can be represented as either structured or unstructured.

Traditionally, most data fell under the structured label. Data including business data, relational data, and operational data, for example. And although the different types of data were still disparate, being inherently structured within their own vertical still made them far easier to manage, define, and analyze.

Unstructured data, however, is the polar opposite. It’s inherently messy and it’s hard to define, making both reporting and analysis potentially problematic. This is an issue many businesses face when transitioning to a more data-centric approach to operations.

Big data sources such as click stream data, IoT data, machine data and social media data all fall under this banner. All of these sources need to be rationalized and correlated so they can be analyzed more effectively, and in the same vain as the aforementioned structured data.

Any2 – Anywhere

The anywhere half of the equation is arguably also predominantly focused on the variety ‘V’ – but from a different angle. Anywhere is more concerned with the differing and disparate ways and places in which data can be securely stored, rather than the variety in the data itself.

Although an understanding of where your data is has always been a necessity, it’s now become more relevant than ever. Prior to the adoption of cloud storage and services, data would have to have been managed locally, within the “firewall”.

Businesses would still have to know where the data was saved, and how it could be accessed.

However, the advantages of storing data outside of the business have become more apparent and more widely accepted. This has seen many businesses take the leap and invest in varying capacities, into-cloud based storage and software-as-a-service (SaaS).

Take SAP, for example. SAP provides one solution and one collated database, in favour of a business paying installation and upkeep fees for multiple softwares and databases.

And we still need to consider the uptick in the amount of businesses that buy customer data.

All of this data still has to be integrated, documented and understood in order for it to be useful, as poor management of data can lead to poor results – or, garbage in, garbage out for short.

Therefore, the key focus of the anywhere part of the equation is granting businesses the ability to manage external data at the same level as internal.

Effectively managing data anywhere, requires data modeling, business process and enterprise architecture.

Data modeling is needed to establish what you have whether internal or external, and to identify what that data is.

Business Processes is required to understand how the data should be used and how it best drives the business.

Enterprise Architecture is useful as it allows a business to determine how best to leverage the data to drive value. It’s also needed to ensure the business has a solid enough architecture to allow for this value to come to fruition, and in analyzing/predicting the impact of change, so that value isn’t adversely affected.

So how do we manage Any Data, Anywhere?

The best way to effectively manage Any Data, Anywhere, so that we can ensure investing in data management and analysis adds value, is to consider the ‘3Vs’ in relation to the data timeline. You should also consider the various initiatives (Data Modeling, Enterprise Architecture and Business Process) that can be actioned at each stage to ensure the data is properly processed and understood.

Any2 - Data management platform

Any2 approach helps you:

  • Effectively manage and govern massive volumes of data
  • Consolidate and build applications with hybrid data architectures – traditional + Big Data, cloud and on-premise
  • Support expanding regulatory and legislative requirements: GDPR etc
  • Simplify collaboration and improve alignment with accurate financial and operation information
  • Improve business processes for operational efficiency and compliance standards
  • Empower your people with self-service data access: The right information at the right time to improve corporate decision-making



For more Data Modeling, Enterprise Architecture, and Business Process advice follow us on Twitter and Linkedin to stay updated with the new posts!

Importance of Governing Data

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

Whether a collection of data could be useful to a business, is all just a matter of perspective. We can view data in its raw form like a tangled set of wires, and for them to be useful again, they need to be separated.

We’ve talked before about how Data Modeling, and Enterprise Architecture can make data easier to manage and decipher, but arguably, there’s still a piece of the equation missing.

To make the most out of Big Data, the data must also be rationalized in the context of the business’ processes, where the data is used, by whom, and how. This is what process modeling aims to achieve. Without process modeling, businesses will find it difficult to quantify, and/or prioritize the data from a business perspective – making a truly business outcome-focused approach harder to realize.

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.

Enterprise Data Architecture and Data Governance

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Big Data Benefits, with Enterprise Architecture and Data Modelling

If Gartner’s word is anything to go by, Big Data adoption is seeing an uptick. The analyst cites “increasing inquiries” into Big Data analytics tools, as more businesses look for new opportunities in capturing increasing amounts, or eek more value out of the large amounts of data they already own.

Supporting this, a US-based study into  budgetary plans, indicated that 60% of CIOs believe Big Data will be a ‘top driver’ of IT spending.

Generally speaking, a collective shift in the industry is rarely a coincidence. Trends are usually propped up by a series of concrete benefits, and in the case of Big Data, this is no different.

Companies with a well actioned Big Data strategy can make more well-rounded and informed decisions. One of the key uses of Big Data is to get a better understanding of the market, prospects and customers.

Data is sometimes referred to as the “oil of the 21st century”, and customer data specifically, is arguably the key factor in that. Online and digital business models, and notably Social Media, has opened up a two way dialogue between people and the rest of the world, and provided businesses with an unprecedented level of meaningful data insight.

As a result, businesses now know more about their customers than ever, and this information can be used to earn new ones.

In gaining a better understanding of the market, Big Data can be used to gauge potential market interest. As well as indicating whether a new service or product is worth providing, this information can also help businesses forecast supply with greater accuracy, in relation to demand.

As well as understanding external factors, Big Data can also provide new insights to understand internal operations and process efficiencies. The data can can highlight capabilities and processes that are ripe for improvement, and be used to guide the best course of action to optimization.

Why You Need Enterprise Architecture and Data Modeling

When businesses get it right, Big Data can open a lot of new doors, and allow a business to reach new heights. But simply collecting the data isn’t enough. To return to an aforementioned analogy, much like oil, Big Data isn’t of much use in its raw form. It needs to be refined, and concentrated into something decipherable, and greater than the sum of its parts.

Both data modeling (DM) and enterprise architecture (EA) are essential in making the most out of this refinement process. Data Modelling helps you to analyze the data by providing a contextualized perspective of the information across various platforms. Enterprise Architecture helps you translate and apply data to strategic business and IT objectives. It also aids in indicating which data insights are a priority within your current-state organization and which data will be critical to support your future-state.

This is great news for businesses who already have established a functioning EA and/or DM initiative, but those behind in terms of architecture and modeling will have to find room in the budget for new tools.

In the past, this would have always been a daunting exercise. Encouraging stakeholder investment into EA especially has been notoriously difficult. High, local installation costs and long term contractual commitments are enough to make any business think twice, especially when the business is trying to stay agile. – and this goes doubly for a specialist profession such as EA, where business leaders and stakeholders might not be fully aware of the potential gains.

However, the introduction of Software as a Service based tools has provided the aforementioned apprehensive businesses a new life line. Local installation costs and long term commitments are avoided, in favor of flexibility.

What’s more, integrating enterprise architecture tools with data modeling tools brings significant benefits in alignment of processes and systems.

Enterprise Architecture & Data Modeling White Paper

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Agile Enterprise Architecture for Big Data

Big Data is a huge enabler for business. It provides business leaders and analysts with a depth of information and insight that had previously been impossible to understand.

But for many businesses, this depth isn’t always as inviting as one might hope and so the scope of big data, often becomes a catch 22. Big data’s greatest asset – namely, masses of information – can easily become it’s biggest challenge. Without proper direction, useful information in big data is actually more barren than its name suggests.

Yes, there is a lot of information there, but without the proper approach, sifting through the useful information can undo much of the productivity big data seeks to improve.

This is where Enterprise Architecture comes in …

Enterprise Architecture (EA) helps organizations identify and capitalize on new business opportunities uncovered by this new influx of information, by acting as the guiding rope for the strategic changes required to handle it. EA helps facilitate big data processing, and helps uncover and prioritize exactly which data can benefit the organization.

Enterprise Architecture has already changed a lot over the last decade or so, and architects are now expected to be far more business outcome orientated, and meet disruptions and opportunities head on, rather than acting primarily on optimization and standardization.

With big data, the role of Enterprise Architecture needs revising again. Too much happens too quickly for the old idea of Enterprise Architecture, one that involves carefully perfecting projects and pouring over detail, to still apply. Big data benefits from the “Just Enough” and “Just in Time” approach to EA, and that’s why …

Big Data requires an Agile approach

Big Data is a product of the mass information, digital business age, whereby opportunities are more plentiful, but have much smaller windows in which they can be capitalized upon.

The constantly changing landscape of modern business is directly reflected in big data and EAs will often have to react in real-time as the variables that dictate the data continue to evolve.

David Newman, research vice president at Gartner, spoke on this very topic. “For the EA practitioner, the balance shifts from a focus on optimization and standardization within the organization, to lightweight approaches,” he said.

“Big data disrupts traditional information architectures — from a focus on data warehousing (data storage and compression) toward data pooling (flows, links, and information shareability). In the age of big data, the task for the EA practitioner is clear: Design business outcomes that exploit big data opportunities inside and outside the organization.”

Therefore, just having an Enterprise Architecture initiative isn’t necessarily enough to properly leverage big data. EAs that are yet to focus on agility won’t find as much success as those that have.

One of the key best practices in transitioning to a more Agile EA initiative, and maintaining this Agility is heavily linked with the perception of EA itself. To truly be effective as an agile arm of the business that meets change and disruption head on, EA must step up from building business and IT architecture models to deliver business focused outcomes.

This is something that analysts and influencers all seem to agree on, as many have championed the business outcome approach to Enterprise Architecture now, for some time.

This shift from IT-system focus to business focus, arguably happened when the concept of a Vanguard Enterprise Architect was introduced, making a clear distinction between Foundational EA (responsible for ensuring “business as usual”) and the innovation focussed Vanguard EA.

In fact, Forrester even placed “assisting the business in opportunity recognition” at number one, in their list of ways enterprise architects lead their organization’s thinking.

One way in which Enterprise Architecture can seek to properly leverage big data to recognize new opportunities is by using a business capability map. Business capability maps can make it far easier to extract the relevant data, when the raw data itself is too large to effectively digest.

Enterprise Architecture can also indicate when an organization’s own data isn’t quite big enough. Often, organizations find themselves held back by inter-departmental walls and silos. Enterprise Architecture can help point out these areas where data sharing is lacking, and work on bridging the gap.

This makes the data provided in big data far more complete, and in turn, more useful in the decision making process.

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5 Things You Should Know About Big Data Enterprise Architecture

Big Data has changed the way in which organizations understand and make use of the growing volume, velocity, variety and value of enterprise data. Any company, whether large or small, can take steps to analyze and make use of the disparate information it has access to, speeding up and increasing focus on initiatives that help drive and grow the company.

With the correct approach, enterprise architecture helps the business target the right market activities and fine tune marketing, sales and business operations. In fact, almost any business transformation initiative can be addressed by utilizing Big Data techniques. Techniques that can help enterprise architects ensure alignment with the business and maximize return on investment.

Architects typically already know the business capabilities they need to deliver and have a roadmap outlining the applications, technology, people, processes and resources needed to accomplish it. Big Data is different in that it enables architects to follow ideas where the outcome isn’t clear, and the data is often wont to trigger new questions or ideas.

A more agile approach to architecture development is required to handle this than what many organizations have in place today, to allow the organization to react and respond where needed to capitalize on opportunities when they arise.

With that in mind, here’s 5 key things you should know about Big Data Enterprise Architecture.

Big Data Enterprise Architecture in Digital Transformation and Business Outcomes

Digital Transformation is about businesses embracing today’s culture and process change oriented around the use of technology, whilst remaining focused on customer demands, gaining competitive advantage and growing revenues and profits.

By focusing on desired business outcomes, companies can target specific initiatives that are likely to yield high returns or deliver greatest business value based on digital adoption. Big Data may be incorporated into business strategies to help drive meaningful strategic adjustments that minimize costs and maximize results.

As more businesses become digitized, the amount and complexity of enterprise data grows, and so making use of it to better understand your customers, employees, operations, and how your products and services are performing has never been more challenging or essential. Some ability to understand and analyze Big Data can help identify the opportunities to reduce costs, serve customers better, or eliminate risks across the architecture of the enterprise.

In fact, it could be said that without any element of Big Data analysis, it’s hard to do digital transformation at all.

Enterprise Architecture Makes Big Data Easier to Digest

CRM and ERP tools are a hive of useful data. Enterprise Architects can use this data to highlight areas of opportunity and potential disruption.

Alongside this, the rise of social media has uncovered a new data goldmine, and online tools like Google Analytics provide deep insight into the consumer. Of course, this is implied by the term “Big Data”.

That said, businesses won’t find all of the data useful at any given time. The organization’s current goals and objectives should influence which parts of the data to hone in on in order to make things more manageable.

An Enterprise Architecture tool supporting a view manager can help achieve this. Organizing the same data into different views in an instant can make finding the best data thread to pull, much easier. Essentially, a view manager streamlines data into customizable, and easily digestable representations that can be updated in real-time. This allows Enterprise Architects to make comparisons far more readily.

A best practice in this instance, is to use EA to sift through Big Data, and find one metric that holds a clear influence on reaching your desired outcome. From here, EAs can branch out and find other useful data sets that can be applied to ensure decisions are as well informed as possible.

This can help eliminate guesswork and save time and cost by avoiding trial and error Big Data work.

Big Data Isn’t Just for Big Business

It can be an easy assumption to make that Big Data is best left for Business Analysts, and the typically lager organizations where they’re employed. However, in the current business landscape, its possible for any business to drill down into Big Data by leveraging the various tools available on the market.

These tools can help find, structure and manipulate data, as well as present them to the wider organization in order to influence strategy.

In EA specifically, the tools available can help you gain a deep understanding of your current-state and past-state enterprise data activity, and therefore can be used to help understand trends and make projections that influence your future-state enterprise.

Reports of this nature go along way, for example, by indicating whether a specific Digital Transformation workstream is worth pursuing or not, as well as steering it once the target future-state has been agreed upon.

Big Data Can Help Position EAs in an Advisory Role

A key objective of Big Data is to surface new value from extensive data sets, and as an Enterprise Architect you should be prepared to advise your business and IT stakeholders on how its possible to leverage Big Data techniques to achieve their objectives.

We’ve talked before about how EAs could in fact, be best place to be a front line in advising the CIO, due to their holistic view of the organizations assets and potential.

To properly leverage Big Data to position yourself at the ‘big table’, EAs should recognize that every enterprise is unique with its own goals – the drivers for each company differ, and near-term and long-term goals can and do change over time.

By understanding the business goals, key challenges and business outcomes, Enterprise Architects can start to break Big Data down into insights that will drive success.

The use of SMART (specific, measurable, achievable, realistic, time) based goals can allow you to have concrete criteria upon which to measure results and effectiveness.

Big Data EA and the Business Motivation Model

The business motivation model (BMM) in ArchiMate® can be used to describe the goals, drivers, assessments carried out, and stakeholders involved in decision making. It’s a way of putting factors of influence on the business in context, providing a language in which they can be discussed and used to better strategic planning.

An invaluable tool for Enterprise Architects and the wider business, the motivation model helps improve decision making by adding a structure and cohesion to the strategic planning process.

Most EAs agree that there is still work to be done in order to reach a perfect (or even near perfect) alignment between IT and the wider organization – something that CIOs across organizations are striving for. Much of the reason for this shortcoming, is a lack of effective communication.

The cohesion in planning achieved by a business motivation model, makes it far easier for plans to be communicated across departments and ensure everybody is working towards similar outcomes. This mutual approach is the driver behind this business and IT alignment.

The connection between the BMM, and Big Data Enterprise Architecture is simple. In short, Big Data provides additional and much needed context to build better informed BMMs. The more data you have surrounding a specific influencing factor, the more accurately you can predict the extent of said influencers, influence. Enterprise Architecture can help refine Big Data for this purpose, so analysts and other relevant parties can see a snapshot of only the relevant data, essentially cutting the fat.