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Internal Business Process Modeling: The Secret Behind Exponential Organizations

Strong internal business process modeling and management helps data-driven organizations compete and lead

In short, an internal business process is a documented account of how things should be done to maximize efficiency and achieve a particular goal.

In the book “Exponential Organizations” by Salim Ismail, Michael S. Malone and Yuri van Geest, the authors, examine how every company is or will evolve into an information-based entity in which costs fall to nearly zero, abundance replaces scarcity and only “exponential organizations” survive.

It’s not news that exponential organizations like Uber, Airbnb and Netflix have flipped the script on disrupting traditional industries like taxis, hotels and video rentals/TV viewing.

But now, even traditional industries like healthcare and financial services, which were historically slow to innovate, are transforming at breakneck speed.

Let’s face it, in today’s hyper-competitive markets, the traditional approach of relying on legacy strengths or inertia for survival just simply won’t work.

The days of enterprises focusing almost exclusively on rigid structures, centralized management and accountability; concentrated knowledge; service mainly to external customers; and reactive, short-term strategy alignment driven mainly by massive-scale projects are antiquated.

The information within your organization’s internal business processes is where the data your company collects, creates, stores and analyzes actually transforms into something that makes your company go, hopefully for the long haul.

Internal Business Process Modeling - Exponential Organizations

The Value of Internal Business Process Modeling

Organizations are built on a series of internal business processes. The complexity of modern data-driven organizations requires processes to work in tandem to create and sustain value.

The degree to which any individual internal business process drives value can vary, but even the most seemingly mundane processes are part of a collective sum, greater than its parts.

Therefore, it’s critical for organizations to map their internal business processes to understand how a given action relates to the organizations’ overall strategy and goals.

Such knowledge is at the core of exponential organizations. They understand how any given internal business process relates to value creation, making it far easier to assess what’s currently working but also identify areas for improvement as well as the potential for competitive differentiation.

Exponential organizations also are better positioned to respond and adapt to disruptive forces, such as 5G. This is because understanding what and how you do things now makes it easier to implement change in an agile manner.

5G Roadmap: Preparing Your Enterprise Architecture

How do you join the ranks of exponential organizations? And where do you begin your journey to becoming an information-based entity?

Attitude Adjustment

More and more organizations are realizing they need to adjust their traditional thinking and subsequent actions, even if just a bit, to gain strategic advantage, reduce costs and retain market dominance. For example:

  1. Structures are becoming more adaptable, allowing for greater flexibility and cost management. How is this possible and why now? Organizations are grasping that effective, well-managed and documented internal business processes should form their operational backbones.
  2. Business units and the departments within them are becoming accountable not only for their own budgets but also on how well they achieve their goals. This is possible because their responsibilities and processes can be clearly defined, documented and then monitored to ensure their work is executed in a repeatable, predictable and measurable way.
  3. Knowledge is now both centralized and distributed thanks to modern knowledge management systems. Central repositories and collaborative portals give everyone within the organization equal access to the data they need to do their jobs more effectively and efficiently.
  4. And thanks to all the above, organizations can expand their focus from external customers to internal ones as well. By clearly identifying individual processes (and their cross-business handover points) and customer touchpoints, organizations can interact with any customer at the right point with the most appropriate resources.

Benefits of Internal Business Process Modeling and Management

One of the main benefits of a process-based organizational engine is that it should be able to better handle outside pressures, such as new regulations, if they are – or are becoming – truly process-based. Because once processes (and their encompassing business architecture) become central to the organization, a wide array of things become simpler, faster and cheaper.

Another benefit is application design – the holy grail or black hole of budgetary spending and project management, depending on your point of view – is streamlined, with requirements clearly gathered and managed in perfect correspondence to the processes they serve and with the data they manage clearly documented and communicated to the developers.

Testing occurs against real-life scenarios by the responsible parties as documented by the process owners – a drastic departure from the more traditional approaches in which the responsibility fell to designated, usually technical application owners.

Finally – and most important – data governance is no longer the isolated domain of data architects but central to the everyday processes that make an organization tick. As processes have stakeholders who use information – data – the roles of technical owners and data stewards become integral to ensuring processes operate efficiently, effectively and – above all – without interruptions. On the other side of this coin, data owners and data stewards no longer operate in their own worlds, distant from the processes their data supports.

Carpe Process

All modern organizations should seize business process as a central component to their operations. Data governance as well, and cost management becoming a third driver for the enterprise machine. But as we all know, it takes more than stable connecting rods to make an engine work – it needs cogs and wheels, belts and multiple power sources, all working together.

In the traditional organization, people are the internal mechanics. These days, powerful and flexible workflow engines provide much-needed automation for greater visibility plus more power, stability and quality – all the things a machine needs to operate as required/designed.

Advanced process management systems are becoming essential, not optional. And while not as sexy or attention-grabbing as other technologies, they provide the power to drive an organization toward its goals quickly, cost-effectively and efficiently.

To learn how erwin can empower a modern, process-based organization, please click here.

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5G Roadmap: Preparing Your Enterprise Architecture

Why planning your 5G roadmap requires significant input from enterprise architects

5G is coming and bringing with it the promise to transform any industry. And while the focus has been on the benefits to consumers,  the effects on the enterprise are far-reaching.

Few examples of emerging technology have the potential to disrupt and downright revolutionize certain markets and processes than 5G.

For enterprise architects, it’s important to understand how a potentially disruptive emerging technology like 5G might be incorporated into an organization, in advance.

A 5G roadmap could be the difference between such disruptions being an obstruction or an opportunity.

As with any emerging technology,  organizations need to test and pilot their projects to answer some important questions before going into production:

  • How do these technologies disrupt?
  • How do they provide value?

While the transition from 3G to 4G wasn’t all that eventful – or all that long ago – 5G is expected to buck the trend.

But how exactly?

5G: What to expect

5G promises dramatically faster download and upload speeds and reduced latency.

For context, average 4G speeds peak at around 45 Mbps (megabits per second); the industry goal is to hit 1 Gb (gigabit per second = 1,000 Mbps).

Telecom company Qualcomm believes real-world applications of 5G could be 10 to 20 times faster than that.

For consumers, this will mean dramatically faster downloads and uploads. Currently, downloading a two-hour movie takes around six  minutes on 4G. A 5G connection would achieve the same in just 3.6 seconds.

Organizations will, of course, enjoy the same benefits but will be burdened by the need to manage new levels of data, starting with telecommunications companies (telcos).

5G – A disruptive force vs. a catalyst for disruption

Usually, when we think of emerging disruptive technologies, the technology (or process, product, etc.) itself is the primary cause of the disruption.

With 5G, that’s still somewhat true. At least for telcos …

For example, 5G-driven disruption is forcing telecommunications companies to upgrade their infrastructure to cope with new volumes and velocities of data.

On a base level, these higher data volumes and velocities will be attributable to the fact that by making something happen faster, more of it can happen in a shorter amount of time.

But the increase in data speeds will be a catalyst for products and services that are currently not feasible becoming completely viable in the near future.

Of course, enterprise architecture is already integral to organizations with Internet of Things (IoT) devices in their portfolios.

5G enterprise architecture roadmap

But companies involved in internet-connected product market, as well as telcos, will need a 5G roadmap to ensure their enterprise architectures can cope with the additional data burden.

In addition to faster connection speeds, 5G will grant telcos more control over networks.

One such example of this control is the potential for network slicing, whereby multiple virtual networks can be generated within one physical 5G network, in turn allowing greater control of the service provided.

For example, self-driving cars would benefit from a network slice that offered exceptionally fast, low-latency connections to better accommodate their real-time data processing and transmitting needs.

Such a set up would go to waste for less-interactive, internet-connected devices. A smart fridge for example, could make do with far slower connection speeds.

This would mean telecommunications companies would start to look more like public-cloud providers and offer scalable services to their user bases.

However, realizing this potential would require more agile-oriented infrastructures than telcos typically have – which will of course require further input from enterprise architects to encourage an efficient implementation.

Another red pin to account for on the 5G roadmap.

So the answer to “Is 5G a disruptive force in and of itself, or is it a catalyst for disruption?” is actually … well, both. With telcos directly impacted by 5G disruption, and IoT product/service providers and digital business on the whole being disrupted by what 5G ultimately enables.

What does this mean for enterprise architects?

As addressed above, many of the business benefits of 5G are directly tied to increasing the amount of data that can be transferred at one time.

This presents a number of challenges for enterprise architects going forward.

As well as the increased volume of data itself, enterprise architects will need to prepare for faster times to market.

Radically improved data transfer speeds will encourage more agile product rollouts and updates, especially in connected devices that will feedback data insights about their performance.

The reduced latency will likely lead to a new influx of remote working, collaboration- enabling tools as well as products and services currently unaccounted for. Organizations with more agile enterprise architectures will be better placed to implement these smoothly when the time comes.

To better understand how your organization can prepare for 5G by adopting an agile enterprise architecture approach, click here.

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Software Deployment Strategy: How to Get It Right the First Time

Big or Small, Enterprise Architecture Is a Key Part of a Successful Software Deployment Strategy

A good software deployment strategy could be the difference between multiple and costly false starts and a smooth implementation. Considering the rate at which emerging technologies are introduced, it’s becoming more important than ever for organizations to have a software deployment strategy in place.

But what does it involve?

Not all software deployments and investments are equal. Large-scale, big-money investments like ERP require a lot of resources and planning. Small-scale investments, like website technology, on the other hand, can be purchased, expensed and deployed with few people knowing. And of course, there are thousands of software decisions made that fall somewhere in between.

Software purchase decisions and deployments represent an opportunity to leverage the experience and knowledge of your enterprise architecture (EA) team so you can make smarter, better investments. The key here is the EA team’s complete view of your IT landscape, which can help eliminate redundant purchases, identify issues of integration and more.

 

Software Deployment Strategy: How to Get It Right the First Time

Small Projects Can Create Big Headaches

Here’s an example of how a small-scale software investment can wreak havoc on an organization.

There is an intense focus today on customer experience (CX). Ensuring that your website visitors have access to the information they want, and they can find it quickly and easily, is just part of your overall CX. This makes your customer-facing technologies – the ones that power your website or mobile app – critical investments, even though they may not carry the price tag of an ERP system.

Even the smallest investments need to be vetted to make sure they work with existing infrastructure and processes. One small piece of website tech that ends up degrading your online CX can cost your organization millions in a very short amount of time. There’s simply too many choices just a click away today if something isn’t working properly. Differentiating technologies are also more likely to be customized than an application like ERP, which can often use a number of out-of-the-box processes.

These are areas where a software deployment strategy involving your EA team can help guide the software purchase and deployment process. But even in a world where software deployments increasingly mean logging into a cloud-based SaaS application, a software deployment strategy is still beneficial.

Don’t Be Resigned to Failure

Many SaaS vendors like to talk about how easy it is to get up and running with their products, especially when the infrastructure elements are in the cloud. But the reality is that the network that connects to the SaaS application, the security, the integrations with existing (often on-premise) applications, the SLAs and licensing, can all benefit from a review by the EA team.

Failed software deployments are, in fact, a significant problem for many organizations. Such failures can often be attributed to a lack of planning and foresight.

Considering the costs associated with some software – including its purchase, implementation and consultancy fees/training required to get started – a good software deployment strategy could save millions … literally.

A Gartner study found that nearly half (46 percent) of respondents said their most expensive, time-intensive software deployments were not delivering. When Gartner broke the software purchases in question into deal sizes of over and under $1 million, the firm got similar results.

When your EA team has the visibility to see across your IT landscape and understand the business processes built on your technology, it can help provide a better idea of the real costs behind your software deployments and you can better estimate your time to value. When it comes to software investments, you don’t be resigned to failure.

erwin EA gives organizations a full-featured, versatile platform for enterprise architecture in its broadest sense to ensure the success of projects – regardless of their size or scope.

Start your free trial of erwin EA now.

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The Design Thinking Process: Five Stages to Solving Business Problems

The design thinking process is a method of encouraging and improving creative problem-solving.

The design thinking process is by no means new.

John Edward Arnold, a professor of mechanical engineering and business administration, was one of the first to discuss the concept in as early as the 1950s.

But the wave of digital and data-driven business has created new opportunities for the design thinking process to be applied.

For example, your business is likely collecting, storing and analyzing more information than ever before.

And while the intense focus on analytics in recent years has been good for many businesses, it’s important to remember the human element of making decisions and solving problems.

So with that in mind, the design thinking process can be used to bridge the gap between the data and the people.

But what is the design thinking process, exactly? And how does it work?

Design Thinking Definition: The Five Stages of the Design Thinking Process

There are lots of ways to harness ideas and solve problems. Design thinking is one means to foster and refine creative problem-solving.

While it doesn’t suggest ignoring your data, design thinking is, at its core, human-centered. It encourages organizations to focus on the people they’re creating for in hopes of producing better products, services and internal processes.

5 Stages in the Design Thinking Process

There are five stages in the design thinking process:

1. Empathize – The first stage of the design thinking process is gaining a better understanding of what problems need solving. It puts the end user you are trying to help first and encourages you to work backwards. By consulting and subsequently empathizing with the end user, you ensure your eventual solution is goal-oriented, increasing the likelihood of its effectiveness.

2. Define the problem – Once you have a better understanding of potential issues, it’s time to get specific. At this point, it’s good practice to translate the problem into a “problem statement” – a concise description of the issue that identifies the current state you wish to address and the desired future state you intend to reach.

3. Ideate solutions – This is the time to get creative. Once you have a solid understanding of the problem you can brainstorm ideas to bridge the gap between the current and the desired future state to eliminate it.

4. Prototype – At stage four, it’s time to implement the ideas from stage three in the real world. Typically, the prototype will be a scaled-down example of the solution – or ideally, possible solutions. It goes without saying, but things are rarely perfect in their first iteration, as you’ll likely discover in the next stage.

5. Test – At this point, it’s time to test whether the proposed solution works. In the case of multiple potential solutions, this stage can identify which is most effective and/or efficient. It’s also an opportunity to assess what – if any – new problems the solution might cause.

With this in mind, it’s important to remember that progression through the five stages of the design thinking process isn’t necessarily linear.

Unsuccessful tests could lead your team back to the ideation stage. In some cases, you may want to circle back to stage one to test your new solution with end users. Then you’ll be able to better emphasize and understand how your solution might work in practice.

It’s also important to understand that the design thinking process is not, strictly speaking, the same as innovation. It’s an approach to problem-solving that may ultimately involve innovation or emerging technologies, but innovation is not inherently required.

Design thinking is an iterative process, and the best solutions that come out of it in many organizations will become part of their enterprise architectures.

Incorporating Design Thinking into Your Organization with Enterprise Architecture

The best way to put design thinking into use in your organization is by creating a strategic planning approach that takes ideas from assessment to analysis to delivery.

By employing an iterative approach with a thorough assessment and a feedback loop, everyone in your organization will feel more empowered and engaged.

The reality of business today is that nearly every business problem is going to have a technological solution.

It will fall to the IT organization to take the ideas that come out of your design thinking and figure out how to deliver them as solutions at scale and speed.

This is where enterprise architecture comes into play.

Evaluating, planning and deploying a business solution will require visibility. How will these solutions impact users? Can they be supported by the existing IT infrastructure? How do they fit into the business ecosystem?

When it comes to these important questions, the best place to get answers is from your enterprise architecture team. Be sure to make them a central part of your design thinking process.

In addition to enterprise architecture software, erwin also provides enterprise architecture consulting. You can learn more about those services here.

You also can try all the current features of erwin EA for free via our secure, cloud-based trial environment.

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Managing Ideation and Innovation with Enterprise Architecture

Organizations largely recognize the need for enterprise architecture tools, yet some still struggle to communicate their value and prioritize such initiatives.

As data-driven business thrives, organizations will have to overcome these challenges because managing IT trends and emerging technologies makes enterprise architecture (EA) increasingly relevant.

“By 2021, 40 percent of organizations will use enterprise architects to help ideate new business innovations made possible by emerging technologies,” says Marcus Blosch, Vice President Analyst, Gartner.

With technology now vital to every aspect of the business, enterprise architecture tools and EA as a function help generate and evaluate ideas that move the business forward.

Every business has its own (often ad hoc) way of gathering ideas and evaluating them to see how they can be implemented and what it would take to deploy them.

But organizations can use enterprise architecture tools to bridge the gap between ideation and implementation, making more informed choices in the process.

By combining enterprise architecture tools with the EA team’s knowledge in a process for managing ideas and innovation, organizations can be more strategic in their planning.

Emerging technologies is one of the key areas in which such a process benefits an organization. The timely identification of emerging technologies can make or break a business. The more thought that goes into the planning of when and how to use emerging technologies, the better the implementation, which leads to better outcomes and greater ROI.

Gartner emphasize the value of enterprise architecture tools

Enterprise Architecture Tools: The Fabric of Your Organization

At its 2019 Gartner Enterprise Architecture & Technology Innovation Summit, Gartner identified 10 emerging and strategic technology trends that will shape IT in the coming years.

They included trends that utilize intelligence, such as autonomous things and augmented analytics; digital trends like empowered edge and immersive experiences; mesh trends like Blockchain and smart spaces; as well as broad concepts like digital ethics and privacy and quantum computing.

As these trends develop into applications or become part of your organization’s fabric, you need to think about how they can help grow your business in the near and long term. How will your business investigate their use? How will you identify the people who understand how they can be used to drive your business?

Many organizations lack a structured approach for gathering and investigating employee ideas, especially those around emerging technologies. This creates two issues:

1. When employee ideas fall into a black hole where they don’t get feedback, the employees become less engaged.

2. The emerging technology and its implementation are disconnected, which leads to silos or wasted resources.

How Enterprise Architecture Tools Help Communicate the Value of Emerging Technologies

When your enterprise architecture is aligned with your business outcomes it provides a way to help your business ideate and investigate the viability of ideas on both the technical and business level. When aligned correctly, emerging technologies can be evaluated based on how they meet business needs and what the IT organization must do to support them.

But the only way you can accurately make those determinations is by having visibility into your IT services and the application portfolio. And that’s how enterprise architecture can help communicate the value of emerging technologies in your organization.

erwin EA provides a way to quickly and efficiently understand opportunities offered by new technologies, process improvements and portfolio rationalization and translate them into an actionable strategy for the entire organization.

Take erwin EA for a free spin thanks to our secure, cloud-based trial.

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Why EA Needs to Be Part of Your Digital Transformation Strategy

Enterprise architecture (EA) isn’t dead, you’re just using it wrong. Part three of erwin’s digital transformation blog series.  

I’ll let you in on a little secret: the rumor of enterprise architecture’s demise has been greatly exaggerated. However, the truth for many of today’s fast-moving businesses is that enterprise architecture fails. But why?

Enterprise architecture is invaluable for internal business intelligence (but is rarely used for real intelligence), governance (but often has a very narrow focus), management insights (but doesn’t typically provide useful insights), and transformation and planning (ok, now we have something!).

In reality, most organizations do not leverage EA teams to their true potential. Instead they rely on consultants, trends, regulations and legislation to drive strategy.

Why does this happen?

Don’t Put Enterprise Architecture in a Corner

EA has remained in its traditional comfort zone of IT. EA is not only about IT …  but yet, EA lives within IT, focuses on IT and therefore loses its business dimension and support.

It remains isolated and is rarely, if ever, involved in:

  • Assessing, planning and running business transformation initiatives
  • Providing real, enterprise-wide insights
  • Producing actionable initiatives

Instead, it focuses on managing “stuff”:

  • Understanding existing “stuff” by gathering exhaustively detailed information
  • Running “stuff”-deployment projects
  • Managing cost “stuff”
  • “Moving to the cloud” (the solution to … everything)

Enterprise Architecture

What Prevents Enterprise Architecture from Being Successful?

There are three main reasons why EA has been pigeon-holed:

  1. Lack of trust in the available information
    • Information is mostly collected, entered and maintained manually
    • Automated data collection and connection is costly and error-prone
    • Identification of issues can be very difficult and time-consuming
  1. Lack of true asset governance and collaboration
    • Enterprise architecture becomes ring-fenced within a department
    • Few stakeholders willing to be actively involved in owning assets and be responsible for them
    • Collaboration on EA is seen as secondary and mostly focused on reports and status updates
  1. Lack of practical insights (insights, analyses and management views)
    • Too small and narrow thinking of what EA can provide
    • The few analyses performed focus on immediate questions, rarely planning and strategy
    • Collaboration on EA is seen as secondary and mostly focused on reports and status updates

Because of this, EA fails to deliver the relevant insights that management needs to make decisions – in a timely manner – and loses its credibility.

But the fact is EA should be, and was designed to be, about actionable insights leading to innovative architecture, not about only managing “stuff!”

Don’t Slow Your Roll. Elevate Your Role.

It’s clear that the role of EA in driving digital transformation needs to be elevated. It needs to be a strategic partner with the business.

According to a McKinsey report on the “Five Enterprise-Architecture Practices That Add Value to Digital Transformations,” EA teams need to:

“Translate architecture issues into terms that senior executives will understand. Enterprise architects can promote closer alignment between business and IT by helping to translate architecture issues for business leaders and managers who aren’t technology savvy. Engaging senior management in discussions about enterprise architecture requires management to dedicate time and actively work on technology topics. It also requires the EA team to explain technology matters in terms that business leaders can relate to.”

With that said, to further change the perception of EA within the organization you need to serve what management needs. To do this, enterprise architects need to develop innovative business, not IT insights, and make them dynamic. Next, enterprise architects need to gather information you can trust and then maintain.

To provide these strategic insights, you don’t need to focus on everything — you need to focus on what management wants you to focus on. The rest is just IT being IT. And, finally, you need to collaborate – like your life depends on it.

Giving Digital Transformation an Enterprise Architecture EDGE

The job of the enterprise architecture is to provide the tools and insights for the C-suite, and other business stakeholders, to help deploy strategies for business transformation.

Let’s say the CEO has a brilliant idea and wants to test it. This is EA’s sweet spot and opportunity to shine. And this is where erwin lives by providing an easy, automated way to deliver collaboration, speed and responsiveness.

erwin is about providing the right information to the right people at the right time. We are focused on empowering the forward-thinking enterprise architect by providing:

  • Superb, near real-time understanding of information
  • Excellent, intuitive collaboration
  • Dynamic, interactive dashboards (vertical and horizontal)
  • Actual, realistic, business-oriented insights
  • Assessment, planning and implementation support

Data-Driven Business Transformation

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Constructing a Digital Transformation Strategy: Putting the Data in Digital Transformation

Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a digital transformation strategy viable?

Part Two of the Digital Transformation Journey …

In our last blog on driving digital transformation, we explored how business architecture and process (BP) modeling are pivotal factors in a viable digital transformation strategy.

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It gives them the ability to identify what challenges and opportunities exist, and provides a low-cost, low-risk environment to model new options and collaborate with key stakeholders to figure out what needs to change, what shouldn’t change, and what’s the most important changes are.

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there.

Digital Transformation Examples

Constructing a Digital Transformation Strategy: Data Enablement

Many organizations prioritize data collection as part of their digital transformation strategy. However, few organizations truly understand their data or know how to consistently maximize its value.

If your business is like most, you collect and analyze some data from a subset of sources to make product improvements, enhance customer service, reduce expenses and inform other, mostly tactical decisions.

The real question is: are you reaping all the value you can from all your data? Probably not.

Most organizations don’t use all the data they’re flooded with to reach deeper conclusions or make other strategic decisions. They don’t know exactly what data they have or even where some of it is, and they struggle to integrate known data in various formats and from numerous systems—especially if they don’t have a way to automate those processes.

How does your business become more adept at wringing all the value it can from its data?

The reality is there’s not enough time, people and money for true data management using manual processes. Therefore, an automation framework for data management has to be part of the foundations of a digital transformation strategy.

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management.

You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.

An automated, metadata-driven framework for cataloging data assets and their flows across the business provides an efficient, agile and dynamic way to generate data lineage from operational source systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture; construct business glossaries; assess what data aligns with specific business rules and policies; and inform how that data is transformed, integrated and federated throughout business processes—complete with full documentation.

Without this framework and the ability to automate many of its processes, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by taking a manual approach. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs.

With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders.

Constructing a Digital Transformation Strategy: Smarter Data

Ultimately, data is the foundation of the new digital business model. Companies that have the ability to harness, secure and leverage information effectively may be better equipped than others to promote digital transformation and gain a competitive advantage.

While data collection and storage continues to happen at a dramatic clip, organizations typically analyze and use less than 0.5 percent of the information they take in – that’s a huge loss of potential. Companies have to know what data they have and understand what it means in common, standardized terms so they can act on it to the benefit of the organization.

Unfortunately, organizations spend a lot more time searching for data rather than actually putting it to work. In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC.

The solution is data intelligence. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement.

It helps solve the lack of visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications.

Organizations need a real-time, accurate picture of the metadata landscape to:

  • Discover data – Identify and interrogate metadata from various data management silos.
  • Harvest data – Automate metadata collection from various data management silos and consolidate it into a single source.
  • Structure and deploy data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.
  • Analyze metadata – Understand how data relates to the business and what attributes it has.
  • Map data flows – Identify where to integrate data and track how it moves and transforms.
  • Govern data – Develop a governance model to manage standards, policies and best practices and associate them with physical assets.
  • Socialize data – Empower stakeholders to see data in one place and in the context of their roles.

The Right Tools

When it comes to digital transformation (like most things), organizations want to do it right. Do it faster. Do it cheaper. And do it without the risk of breaking everything. To accomplish all of this, you need the right tools.

The erwin Data Intelligence (DI) Suite is the heart of the erwin EDGE platform for creating an “enterprise data governance experience.” erwin DI combines data cataloging and data literacy capabilities to provide greater awareness of and access to available data assets, guidance on how to use them, and guardrails to ensure data policies and best practices are followed.

erwin Data Catalog automates enterprise metadata management, data mapping, reference data management, code generation, data lineage and impact analysis. It efficiently integrates and activates data in a single, unified catalog in accordance with business requirements. With it, you can:

  • Schedule ongoing scans of metadata from the widest array of data sources.
  • Keep metadata current with full versioning and change management.
  • Easily map data elements from source to target, including data in motion, and harmonize data integration across platforms.

erwin Data Literacy provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms, complete with built-in accountability and workflows. With it, you can:

  • Enable data consumers to define and discover data relevant to their roles.
  • Facilitate the understanding and use of data within a business context.
  • Ensure the organization is fluent in the language of data.

With data governance and intelligence, enterprises can discover, understand, govern and socialize mission-critical information. And because many of the associated processes can be automated, you reduce errors and reliance on technical resources while increasing the speed and quality of your data pipeline to accomplish whatever your strategic objectives are, including digital transformation.

Check out our latest whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.

Data Intelligence: Empowering the Citizen Analyst with Democratized Data

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements.

News broke earlier this week of British Airways being fined 183 million pounds – or $228 million – by the U.K. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). While not the first, it is the largest penalty levied since the GDPR went into effect in May 2018.

Given this, Oppenheimer & Co. cautions:

“European regulators could accelerate the crackdown on GDPR violators, which in turn could accelerate demand for GDPR readiness. Although the CCPA [California Consumer Privacy Act, the U.S. equivalent of GDPR] will not become effective until 2020, we believe that new developments in GDPR enforcement may influence the regulatory framework of the still fluid CCPA.”

With all the advance notice and significant chatter for GDPR/CCPA,  why aren’t organizations more prepared to deal with data regulations?

In a word? Complexity.

The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises.

So it’s important to understand how to use strategic data governance to manage the complexity of regulatory compliance and other business objectives …

Designing and Operationalizing Regulatory Compliance Strategy

It’s not easy to design and deploy compliance in an environment that’s not well understood and difficult in which to maneuver. First you need to analyze and design your compliance strategy and tactics, and then you need to operationalize them.

Modern, strategic data governance, which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. It also helps enterprises put these strategic capabilities into action by:

  • Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance.
  • Creating and automating a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage.
  • Activating their metadata to drive agile data preparation and governance through integrated data glossaries and dictionaries that associate policies to enable stakeholder data literacy.

Strategic Data Governance for GDPR/CCPA

Five Steps to GDPR/CCPA Compliance

With the right technology, GDPR/CCPA compliance can be automated and accelerated in these five steps:

  1. Catalog systems

Harvest, enrich/transform and catalog data from a wide array of sources to enable any stakeholder to see the interrelationships of data assets across the organization.

  1. Govern PII “at rest”

Classify, flag and socialize the use and governance of personally identifiable information regardless of where it is stored.

  1. Govern PII “in motion”

Scan, catalog and map personally identifiable information to understand how it moves inside and outside the organization and how it changes along the way.

  1. Manage policies and rules

Govern business terminology in addition to data policies and rules, depicting relationships to physical data catalogs and the applications that use them with lineage and impact analysis views.

  1. Strengthen data security

Identify regulatory risks and guide the fortification of network and encryption security standards and policies by understanding where all personally identifiable information is stored, processed and used.

How erwin Can Help

erwin is the only software provider with a complete, metadata-driven approach to data governance through our integrated enterprise modeling and data intelligence suites. We help customers overcome their data governance challenges, with risk management and regulatory compliance being primary concerns.

However, the erwin EDGE also delivers an “enterprise data governance experience” in terms of agile innovation and business transformation – from creating new products and services to keeping customers happy to generating more revenue.

Whatever your organization’s key drivers are, a strategic data governance approach – through  business process, enterprise architecture and data modeling combined with data cataloging and data literacy – is key to success in our modern, digital world.

If you’d like to get a handle on handling your data, you can sign up for a free, one-on-one demo of erwin Data Intelligence.

For more information on GDPR/CCPA, we’ve also published a white paper on the Regulatory Rationale for Integrating Data Management and Data Governance.

GDPR White Paper

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The Importance of EA/BP for Mergers and Acquisitions

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

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

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

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

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

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

Mergers and Acquisitions

Getting Rid of Duplications Duplications

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

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

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

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

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

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

Knowledge Retention – Avoiding Brain Drain

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

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

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

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

 

[FREE EBOOK] Application Portfolio Management For Mergers & Acquisitions 

 

Ensuring Compliance

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

Now, broader mandates like the European Union’s Generation Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require businesses across industries to think about their compliance efforts. Business process modeling helps organizations prove what they are doing to meet compliance requirements and understand how changes to their processes impact compliance efforts (and vice versa).

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

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

Introducing Business Architecture

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

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

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

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

Cloud-based enterprise architecture and business process

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Digital Transformation Examples: Three Industries Dominating Digital Transformation

Digital transformation examples can be found almost anywhere, in almost any industry. Its past successes – and future potential – are well documented, chronicled in the billion-dollar valuations of the frontrunners in the practice.

Amazon began as a disruptor to brick-and-mortar bookstores, eventually becoming one of the most obvious digital transformation examples as it went on to revolutionize online shopping.

Netflix’s origins were similar – annihilating its former rival Blockbuster and the entire DVD rental market to become a dominant streaming platform and media publisher.

Disruption is the common theme. Netflix decimated the DVD rental market while Amazon continues to play a role in “high-street” shopping’s decline.

As technology continues to disrupt markets, digital transformation is do or die.

According to IDC’s digital transformation predictions report for 2019, these types of initiatives are going to flood the enterprise during the next five years.

The following three examples highlight the extent to which digital transformation is reshaping the nature of business and government and how we – as a society – interact with the world.

Digital Transformation in Retail

The inherently competitive nature of retail has made the sector a leader in adopting data-driven strategy.

From loyalty cards to targeted online ads, retail has always had to adapt to stay relevant.

Four main areas in retail demonstrate digital transformation, with a healthy data governance initiative driving them all.

Digital transformation examples

With accurate, relevant and accessible data, organizations can address the following:

  • Customer experience: If your data shows a lot of abandoned carts from mobile app users, then that’s an area to investigate, and good data will identify it.
  • Competitive differentiation: Are personalized offers increasing sales and creating customer loyalty? This is an important data point for marketing strategy.
  • Supply chain:Can a problem with quality be related to items shipping from a certain warehouse? Data will zero in on the location of the problem.
  • Partnerships:Are your partnerships helping grow other parts of your business and creating new customers? Or are your existing customers using partners in place of visiting your store? Data can tell you.

This article further explores digital transformation and data governance in retail.

Digital Transformation in Hospitality

Hospitality is another industry awash in digital transformation examples. Brick-and-mortar travel agencies are ceding ground to mobile-first (and mobile-only) businesses.

Their offerings range from purchasing vacation packages to the ability to check in and order room service via mobile devices.

With augmented and virtual reality, it even may be possible to one day “test drive” holiday plans from the comfort of the sofa – say before swimming with sharks or going on safari.

The extent of digitization now possible in the hospitality industry means these businesses have to account for and manage an abundance of data types and sources to glean insights to fuel the best customer experiences.

Unsurprisingly, this is yet another area where a healthy data governance initiative can be the difference between industry-disrupting success and abject failure.

This piece further discusses how data is transforming the hospitality industry and the role of data governance in it.

Digital Transformation in Municipal Government

Historically, municipal government isn’t seen as an area at the forefront of adopting emerging technology.

But the emergence of “smart cities” is a prominent example of digital transformation.

Even the concept of a smart city is a response to existing digital transformation in the private sector, as governments have been coerced into updating infrastructure to reflect the modern world.

Today, municipal governments around the world are using digital transformation to improve residents’ quality of life, from improving transportation and public safety to making it convenient to pay bills or request services online.

Of course, when going “smart,” municipal governments will need an understanding of data governance best practices.

This article analyzes how municipal governments can be “smart” about their transformation efforts.

Mitigating Digital Transformation Risks

Risks come with any investment. But in the context of digital transformation, taking risks is both a necessity and an inevitability.

Organizations also will need to consult their data to ensure they transform themselves the right way – and not just for transformation’s sake.

A recent PwC study found that successful digital transformation risk-takers “find the right fit for emerging technologies.”

Doing so points to the need for both effective data governance to find, understand and socialize the most relevant data assets and healthy enterprise architecture to learn what systems and applications create, store and use those data assets.

With application portfolio management and impact analysis, organizations can identify immediate opportunities for digital transformation and areas where more consideration and planning may be necessary before making changes.

As the data governance company, we provide data governance as well as enterprise architecture software, plus tools for business process and data modeling, data cataloging and data literacy. As an integrated software platform, organizations ensure IT and business collaboration to drive risk management, innovation and transformation efforts.

If you’d like to learn more about digital transformation and other use cases for data governance technologies, stay up to date with the erwin Experts here.

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