Author: Bunny Tharpe

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Enterprise Architecture for the Cloud – Get Ahead in the Cloud

It’s almost 2018, so there’s a good chance a portion of your business relies on the cloud. You’ve either moved some legacy systems there already, or you’re adopting entirely new, cloud-based systems – or both. But what about enterprise architecture for the cloud? After all, it’s the glue that helps tie all your disparate IT threads together.

The transition to the cloud is owed heavily to improvements in its technology foundation, especially increased security and safeguards. For the likes of governments, banks and defense organizations, these enhancements have been paramount.

Additionally, organizations across industry increasingly turn to the cloud to keep costs low and maximize profits. These options are usually easier and cheaper to install than their on-premise counterparts.

A 2016 RightScale study found that 31 percent of the 1,060 IT professionals surveyed said their companies run more than 1,000 virtual machines. This demonstrates a sizeable uptick compared to 2015, when only 22 percent of participants answered the same.

The rate of adoption is even more impressive when you consider how forecasts have been outpaced. In 2014, Forrester predicted the cloud market would be worth $191 billion by 2020. In 2016, this estimate was revised to $236 billion.

The cloud is big business.

Enterprise architecture for the cloud

Why Enterprise Architecture for the Cloud?

We’ve established the case for the growing cloud market. So why is enterprise architecture for the cloud so important? To answer that question, you have to consider why the cloud market is so expansive.

In short, the answer is competition. Although there are some colossal, main players in the cloud market – Amazon Web Services (AWS), Azure, Google Cloud Platform and IBM make up more than an estimated 60% – they act as hosts to smaller cloud businesses spanning copious industries.

Unlike the hosts, this layer of the cloud market is incredibly and increasingly saturated. Such saturation is due to an even more complex web of disparate systems for which a business must account.

And said business ­must account for these systems to establish what it has right now, what it needs to reach the organization’s future state objectives, and what systems can be integrated for the sake of efficiency.

If enterprise architecture (EA) isn’t actioned in bridging the gap between an organization’s current state and its desired future state, then it’s fundamentally underperforming.

Enterprise Architecture for Introducing Cloud Systems

The above details how EA benefits a business in managing its current cloud-based systems. The primary benefit being the ability to see how and where the newer, disparate cloud systems fit with legacy systems.

But EA’s usefulness doesn’t start once cloud systems are already in place. As established, a key objective of EA is to bridge an organization’s current and future state objectives.

Another key objective is to better align an organization with IT for better preparation in the face of change. In this way, EA enables organizations to make or face enterprise changes with minimal disruptions and costs.

Cloud systems have disrupted how businesses operate already.

And with competition in the cloud space as populated as it is today, more disruption is coming. Therefore, having well-executed EA will make it easier for businesses to manage such inevitable disruption. It will also enable organizations undergoing digital transformation to implement new cloud systems with less friction.

Data-Driven Business Transformation

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Every Company Requires Data Governance and Here’s Why

With GDPR regulations imminent, businesses need to ensure they have a handle on data governance.

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Data Modeling is Changing – Time to Make NoSQL Technology a Priority

Make NoSQL technology a priority

As the amount of data enterprises are tasked with managing increases, the benefits of NoSQL technology are becoming more apparent. 

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NoSQL Database Adoption Is Poised to Explode

NoSQL database technology is gaining a lot of traction across industry. So what is it, and why is it increasing in use?

Techopedia defines NoSQL as “a class of database management systems (DBMS) that do not follow all of the rules of a relational DBMS and cannot use traditional SQL to query data.”

The rise of the NoSQL database

The rise of NoSQL can be attributed to the limitations of its predecessor. SQL databases were not conceived with today’s vast amount of data and storage requirements in mind.

Businesses, especially those with digital business models, are choosing to adopt NoSQL to help manage “the three Vs” of Big Data: increased volume, variety and velocity. Velocity in particular is driving NoSQL adoption because of the inevitable bottlenecks of SQL’s sequential data processing.

MongoDB, the fastest-growing supplier of NoSQL databases, notes this when comparing the traditional SQL relational database with the NoSQL database, saying “relational databases were not designed to cope with the scale and agility challenges that face modern applications, nor were they built to take advantage of the commodity storage and processing power available today.”

With all this in mind, we can see why the NoSQL database market is expected to reach $4.2 billion in value by 2020.

What’s next and why?

We can expect the adoption of NoSQL databases to continue growing, in large part because of Big Data’s continued growth.

And analysis indicates that data-driven decision-making improves productivity and profitability by 6%.

Businesses across industry appear to be picking up on this fact. An EY/Nimbus Ninety study found that 81% of companies understand the importance of data for improving efficiency and business performance.

However, understanding the importance of data to modern business isn’t enough. What 100% of organizations need to grasp is that strategic data analysis that produces useful insights has to start from a stable data management platform.

Gartner indicates that 90% of all data is unstructured, highlighting the need for dedicated data modeling efforts, and at a wider level, data management. Businesses can’t leave that 90% on the table because they don’t have the tools to properly manage it.

This is the crux of the Any2 data management approach – being able to manage “any data” from “anywhere.” NoSQL plays an important role in end-to-end data management by helping to accelerate the retrieval and analysis of Big Data.

The improved handling of data velocity is vital to becoming a successful digital business, one that can effectively respond in real time to customers, partners, suppliers and other parties, and profit from these efforts.

In fact, the velocity with which businesses are able to harness and query large volumes of unstructured, structured and semi-structured data in NoSQL databases makes them a critical asset for supporting modern cloud applications and their scale, speed and agile development demands.

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 Taking Control of NoSQL Databases, get the FREE eBook below.

Benefits of NoSQL

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Multi-tenancy vs. Single-tenancy: Have We Reached the Multi-Tenant Tipping Point?

Multi-tenancy vs. single-tenancy cloud hosting

The multi-tenancy vs. single-tenancy hosting debate has raged for years. Businesses’ differing demands have led to a stalemate, with certain industries more likely to lean one way than the other.

But with advancements in cloud computing and storage infrastructure, the stalemate could be at the beginning of its end.

To understand why multi-tenancy hosting is gaining traction over single-tenancy, it’s important to understand the fundamental differences.

Multi-Tenancy vs. Single-Tenancy

Gartner defines multi-tenancy as: “A reference to the mode of operation of software where multiple independent instances of one or multiple applications operate in a shared environment. The instances (tenants) are logically isolated, but physically integrated.”

The setup is comparable to that of a bank. The bank houses the assets of all customers in one place, but each customer’s assets are stored separately and securely from one another. Yet every bank customer still uses the same services, systems and processes to access the assets that belong to him/her.

The single-tenancy counterpart removes the shared infrastructure element described above. It operates on a one customer (tenant) per instance basis.

The trouble with the single-tenancy approach is that those servers are maintained separately by the host. And of course, this comes with costs – time as well as money – and customers have to foot the bill.

Additionally, the single-tenancy model involves tenants drawing from the power of a single infrastructure. Businesses with thorough Big Data strategies (of which numbers are increasing), need to be able to deal with a wide variety of data sources. The data is often high in volume, and must be processed at increasingly high velocities (more on the Three Vs of Big Data here).

Such businesses need greater ‘elasticity’ to operate efficiently, with ‘elasticity’ referring to the ability to scale resources up and down as required.

Along with cost savings and greater elasticity, multi-tenancy is also primed to make things easier for the tenant from the ground up. The host upgrades systems on the back-end, with updates instantly available to tenants. Maintenance is handled on the host side as well, and only one set of code is needed for delivering, greatly increasing the speed at which new updates can be made.

Given these considerations, it’s hard to fathom why the debate over multi-tenancy vs. single-tenancy has waged for so long.

Diminishing Multi-Tenancy Concerns

The advantages of cost savings, scalability and the ability to focus on improving the business, rather than up-keep, would seem to pique the interest of any business leader.

But the situation is more nuanced than that. Although all businesses would love to take advantage of multi-tenancy’s obvious advantages, shared infrastructure remains a point of contention for some.

Fears about host data breaches are valid and flanked by externally dictated downtime.

But these fears are now increasingly alleviated by sound reassurances. Multi-tenancy hosting initially spun out of single-tenancy hosting, and the fact it wasn’t built for purpose left gaps.

However, we’re now witnessing a generation of purpose-built, multi-tenancy approaches that address the aforementioned fears.

Server offloading means maintenance can happen without tenant downtime and widespread service disruption.

Internal policies and improvements in the way data is managed and siloed on a tenant-by-tenant basis serve to squash security concerns.

Of course, shared infrastructure will still be a point of contention in some industries, but we’re approaching a tipping point as evidenced by the success of such multi-tenancy hosts as Salesforce.

Through solid multi-tenancy strategy, Salesforce has dominated the CRM market, outstripping the growth of its contemporaries. Analysts expect further growth this year to match the uptick in cloud adoption.

What are your thoughts on multi-tenancy vs. single tenancy hosting?

Data-Driven Business Transformation

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erwin Brings NoSQL into the Enterprise Data Modeling and Governance Fold

“NoSQL is not an option — it has become a necessity to support next-generation applications.”

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Five Steps to Digital Transformation

Digital Transformation

Digital transformation is ramping up in all industries. Facing regular market disruptions, and landscape-changing technological breakthroughs, modern businesses must be both malleable and willing to change.

To stay competitive, you must be agile.

Digital Transformation is Inevitable

Increasing numbers of organizations are undergoing a digital transformation. The tried-and-tested yet rigid methods of doing business are being replaced by newer, data-orientated approaches that require thorough but fast analysis.

Some businesses – like Amazon, Netflix and Uber – are leading this evolution. They all provide very different services, but at their core, they are technology focused.

And they’re reaping rewards for it too. Amazon is one of the most valuable businesses in the world, perhaps one of the first companies to reach a $1-trillion valuation.

It’s not too late to adopt digital transformation, but it is  too late to keep fighting against it. The tide of change has quickened, and stubborn businesses could be washed away.

But what’s the best way to get started?

Step One: Determine Your End Goal

Any form of change must start with the end in mind, as it’s impossible to make a transformation without understanding why and how.

Before you make a change, big or small, you need to ask yourself why are we doing this? What are the positives and negatives? And if there are negatives, what can we do to mitigate them?

To ensure a successful digital transformation, it’s important to plot your journey from the beginning through your end goal, understanding how one change or a whole series of changes will alter your business.

Business process modeling tools can help map your digital transformation journey.

Step Two: Get Some Strategic Support

For businesses of any size, transformational change can disrupt day-to-day operations. In most organizations, the expertise to manage a sizeable transformation program doesn’t exist, and from the outset, it can appear quite daunting.

If your goal is to increase profits, it can seem contradictory to pay for support to drive your business forward. However, a slow or incorrect transformational process can be costly in many ways. Therefore, investing in support can be one of the best decisions you make.

Effective strategic planning, rooted in enterprise architecture, can help identify gaps and potential oversights in your strategy. It can indicate where investment is needed and ensure transformative endeavors aren’t undermined by false-starts and U-turns.

Many businesses would benefit further by employing strategic consultants. As experts in their fields, strategic consultants know the right questions to ask to uncover the information you need to influence change.

Their experience can support your efforts by identifying and cataloging underlying components, providing input to the project plan and building the right systems to capture important data needed to meet the business’s transformation goals.

Step Three: Understand What You Have

Once you know where you want to go, it’s important to understand what you currently do. That might seem clear, but even the smallest organizations are underpinned by thousands of business processes.

Before you decide to change something, you need to understand everything about what you currently do, or else a change could have an unanticipated and negative impact.

Enterprise architecture will also benefit a business here, uncovering strategic improvement opportunities – valuable changes you might not have seen.

As third-parties, consultants can provide an impartial view, rather than letting historic or legacy decisions cloud future judgment.

Businesses will also benefit from data modeling. This is due to the exponential increase in the volume of data businesses have to manage – as well as the variety of disparate sources.

Data modeling will ensure data is accessible, understood and better prepared for analysis and the decision-making process.

Step Four: Collect Knowledge from Within

Your employees are a wealth of knowledge and ideas, so it’s important to involve them in the enterprise architecture process.

Consultants can facilitate a series of staff workshops to enable employee insights to be shared and then developed into real, actionable changes.

Step Five: Get Buy-in Across the Business

Once you’ve engaged with your staff to collect the knowledge they hold, make sure you don’t cut them off there. Business change is only successful if everyone understands what is happening and why, with continuous updates.

Ensure that you take your employees through the change process, making them  part of the digital transformation journey.

Evidence suggests that 70 percent of all organizational change efforts fail, with a primary reason being that executives don’t get enough buy-in for new initiatives and ideas.

By involving relevant stakeholders in the strategic planning process, you can mitigate this risk. Strategic planning tools that enable collaboration can achieve this. Thanks to technological advancements in the cloud, collaboration can even be effectively facilitated online.

Take your employees through your digital transformation journey, and you’ll find them celebrating with you when you arrive at your goal.

If you think now is the right time for your business to change, get in touch with us today.

Data-Driven Business Transformation

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What Are Customer Journey Architects, and Do You Need One?

Customer journey architects are becoming more relevant than ever before.

For businesses that want to make improvements, enterprise architecture has long been a tried and tested technique for mapping out how change should take place.

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Enterprise Architecture vs. Data Architecture vs. Business Process Architecture

Enterprise architects, Data architects, Process architects

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 – Changing Perspective

Data-driven business is booming. The dominant, driving force in business has arguably become a driving force in our daily lives for consumers and corporations alike.

We now live in an age in which data is a more valuable resource than oil, and five of the world’s most valuable companies – Alphabet/Google, Amazon, Apple, Facebook and Microsoft – all deal in data.

However, just acknowledging data’s value won’t do. For a business to truly benefit from its information, a change in perspective is also required. With an additional $65 million in net income available to Fortune 1000 companies that make use of just 10 percent more of their data, the stakes are too high to ignore.

Changing Perspective

Traditionally, data management only concerned data professionals. However, mass digital transformation, with data as the foundation, puts this traditional approach at odds with current market needs. Siloing data with data professionals undermines the opportunity to apply data to improve overall business performance.

The precedent is there. Some of the most disruptive businesses of the last decade have doubled down on the data-driven approach, reaping huge rewards for it.

Airbnb, Netflix and Uber have used data to transform everything, including how they make decisions, invent new products or services, and improve processes to add to both their top and bottom lines. And they have shaken their respective markets to their cores.

Even with very different offerings, all three of these businesses identify under the technology banner – that’s telling.

Common Goals

One key reason for the success of data-driven business, is the alignment of common C-suite goals with the outcomes of a data initiative.

Those goals being:

  • Identifying opportunities and risk
  • Strengthening marketing and sales
  • Improving operational and financial performance
  • Managing risk and compliance
  • Producing new products and services, or improve existing ones
  • Monetizing data
  • Satisfying customers

This list of C-suite goals is, in essence, identical to the business outcomes of a data-driven business strategy.

What Your Data Strategy Needs

In the early stages of data transformation, businesses tend to take an ad-hoc approach to data management. Although that might be viable in the beginning, a holistic data-driven strategy requires more than makeshift efforts, and repurposed Office tools .

Organizations that truly embrace data, becoming fundamentally data-driven businesses, will have to manage data from numerous and disparate sources (variety) in increasingly large quantities (volume) and at demandingly high speeds (velocity).

To manage these three Vs of data effectively, your business needs to take an “any-squared” (Any2) approach. That’s “any data” from “anywhere.”

Any2

By leveraging a data management platform with data modeling, enterprise architecture and business process modelling, you can ensure your organization is prepared to undergo a successful digital transformation.

Data modeling identifies what data you have (internal and external), enterprise architecture determines how best to use that data to drive value, and business process modeling provides understanding in how the data should be used to drive business strategy and objectives.

Therefore, the application of the above disciplines and associated tools goes a long way in achieving the common goals of C-suite executives.

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Data-Driven Business Transformation