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Managing Any Data, Anywhere with Any2

Any2 - Data Management Platform

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

 

 

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Importance of Governing Data

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Data management tools essential for building the data foundation platform

Instead of utilizing built for purpose data management tools, businesses in the early stages of a data strategy often leverage pre existing, make-shift software.

However, the rate in which modern businesses create and store data, means these methods can be quickly outgrown.

In our last post, we looked at why any business with current, or future plans for a data-driven strategy need to ensure a strong data foundation is in place.

Without this, the insight provided by data can often be incomplete and misleading. This negates many of the benefits data strategies are typically implemented to find, and can cause problems down the line such as slowing down time to markets; increasing the potential for missteps and false starts; and above all else, adding to costs.

Leveraging a combination of data management tools, including data modeling, enterprise architecture and business processes can ensure the data foundations are strong, and analysis going forward is as accurate as possible.

For a breakdown of each discipline, how they fit together, and why they’re better together, read on below:

This post is part two of a two part series. For part 1, see here.

Data management tools for an agile data foundation.

Data Modeling

Effective Data Modeling helps an organization catalogue, and standardize data, making the data more consistent and easier to digest and comprehend. It can provide direction for a systems strategy and aid in data analysis when developing new databases.

The value in the former is that it can indicate what kind of data should influence business processes, while the latter helps an organization find exactly what data they have available to them and categorize it.

In the modern world, data is a valuable resource, and so active data modeling in order to manage data, can reveal new threads of useful information. It gives businesses a way to query their databases for more refined and targeted analysis. Without an effective data model, insightful data can quite easily be overlooked.

Data modeling also helps organizations break down data silos. Typically, much of the data an organization possesses is kept on disparate systems and thus, making meaningful connections between them can be difficult. Data modeling serves to ease the integration of these systems, adding a new layer of depth to analysis.

Additionally, data modeling makes collaborating easier. As a rigorous and visual form of documentation, it can break down complexity and provide an organization with a defined framework, making communicating and sharing information about the business and its operations more straightforward.

Enterprise Architecture

Enterprise Architecture (EA) is a form of strategic planning used to map a businesses current capabilities, and determine the best course of action to achieve the ideal future state vision for the organization.

It typically straddles two key responsibilities. Those being ‘foundational’ enterprise architecture, and ‘vanguard’ enterprise architecture. Foundational EA tends to be more focused on the short term and is essentially implemented to govern ‘legacy IT’ tasks. The tasks we colloquially refer to as ‘keeping on the lights’.

It benefits a business by ensuring things like duplications in process, redundant processes, and unaccounted for systems and shelfware don’t cost the business time and money.

Vanguard enterprise architects tend to work with the long term vision in mind, and are expected to innovate to find the business new ways of reaching their future state objectives that could be more efficient than the current strategy.

It’s value to a business becomes more readily apparent when it enterprise architects operate in terms of business outcomes, and include better alignment of IT and the wider business; better strategic planning by adding transparency to the strategy, allowing the whole business to align behind, and work towards the future objective; and a healthier approach to risk, as the value (reward) in relation to the risk can be more accurately established.

Business Process

Business process solutions help leadership, operations and IT understand the complexities of their organizations in order to make better, more informed and intelligent opinions.

There are a number of factors that can influence an organization who had been making it by without a business process solution, to implement the initiative. Including strategic initiatives – like business transformation, mergers and acquisitions and business expansion; compliance & audits – such as new/changing industry regulations, government legislation and internal policies; and process improvement – enhancing financial performance, lowering operating costs and polishing the customer experience.

We can also look at the need for business process solutions from the perspective of challenges it can help overcome. Challenges including the complexities of a large organization and international workforces; confusion born of undefined and undocumented processes as well as outdated and redundant ones; competitor driven market disruption; and managing change.

Business process solutions aim to tackle these issues by allowing an organization to do the following:-

  • Establish processes where they don’t exist
  • Document processes that exist but aren’t consistently followed
  • Examine/analyze/improve/eliminate processes that don’t work
  • Optimize processes that take too long, cost too much or don’t make sense
  • Harmonize redundant processes across the organization.
  • Construct processes for new products, markets and organizations
  • Disrupt processes with new technology and data assets.

The Complete, Agile Foundation for the Data-Driven Enterprise.

As with data, these three examples of data management tools also benefit from a more fluent relationship, and for a long time, industry professionals have hoped for a more comprehensive approach. With DM, EA and BP tools that work in tandem with, and complement one another inherently.

It’s a request that makes sense too, as although all three data management tools are essential in their own right, they all influence one another.

We can look at acquiring, storing and analyzing data, then creating a strategy from that analysis’ as separate acts, or chapters. And when we bring the whole process together, under one suite, we effectively have the whole ‘Data Story’ available to us in a format we can analyze and inspect as a whole.

 

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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.

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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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Why Enterprise Architecture Agility is Becoming Increasingly Important

The importance of enterprise architecture agility is growing at a rapid pace.

Technology has made us faster. In the last century, we’ve found ways to travel faster, exchange information faster, and most importantly – innovate faster. In fact, there’s an argument to be made that perhaps we’ve entered an age where we innovate too quickly.

Think about it – how can any company aim for longevity when the next industry revolutionizing innovation is just around the corner. And it seems as if there’s a new one every other month.

Start-ups like Uber, Deliveroo and others have already reshaped their respective industries and largely without much – if any at all – forewarning. These disruptions to the market can rarely be planned for. Much of this is down to the nature of the internet and how quickly a message can spread.

Now that more and more products are being offered in the form of software, and within that, more and more of those being SaaS offerings, it’s not hard to imagine yet another uptick in the speed in which the market changes and evolves.

This is a particularly important area of focus for Enterprise Architects, as they are largely going to be responsible for implementing and integrating these new technologies. And with the evolution of Enterprise Architecture itself, from fringe support IT role to a forward thinking source of innovation, EA’s will even be responsible for sourcing new tech, predicting market trends, and generally operating with the businesses future-state in mind, in order to stay ahead of the curve.

So how do you account for rapid and unforeseen changes in strategic planning?

Well, the short answer is that you don’t. Realistically, you can’t plan for something if you don’t know…

  1. What’s coming
  2. When it’s coming

Businesses must instead prepare to react. This is what’s at the core of “being agile”, – a business mantra and way of operating that has been championed by many of the leading analysts, including Gartner, Forrester and others for a number of years now.

The point in being agile is understanding that you’re not always going to be ahead of the curve. This is because it’s impossible to constantly live “outside the box”. Eventually, an outside of the box idea becomes the box, and it takes a new outsider to really pivot the market.

Agility then, is about making sure that when disruptions occur, you’re not only ready to change course (or in some extreme cases, radically pivot), you’re also ready to turn disruption into opportunity.

What does agility mean to Enterprise Architecture?

In Enterprise Architecture, agility is mainly about reducing time to markets and making the practice more efficient.

There are a number of ways in which this can be achieved. Some relate to how EA is done, and some relate to how EA is perceived by the wider business.

Firstly, when it comes to doing EA, one best practice is the “just enough” mantra. Just enough Enterprise Architecture aims to avoid the issues that can plague an EA initiative due to over analysis.

Enterprise Architecture is one of those disciplines where it could be argued that the work is never done – but that doesn’t mean that there isn’t a time to stop and move on. Instead of documenting every inch and detail of an organization, EAs should instead aim to to deliver just enough to support the desired business outcomes.

A failure to implement just enough Enterprise Architecture plays a huge part in an organization’s failings in staying agile. It takes the EAs focus away from the businesses future-state, and instead, bogs them down with the past and current.

Of course, it’s vital for an EA to understand a business’s current capabilities, but looking forward is a huge part of the balance that makes a successful Enterprise Architecture effort.

If you feel your Enterprise Architecture efforts get bogged down with “analysis paralysis,” you should try using a goal based decision process. This keeps a focus on what needs to be built and by when.

Another reason an Enterprise Architecture department might be struggling to increase agility is the lack of a purpose built EA tool. As Enterprise Architecture has traditionally been quite expensive to get off the ground – with installation, upkeep and other costs – many businesses have resorted to using a hodgepodge of not-for-purpose Office Tools.

It’s a viable option at first, but as the Enterprise Architecture grows, these guerrilla Enterprise Architecture efforts often become increasingly difficult to manage as the architecture’s documentation spills across multiple files that can be exclusive to multiple people.

A purpose built EA tool with a strong focus on collaboration means that Enterprise Architects can share and collaborate on work directly within the tool. It also makes the architecture as a whole far easier to navigate and present to the wider business as it’s all kept within the same programme.

The improved capabilities in collaboration can also help with EA’s perception in the wider business. Getting stakeholders more involved in the EA makes its purpose and value far more clear.

This will lead to an environment where EA and the results it produces are treated with more dignity and trust, aiding in increasing agility by removing some of the bureaucratic hurdles faced before.

It’s now easier than ever to kickstart an Enterprise Architecture initiative with an EA tool. SaaS offerings with staggered license types especially, can be an in for businesses starting at Level 0 on the EA maturity model. Businesses no longer need to worry about the massive up front cost, and with the option to buy more licenses, the tool can scale with the EA initiative as it develops.

enterprise architecture business process

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Enterprise Architecture’s Economic Value

In the past, the economic value of enterprise architecture has been hard to show.

Yearly surveys routinely indicate a need for enterprise architecture (EA). CIOs often list implementing or improving an EA initiative as a top priority. Despite their position at the “top table”, CIOs are still expected to justify their plans to invest in EA (or elsewhere), based on the plan’s expected effectiveness.

In theory, the benefits of enterprise architecture should justify themselves. But the niche and expert nature of the practice means the value doesn’t always translate well to those outside of the immediate stakeholder community.

In this case, CIOs, Chief Architects, EAs and the like, need to show the economic value of enterprise architecture. But how?

The Economic Value of Enterprise Architecture

The Economic Value of Enterprise Architecture

The need for enterprise architecture can be summed up by two of its main goals – aligning the business and its operations with IT, and bridging the gap between the organization’s current state, and its desired future state.

The economic value of enterprise architecture often comes as a result of nearing the ideal state of these two goals. I say nearing, as Enterprise Architecture initiatives rarely achieve perfect alignment. The two goals work against each other in this regard – success in bridging the current/future gap, creates a constantly changing landscape, and business/IT alignment has to be adjusted accordingly.

With this in mind, it could be said that the economic value of enterprise architecture is achieved in the long term, as opposed to the shorter term. That said, there are a number of markers of success that can be achieved along the way – each providing clear and tangible benefits to the organization that undoubtedly hold economic value of their own merit.

There are a number of indicators of success within EA that indicate the initiative’s economic value. Four core indicators come in the form of improving strategic planning, communication and risk evaluation, and tactical advancements. These markers can be seen as best practices in order to work towards, to fufil the overall goal of making an EA initiative an economic success.

Improving Strategic Planning

Enterprise architecture is often seen as the bridge between defining a strategy and its implementation – hence one of EAs main goals being to bridge the gap between the business’ current state, and its future.

EA adds a much needed dimension of transparency to strategy implementation. It’s often the guiding rope for implementing strategy that will affect the whole business. Because different business departments often work in silos that aren’t all completely in sync, new initiatives can suffer from a lack of foresight and lead to disparity and disconnections in data and ideas.

Enterprise architecture works as a framework to ensure no department/silo is overlooked, and that with the new strategy, each separate business arm is still working towards the same goal.

Bettering Communication

Enterprise architecture is arguably concerned with strategic planning, first and foremost. But there will always come a time when that strategy has to be communicated to the wider business for it to be successfully implemented.

The problem most businesses will find here, is that due to the holistic, top down, and all encompassing perspective EA has on the business, and the universal/inter-departmental changes any strategy EAs suggest can cause.

This is where the right enterprise architecture tool is important. Rather than just the actioning of enterprise architecture itself.

The right enterprise architecture tool can enable the various stakeholders relevant to a proposed scheme, to actually collaborate on the project to ensure the strategy works in the best interest of all parties.

In the past, enterprise architecture has been deemed as an “ivory tower” profession, catering only to the expert. This is still true to some extent, especially when talking about back end data and the repository. However, that doesn’t mean the results at the front end aren’t useful to non Enterprise Architects.

The right tool can enable true collaboration (in tool, not just reports and feedback which can slow down the process) and therefore be a great asset to line managers, C-Level executives and others as they can be a more critical part of the planning process.

All in all, this facilitating of true collaboration should improve the communication and coordination of strategy implementation, and lead to less false starts, wrong turns and a return on investment that’s both faster and more fruitful.

Tactical Improvements

As well as improving the strategic planning process, enterprise architecture plays a huge role in improving processes overall. By taking a look into what is aligned, and what isn’t, EAs can uncover areas of redundancies – where two separate processes are being actioned when they could be merged into one.

There are many examples of this across varying sectors, especially when an organization has been void of EA until now, or is on the lower end of EA maturity. These businesses tend to be less aligned and so suffer from the issues typical to such situations. These issues can range from a non standardized practice for keeping and labeling data, leading to duplication and corruption, to different departments holding separate licenses for software that does essentially the same thing.

By identifying these discrepancies, enterprise architects can save an organization both time and money. Both of which hold clear economic value.

Taking Better Risks

Modern enterprise architecture is often seen as a two headed coin. One side, the Foundational side, deals with the ‘legacy’ IT-based tasks – what we refer to as keeping the lights on, keeping costs down etc. The tactical value of enterprise architecture resonates most vibrantly here.

Vanguard enterprise architecture is the other side of this coin. This more modern take on enterprise architecture was introduced to reflect ITs shift from a solely support based domain, to a role more central to the business.

Vanguard EAs are the forward thinkers, more concerned with innovating, and bridging current and future gaps in technology, than the alignment in the present. In this regard, enterprise architecture becomes a fantastic tool for evaluating risk.

Although evaluating risk can never be seen as an exact science, vanguard EAs are invaluable in that they can help indicate what strategies should, or should not be pursued based on their potential value to the business, and the costs it could take to implement them.

The Business Capability Model, for example, can indicate what a business is already suited to achieve. Therefore, they can be used to point out strategies the business might be able to implement more readily.

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Enterprise Architecture erwin Expert Blog

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.

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erwin Expert Blog

How To Kickstart an Enterprise Architecture Initiative

The increased rate in technology disruptions means more businesses than ever will be looking to start up an Enterprise Architecture (EA) initiative. This has extended to smaller businesses too – many of which wouldn’t have considered EA before – due to the availability of more accessible tools.

But where to start? Previously, looks into Enterprise Architecture tooling uncovered two alternatives. The legacy, established EA tools, high in productivity but equally high in cost and effort, and the drawing tools – such as Visio – that require excessive management and juggling of information between multiple applications.

start enterprise architecture initiative

First Steps in Enterprise Architecture

It’s important to establish what Enterprise Architecture is for, and why you would need to get started. Superficially, Enterprise Architecture is about the description and management of systems. In the past, it had been typified as a domain fit for “keeping the lights on” – the legacy attributes associated with IT. Today, largely due to the rise in digital business, the role of an Enterprise Architect has been pulled out of the shadows.

Now, Enterprise Architecture is more concerned with responding to disruption. EAs identify the potential for business and IT change, and analyze how best to execute or manage such changes to meet business objectives, desired outcomes and vision.

The Beginnings of Enterprise Architecture Maturity

Chances are, if you’re just starting out in EA, you’ll be ‘pre level 1’ on the maturity model. To take that first step into maturing the practice, and before you really start working on what your future state architecture looks like, you must understand your current state.

The frameworks to which your Enterprise Architecture will observe are also decided here. TOGAF, Zachman, DODAF are three common examples of such frameworks.

Your choice will ultimately come down to what you’re trying to achieve with EA, the experience of your team, and whether you prefer a defined model.

To reach full maturity, your EA initiative will have to transition from ad-hoc, reactive EA, to something repeatable, and so a defined model best suits this long term aim. Typically speaking, the TOGAF method is most widely adopted.

Enterprise Architecture Maturity

That said, some organizations won’t want a standard method, and may start with a few object types and relationships.  What matters most is that its understandable by the organization and that its repeatable.

At this point, you can move onto ‘Level 1’ maturity, carrying out ‘horizontal assessments’ of the business.

This is essentially ‘cleaning up’, sorting the data, resources etc., involved in the architecture of the enterprise, in order to build upon strong foundations going forward.

Any existing architectures should be united under one model, and one standard of terminology.

Disparity in this area can cause problems down the line, as it’s often common for different departments to have different names for the same thing and vice-versa, leading to duplicated or inaccurate data.

This is the point whereby existing data is imported into the EA repository. This should identify what the business has, and allow for modeling, roadmapping etc. going forward.

A best practice at this stage, is to start by creating a metamodel. Metamodels provide the top down, abstract view of the business and help EA’s establish alignment.

For example, they show the relationships between applications, and the business process they support, and a lack of such relationships and connections indicates areas of the architecture that aren’t properly aligned and non-functioning.

What Do I Need to Start An Enterprise Architecture Practice?

Most businesses start small, and mature the practice as they go, but some businesses with deeper resources opt to buy their way through Enterprise Architecture maturity – hiring consultants and acquiring a heavy-weight EA tool right off the bat.

For businesses where this isn’t an option, the usual process is as follows…

Typically, lower maturity EA set-ups feature a small team, an ad-hoc approach, and free and repurposed tools.

But as these EA practices grow, architects, stakeholders and business leaders often find that this approach can quite quickly become difficult to maintain.

Managing something as complex as an Enterprise’s applications, technology, information and processes across a suite of different tools that don’t interact with each other is hard enough as it is.

Then, when you factor in a growing team size, and add collaboration into the mix, this method can become nigh on impossible to sustain. sooner or later, an EA tool is required.

For more on deciding the best time to acquire a tool, click here.

How Much Do Enterprise Architecture Tools Cost?

Many companies think they can’t justify the costs, lengthy implementations projects and other resourcing factors. However, with the emergence of the Cloud and Software as a Service (SaaS) model, much of these initial headaches can be avoided. SaaS pricing models usually work on a “pay for what you need” basis, reducing the threat of a new tool purchase becoming shelfware. This makes them a great option for businesses who are looking to introduce an Enterprise Architecture programme for a specific initiative.

The SaaS model is also great for businesses that are looking to get off the ground quickly. The burden of having to pay for a consultant or technician to install the tool locally is relieved – saving both time and money.

We’ve seen a shift towards the model from leading tech providers recently. More and more enterprise tools are moving to the Cloud. One prime example is Google and their Apps for Work. Not only does the model save time and money, it can also greatly improve collaboration. Google’s suite demonstrates this, by allowing users to work together on a document or project remotely. Considering the nature of Enterprise Architecture – it’s top down view of the organization, and its inter-departmental linking of systems, people and other resources – this enabling of collaboration is vastly important to the industry.

Start enterprise architecture - Free Enterprise Architecture & Data Modeling White Paper

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Enterprise Architecture erwin Expert Blog

How Agile Enterprise Architecture is Helping to Build Smart Cities

Having an agile enterprise architecture is essential to enabling a smart city.

One of the challenges when creating a smart city is how to represent all the moving parts. From my perspective as KnowNowCities designs new smart places the best approach is to use agile enterprise architecture techniques to describe how the technology is applied in a place.

What is a Smart City

Firstly though, let’s start by explaining what a smart city is. The elements of a smart city can be boiled down into 5 interwoven and parallel components.

These are:

  1. Apply standards – such as Privacy by Design and PAS181 – Smart City Framework
  2. Are Open Data Centric – see the Open Data Institute
  3. Are Interoperable – so data and services from one place can interact with another place
  4. Have good governance and leadership (both hard and soft)
  5. Are Outcomes Centric and put Citizens first. Citizens make cities what they are

In short these become the overriding Smart City principles. So a key role of any enterprise architecture is to demonstrate how the technology designed meets these principles.

agile enterprise architecture smart city

Smart City Enterprise Architecture

The role of an EA in a smart city is multiple. Typically when building a new place the overriding concern is maintaining the promised return on investment. The developer needs a place that will attract customers (good rental or freehold returns); be compelling and competitive so the new place has its own ‘unique selling points’; yet, not break the bank! The EA can aid the developer in what I term is a “plan for tomorrow, but build for today” approach.

What does this mean? Planning for tomorrow is about making sure where it is going to be expensive to retrofit in the future mitigate that expense by deploying sufficient capacity for the future where it makes sense.

Simply put, this is about the reservation and allocation of space and capacity where technology may well end up being deployed. But not necessarily deploying that technology.

An example would be deploying conduit underground during the civil engineering phase of a build, but not deploying the cable through the conduit. Provide the access rights and access points to deploy any technology upgrades (be it for technology refresh or for capacity gains).

Another key aspect when designing a smart city is to recognize that technology is now part of the urban design. Yet traditional architecture of buildings and the public realm does not easily address the adoption of technology. The key here is to be embedded in the architecture and construction team from day one. As I tell my clients… technology engineers for a smart city should be considered similarly to the water engineers.

With Water you have three types (blue – potable, grey – rain/surface run off & black – waste) that each require their own special attention. When you think water… now also think technology!

Integrate with the RIBA Plan of Work

Luckily from a technology perspective having a plan of work that goes through distinct phases also fits nicely with an Enterprise Architecture centric method too. The RIBA Plan of Work method can be applied to the technology aspects of a smart city as much as it can the construction of the buildings and public realm too. So at concept stage, the technology concept architecture can sit in that phase too.

Roadmaps are the icing on the cake however. Referring back to the principle of design for tomorrow but deliver for today. It is possible to show when a particular technology component is required, how it flows from previous technology deployed/enabled and how this then supports future technology growth/deployments.

Additionally, because the EA at a concept level is exactly that, a certain amount of stability can be expected. Let us not forget that technology has a lifespan often measured in months, whereas a structure’s lifespan is measured in decades. So the EA becomes a living organic entity. Constantly evolving and changing based on need and technological capability.

Yet it is the specified and physical designs that are likely to change here. Again, the EA approach allows for this transfer of different types of technology, yet still keeping a cohesive overall architecture that can still represent the principles and concept architecture the first design delivered.

How Does erwin Evolve help?

The beauty of using erwin Evolve is that the tool provides all the outputs that are required in a smart city engagement.

Firstly, it is great way of capturing the requirements and then matching those to principles, owners and then attributing this to architected components.

Secondly, the tool can manage capacity plans, and what if scenario planning.

Thirdly, using the roadmaps and Kanban views the tool can help a client prioritize and plan the work to be undertaken.

Finally, because of erwin Evolve’s collaborative features I get to share a common view with all project stakeholders.

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