Tag: collaboration

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An Agile Data Governance Foundation for Building the Data-Driven Enterprise

The data-driven enterprise is the cornerstone of modern business, and good data governance is a key enabler.

In recent years, we’ve seen startups leverage data to catapult themselves ahead of legacy competitors. Companies such as Airbnb, Netflix and Uber have become household names. Although the service each offers differs vastly, all three identify as ‘technology’ organizations because data is integral to their operations.

Data-Driven Business

As with any standard-setting revolution, businesses across the spectrum are now following these examples. But what these organizations need to understand is that simply deciding to be data-driven, or to “do Big Data,” isn’t enough.

As with any strategy or business model, it’s advisable to apply best practices to ensure the endeavor is worthwhile and that it operates as efficiently as possible. In fact, it’s especially important with data, as poorly governed data will lead to slower times to market and oversights in security. Additionally, poorly managed data fosters inaccurate analysis and poor decision-making, further hampering times to market due to inaccuracy in the planning stages, false starts and wasted cycles.

Essentially garbage in, garbage out – so it’s important for businesses to get their foundations right. To build something, you need to know exactly what you’re building and why to understand the best way to progress.

Data Governance 2.0 Is the Underlying Factor

Good data governance (DG) enables every relevant stakeholder – from executives to frontline employees – to discover, understand, govern and socialize data. Then the right people have access to the right data, so the right decisions are easier to make.

Traditionally, DG encompassed governance goals such as maintaining a business glossary of data terms, a data dictionary and catalog. It also enabled lineage mapping and policy authoring.

However, Data Governance 1.0 was siloed with IT left to handle it. Often there were gaps in context, the chain of accountability and the analysis itself.

Data Governance 2.0 remedies this by taking into account the fact that data now permeates all levels of a business. And it allows for greater collaboration.

It gives people interacting with data the required context to make good decisions, and documents the data’s journey, ensuring accountability and compliance with existing and upcoming data regulations.

But beyond the greater collaboration it fosters between people, it also allows for better collaboration between departments and integration with other technology.

By integrating data governance with data modeling (DM), enterprise architecture (EA) and business process (BP), organizations can break down inter-departmental and technical silos for greater visibility and control across domains.

By leveraging a common metadata repository and intuitive role-based and highly configurable user interfaces, organizations can guarantee everyone is singing off the same sheet of music.

Data Governance Enables Better Data Management

The collaborative nature of Data Governance 2.0 is a key enabler for strong data management. Without it, the differing data management initiatives can and often do pull in different directions.

These silos are usually born out of the use of disparate tools that don’t enable collaboration between the relevant roles responsible for the individual data management initiative. This stifles the potential of data analysis, something organizations can’t afford given today’s market conditions.

Businesses operating in highly competitive markets need every advantage: growth, innovation and differentiation. Organizations also need a complete data platform as the rise of data’s involvement in business and subsequent frequent tech advancements mean market landscapes are changing faster than ever before.

By integrating DM, EA and BP, organizations ensure all three initiatives are in sync. Then historically common issues born of siloed data management initiatives don’t arise.

A unified approach, with Data Governance 2.0 at its core, allows organizations to:

  • Enable data fluency and accountability across diverse stakeholders
  • Standardize and harmonize diverse data management platforms and technologies
  • Satisfy compliance and legislative requirements
  • Reduce risks associated with data-driven business transformation
  • Enable enterprise agility and efficiency in data usage.

Data governance is everyone's business

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erwin Expert Blog

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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erwin Expert Blog

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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erwin Expert Blog

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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erwin Expert Blog

Business Process Management Provides Invaluable Knowledge

‘Knowledge is power’ – a well-known phrase and one that is especially true in the business world. Statistics show that Fortune 500 companies lose $31.5 billion each year by failing to gather and share knowledge effectively. So knowing the best way to undertake every business process you have will help drive your business forward.

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