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Data Intelligence Enterprise Architecture Data Governance erwin Expert Blog

Integrating Data Governance and Enterprise Architecture

Aligning these practices for regulatory compliance and other benefits

Why should you integrate data governance (DG) and enterprise architecture (EA)? It’s time to think about EA beyond IT.

Two of the biggest challenges in creating a successful enterprise architecture initiative are: collecting accurate information on application ecosystems and maintaining the information as application ecosystems change.

Data governance provides time-sensitive, current-state architecture information with a high level of quality. It documents your data assets from end to end for business understanding and clear data lineage with traceability.

In the context of EA, data governance helps you understand what information you have; where it came from; if it’s secure; who’s accountable for it; who accessed it and in which systems and applications it’s located and moves between.

You can collect complete application ecosystem information; objectively identify connections/interfaces between applications, using data; provide accurate compliance assessments; and quickly identify security risks and other issues.

Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

To better understand and align data governance and enterprise architecture, let’s look at data at rest and data in motion and why they both have to be documented.

  1. Documenting data at rest involves looking at where data is stored, such as in databases, data lakes, data warehouses and flat files. You must capture all of this information from the columns, fields and tables – and all the data overlaid on top of that. This means understanding not just the technical aspects of a data asset but also how the business uses that data asset.
  2. Documenting data in motion looks at how data flows between source and target systems and not just the data flows themselves but also how those data flows are structured in terms of metadata. We have to document how our systems interact, including the logical and physical data assets that flow into, out of and between them.

data governance and enterprise architecture

Automating Data Governance and Enterprise Architecture

If you have a data governance program and tooling in place, you’re able to document a lot of information that enterprise architects and process modelers usually spend months, if not years, collecting and keeping up to date.

So within a data governance repository, you’re capturing systems, environments, databases and data — both logical and physical. You’re also collecting information about how those systems are interconnected.

With all this information about the data landscape and the systems that use and store it, you’re automatically collecting your organization’s application architecture. Therefore you can drastically reduce the time to achieving value because your enterprise architecture will always be up to date because you’re managing the associated data properly.

If your organization also has an enterprise architecture practice and tooling, you can automate the current-state architecture, which is arguably the most expensive and time-intensive aspect of enterprise architecture to have at your fingertips.

In erwin’s 2020 State of Data Governance and Automation report, close to 70 percent of respondents said they spend an average of 10 or more hours per week on data-related activities, and most of that time is spent searching for and preparing data.

At the same time, it’s also critical to answer the executives’ questions. You can’t do impact analysis if you don’t understand the current-state architecture, and it’s not going to be delivered quick enough if it isn’t documented.

Data Governance and Enterprise Architecture for Regulatory Compliance

First and foremost, we can start to document the application inventory automatically because we are scanning systems and understanding the architecture itself. When you pre-populate your interface inventory, application lineage and data flows, you see clear-cut dependencies.

That makes regulatory compliance a fantastic use case for both data governance and EA. You can factor this use case into process and application architecture diagrams, looking at where this type of data goes and what sort of systems in touches.

With that information, you can start to classify information for such regulations as the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) or any type of compliance data for an up-to-date regulatory compliance repository. Then all this information flows into processing controls and will ultimately deliver real-time, true impact analysis and traceability.

erwin for Data Governance and Enterprise Architecture

Using data governance and enterprise architecture in tandem will give you a data-driven architecture, reducing time to value and show true results to your executives.

You can better manage risk because of real-time data coming into the EA space. You can react quicker, answering questions for stakeholders that will ultimately drive business transformation. And you can reinforce the value of your role as an enterprise architect.

erwin Evolve is a full-featured, configurable set of enterprise architecture and business process modeling and analysis tools. It integrates with erwin’s data governance software, the erwin Data Intelligence Suite.

With these unified capabilities, every enterprise stakeholder – enterprise architect, business analyst, developer, chief data officer, risk manager, and CEO – can discover, understand, govern and socialize data assets to realize greater value while mitigating data-related risks.

You can start a free trial of erwin Evolve here.

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erwin Expert Blog Data Governance Data Intelligence

Data Governance Definition, Best Practices and Benefits

Any organziation with a data-driven strategy should understand the definition of data governance. In fact, in light of increasingly stringent data regulations, any organzation that uses or even stores data, should understand the definition of data governance.

Organizations with a solid understanding of data governance (DG) are better equipped to keep pace with the speed of modern business.

In this post, the erwin Experts address:

 

 

Data Governance Definition

Data governance’s definition is broad as it describes a process, rather than a predetermined method. So an understanding of the process and the best practices associated with it are key to a successful data governance strategy.

Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations.

It’s often said that when we work together, we can achieve things greater than the sum of our parts. Collective, societal efforts have seen mankind move metaphorical mountains and land on the literal moon.

Such feats were made possible through effective government – or governance.

The same applies to data. A single unit of data in isolation can’t do much, but the sum of an organization’s data can prove invaluable.

Put simply, DG is about maximizing the potential of an organization’s data and minimizing the risk. In today’s data-driven climate, this dynamic is more important than ever.

That’s because data’s value depends on the context in which it exists: too much unstructured or poor-quality data and meaning is lost in a fog; too little insight into data’s lineage, where it is stored, or who has access and the organization becomes an easy target for cybercriminals and/or non-compliance penalties.

So DG is quite simply, about how an organization uses its data. That includes how it creates or collects data, as well as how its data is stored and accessed. It ensures that the right data of the right quality, regardless of where it is stored or what format it is stored in, is available for use – but only by the right people and for the right purpose.

With well governed data, organizations can get more out of their data by making it easier to manage, interpret and use.

Why Is Data Governance Important?

Although governing data is not a new practice, using it as a strategic program is and so are the expectations as to who is responsible for it.

Historically, governing data has been IT’s business because it primarily involved cataloging data to support search and discovery.

But now, governing data is everyone’s business. Both the data “keepers” in IT and the data users everywhere else within the organization have a role to play.

That makes sense, too. The sheer volume and importance of data the average organization now processes are too great to be effectively governed by a siloed IT department.

Think about it. If all the data you access as an employee of your organization had to be vetted by IT first, could you get anything done?

While the exponential increase in the volume and variety of data has provided unparalleled insights for some businesses, only those with the means to deal with the velocity of data have reaped the rewards.

By velocity, we mean the speed at which data can be processed and made useful. More on “The Three Vs of Data” here.

Data giants like Amazon, Netflix and Uber have reshaped whole industries, turning smart, proactive data governance into actionable and profitable insights.

And then, of course, there’s the regulatory side of things. The European Union’s General Data Protection Regulation (GDPR) mandates organization’s govern their data.

Poor data governance doesn’t just lead to breaches – although of course it does – but compliance audits also need an effective data governance initiative in order to pass.

Since non-compliance can be costly, good data governance not only helps organizations make money, it helps them save it too. And organizations are recognizing this fact.

In the lead up to GDPR, studies found that the biggest driver for initiatives for governing data was regulatory compliance. However, since GDPR’s implementation better decision-making and analytics are their top drivers for investing in data governance.

Other areas in where well governed data plays an important role include digital transformation, data standards and uniformity, self-service and customer trust and satisfaction.

For the full list of drivers and deeper insight into the state of data governance, get the free 2020 State of DGA report here.

What Is Good Data Governance?

We’re constantly creating new data whether we’re aware of it or not. Every new sale, every new inquiry, every website interaction, every swipe on social media generates data.

This means the work of governing data is ongoing, and organizations without it can become overwhelmed quickly.

Therefore good data governance is proactive not reactive.

In addition, good data governance requires organizations to encourage a culture that stresses the importance of data with effective policies for its use.

An organization must know who should have access to what, both internally and externally, before any technical solutions can effectively compartmentalize the data.

So good data governance requires both technical solutions and policies to ensure organizations stay in control of their data.

But culture isn’t built on policies alone. An often-overlooked element of good data governance is arguably philosophical. Effectively communicating the benefits of well governed data to employees – like improving the discoverability of data – is just as important as any policy or technology.

And it shouldn’t be difficult. In fact, it should make data-oriented employees’ jobs easier, not harder.

What Are the Key Benefits of Data Governance?

Organizations with a effectively governed data enjoy:

  • Better alignment with data regulations: Get a more holistic understanding of your data and any associated risks, plus improve data privacy and security through better data cataloging.
  • A greater ability to respond to compliance audits: Take the pain out of preparing reports and respond more quickly to audits with better documentation of data lineage.
  • Increased operational efficiency: Identify and eliminate redundancies and streamline operations.
  • Increased revenue: Uncover opportunities to both reduce expenses and discover/access new revenue streams.
  • More accurate analytics and improved decision-making: Be more confident in the quality of your data and the decisions you make based on it.
  • Improved employee data literacy: Consistent data standards help ensure employees are more data literate, and they reduce the risk of semantic misinterpretations of data.
  • Better customer satisfaction/trust and reputation management: Use data to provide a consistent, efficient and personalized customer experience, while avoiding the pitfalls and scandals of breaches and non-compliance.

For a more in-depth assessment of data governance benefits, check out The Top 6 Benefits of Data Governance.

The Best Data Governance Solution

Data has always been important to erwin; we’ve been a trusted data modeling brand for more than 30 years. But we’ve expanded our product portfolio to reflect customer needs and give them an edge, literally.

The erwin EDGE platform delivers an “enterprise data governance experience.” And at the heart of the erwin EDGE is the erwin Data Intelligence Suite (erwin DI).

erwin DI provides all the tools you need for the effective governance of your data. These include data catalog, data literacy and a host of built-in automation capabilities that take the pain out of data preparation.

With erwin DI, you can automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable via role-based, contextual views.

With the broadest set of metadata connectors, erwin DI combines data management and DG processes to fuel an automated, real-time, high-quality data pipeline.

See for yourself why erwin DI is a DBTA 2020 Readers’ Choice Award winner for best data governance solution with your very own, very free demo of erwin DI.

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Enterprise Architecture erwin Expert Blog

What Is Enterprise Architecture? – Definition, Methodology & Best Practices

Enterprise architecture (EA) is a strategic planning initiative that helps align business and IT. It provides a visual blueprint, demonstrating the connection between applications, technologies and data to the business functions they support.
Continue reading “What Is Enterprise Architecture? – Definition, Methodology & Best Practices”
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erwin Expert Blog

Let’s Social Distance Together, Register Now for erwin Insights 2020

 

I’m thrilled to officially announce that registration is open for our first global conference as erwin, Inc. erwin Insights 2020 is a free, virtual, two-day event being held October 13-14.

Social distancing doesn’t mean we should stop connecting. In fact, opportunities for personal and professional growth are more important than ever.

That’s why we look forward to bringing together erwin’s global community of users, partners, prospects and friends to engage and explore ideas, experiences, trends and technologies driving data modeling (DM),  data governance and intelligence (DI), and enterprise architecture/business process modeling (EA/BP).

We truly have a fantastic line-up of content including two live keynotes, 20 sessions, “manned booths,” and a virtual networking lounge. You can join remotely as keynotes stream live and/or access sessions on demand after they launch.

The event kicks off on October 13 at 9 a.m. EDT with a live keynote from our CEO, Adam Famularo, on Surviving Radical Disruption with Data Intelligence. The reality is that to survive and thrive in the new world of disorder, enterprise architecture, business processes and data management all depend on one another.

Adam will discuss how data intelligence is the common denominator across business, technology and data domains and how supercharging your organization’s data IQ will enable it to be adaptive, compete more effectively, design better customer journeys, and improve overall performance.

On October 14 at 9 a.m. EDT, Keith Ferrazzi, The New York Times best-selling author of “Who’s Got Your Back,” “Never Eat Alone,” and his newest book, “Leading Without Authority,” hosts Powerful Leadership in Challenging Times. Keith’s 20-year history of transforming C-suite executive teams has made him one of the world’s most sought-after coaches.

Each keynote is followed by technical and customer sessions and panels focused on DI, DM  and EA/BP. These will be led by some of the world’s most notable organizations, including Snowflake, Microsoft, HSBC, Pfizer, E.ON and many more.

We hope you’ll join us!

Check out the full agenda here.

Then register for what is sure to be a fantastic event!

erwin Insights 2020

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Enterprise Architecture erwin Expert Blog

Enterprise Architecture: Secrets to Success

For enterprise architecture, success is often contingent on having clearly defined business goals. This is especially true in modern enterprise architecture, where value-adding initiatives are favoured over strictly “foundational,” “keeping the lights on,” type duties.

But what does enterprise architecture success look like?

Enterprise architecture is central to managing change and addressing key issues facing organizations. Today, enterprises are trying to grow and innovate – while cutting costs and managing compliance – in the midst of a global pandemic.

Executives are beginning to turn more to enterprise architects to help quickly answer questions and do proper planning around a number of key issues. The good news is that this is how enterprise architects stay relevant, and why enterprise architect salaries are so competitive.

Here are some of the issues and questions being raised:

  • Growth: How do we define growth strategies (e.g., M&A, new markets, products and businesses)
  • Emerging Markets: What opportunities align to our business (e.g., managing risk vs ROI and emerging countries)?
  • Technology Disruption: How do we focus on innovation while leveraging existing technology, including artificial intelligence, machine learning, cloud and robotics?
  • Customer Engagement: How can we better engage with customers including brand, loyalty, customer acquisition and product strategy?
  • Compliance and Legislation: How do we manage uncertainty around legislative change (e.g., data protection, personal and sensitive data, tax issues and sustainability/carbon emissions)?
  • Data Overload: How do we find and convert the right data to knowledge (e.g., big data, analytics and insights)?
  • Global Operations: How do we make global operations decisions (e.g., operating strategy, global business services and shared services)?
  • Cost Reduction: What can we do to reduce costs while not impacting the business (e.g., balance growth goals with cost reduction, forecast resources needs vs. revenue)?
  • Talent and Human Capital: How do we retain, empower and manage employees and contractors (e.g., learning and development, acquisition and retention, talent development)

Enterprise architecture

Undeniable Enterprise Architecture Truths & the Secrets to Success

As enterprise architects, we need to overcome certain undeniable truths to better serve our organizations:

  1. Management does not always rely on EA to make critical decisions: They often hire consultants to come in for six months to make recommendations.
  2. Today’s enterprises need to be agile to react quickly: Things change fast in our current landscape. Taking months to perform impact analysis and solution design is no longer viable, and data has to be agile.
  3. Enterprise architecture is about more than IT: EA lives within IT and focuses on IT. As a result it loses its business dimension and support.

What can enterprise architects do to be more successful?

First and foremost, we need to build trust in the information we hold within our repositories. That has been challenging because it takes so long to collect and keep relevant and that means our analyses aren’t always accurate and up to date.

With more governance around the information and processes we use to document that information, we can produce more accurate and robust analyses for a true “as-is” view of the entire organization for better decision-making.

Next, we need to close the information gap between enterprise architecture functions that fail to provide real value to their stakeholders. We also need to reduce the cost of curating and governing information within our repositories.

Taking a business-outcome-driven enterprise architecture approach will enhance the value of enterprise architecture. Effective EA is about smarter decision-making, enabling management to make decisions more quickly because they have access to the right information in the right format at the right time.

Taking a business-outcome approach means enterprise architects should:

  • Understand who will benefit the most from enterprise architecture. While many stakeholders sit within the IT organization, business and C-level stakeholders should be able to gain the most.
  • Understand your leadership’s objectives and pain points, and then help them express them in clear business-outcomes. This will take time and skill, as many business users simply ask for system changes without clearly stating their actual objectives.
  • Review your current EA efforts and tooling. Question whether you are providing or managing data the business does not need, whether you are working too deeply in areas that may not be adding value, or whether you have your vital architecture data spread across too many disconnected tools.

Why erwin for Enterprise Architecture?

erwin has a proven track record supporting enterprise architecture initiatives in large, global enterprises in highly regulated environments, such as critical infrastructure, financial services, healthcare, manufacturing and pharmaceuticals.

Whether documenting systems and technology, designing processes and critical value streams, or managing innovation and change, erwin Evolve will help you turn your EA artifacts into insights for better decisions. And the platform also supports business process modeling and analysis. Click here for a free trial of erwin Evolve.

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Data Intelligence erwin Expert Blog

What Is Data Literacy?

Today, data literacy is more important than ever.

Data is now being used to support business decisions few executives thought they’d be making even six months ago.

With your employees connected and armed with data that paints a clear picture of the business, your organization is better prepared to turn its attention to whatever your strategic priority may be – i.e. digital transformation, customer experience, or withstanding this current (or future) crisis.

So, what is data literacy?

Data Literacy

Data Literacy Definition

Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.

Organizations use data literacy tools to improve data literacy across the organization. A good data literacy tool will include functionality such as business glossary management and self-service data discovery. The end result is an organization that’s more data fluent and efficient in how they store, discover and use their data.

What Is Data Literacy For?

For years, we’ve been saying that “we’re all data people.” When all stakeholders in an organization can effectively “speak data” they can:

  • Better understand and identify the data they require
  • Be more self-sufficient in accessing and preparing the data
  • Better articulate the gaps that exist in the data landscape
  • Share their knowledge and experience with data with other consumers to contribute to the greater good
  • Collaborate more effectively with their partners in data (management and governance) for greater efficiency and higher quality outcomes

Why is Data Literacy Important?

Without good data, it’s difficult to make good decisions.

Data access, literacy and knowledge leads to sound decision-making and that’s key to data governance and any other data-driven effort.

Data literacy enables collaboration and innovation. To determine if your organization is data literate you need to ask two questions:  

  1. Can your employees use data to effectively communicate with each other?
  2. Can you develop and circulate ideas that will help the business move forward?

data literacy and data intelligence

The Data Literacy and Data Intelligence Connection

Businesses that invest in data intelligence and data literacy are better positioned to weather any storm and chart a path forward because they have accurate, trusted data at their disposal.

erwin helps customers turn their data from a burden into a benefit by fueling an accurate, real-time, high-quality data pipeline they can mine for insights that lead to smart decisions for operational excellence.

erwin Data Intelligence (erwin DI) combines data catalog and data literacy capabilities for greater awareness of and access to available data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed.

erwin Data Literacy (DL) is founded on enriched business glossaries and socializing data so all stakeholders can view and understand it within the context of their roles.

It allows both IT and business users to discover the data available to them and understand what it means in common, standardized terms, and automates common data curation processes, such as name matching, categorization and association, to optimize governance of the data pipeline including preparation processes.

erwin DL provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms.

It also includes built-in accountability and workflows to enable data consumers to define and discover data relevant to their roles, facilitate the understanding and use of data within a business context, and ensure the organization is data literate.

With erwin DL, your organization can build glossaries of terms in taxonomies with descriptions, synonyms, acronyms and their associations to data policies, rules and other critical governance artifacts. Other advantages are:

  • Data Visibility & Governance: Visualize and navigate any data from anywhere within a business-centric data asset framework that provides organizational alignment and robust, sustainable data governance.
  • Data Context & Enrichment: Put data in business context and enable stakeholders to share best practices and build communities by tagging/commenting on data assets, enriching the metadata.
  • Enterprise Collaboration & Empowerment: Break down IT and business silos to provide broad access to approved organizational information.
  • Greater Productivity: Reduce the time it takes to find data assets and therefore reliance on technical resources, plus streamline workflows for faster analysis and decision-making.
  • Accountability & Regulatory Peace of Mind: Create an integrated ecosystem of people, processes and technology to manage and protect data, mitigating a wide range of data-related risks and improving compliance.
  • Effective Change Management: Better manage change with the ability to identify data linkages, implications and impacts across the enterprise.
  • Data Literacy, Fluency & Knowledge: Enhance stakeholder discovery and understanding of and trust in data assets to underpin analysis leading to actionable insights.

Learn more about the importance of data literacy by requesting a free demo of erwin Data Intelligence.

erwin Data Intelligence

 

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Enterprise Architecture erwin Expert Blog

What is ArchiMate? Top 6 ArchiMate Benefits

ArchiMate is an enterprise architecture (EA) modeling language from The Open Group that is used to communicate an organization’s enterprise architecture.

In this post:

What is ArchiMate?

Pronounced “AR-ki-mayt”, the modeling language’s name comes from a compounding of “architecture” and “animate.” The name conveys its aim to provide a way to visualize an organization’s EA.

Unlike other modeling languages such as Unified Modeling Language (UML) and Business Process Modeling Notation (BPMN), ArchiMate is designed to be narrow in its scope. The idea being that this makes the standard easier to learn and apply.

It’s narrow scope and ease of understanding could well be the driving force behind ArchiMate’s adoption within the enterprise architecture space.

Additionally, ArchiMate is often seen as a source of great background knowledge for enterprise architecture learning resources for anyone working towards becoming an enterprise architect.

Top 6 ArchiMate Benefits

Some of the key benefits of ArchiMate are:

  • It is an independent and consistent modeling language, meaning organizations and their enterprise architecture projects aren’t tied to vendor-specific tools or individual architects.
  • Its narrow scope and carefully developed concepts combine to provide organizations clear and actionable insight into their enterprise architectures.
  • Its narrow scope makes it easier to learn, and many enterprise architects use ArchiMate as a way to learn more about EA in general.
  • Its place under the Open Group umbrella means it is well integrated with the popular architecture framework, TOGAF.
  • It was designed to share concepts with existing modeling languages including UML and BPMN, and it can work as a bridge between them.
  • It’s tried and tested from an enterprise perspective and an in-demand certification for enterprise architects, so there are relatively low risks associated with adopting it.

ArchiMate in Practice

With ArchiMate, users have a common language through which they can discuss an organization’s business processes, organizational structures, systems and infrastructure.

By establishing a recognized standard to describe, analyze and map out an organization’s EA, organizations can limit the misunderstandings and ambiguity.

Such standardization is an important factor in ensuring consistency between departments, projects and even enterprise architects themselves.

It means that stakeholders can more easily acknowledge, understand and mitigate the consequences of making changes to an organization’s systems or structure.

Parallels can be found in construction, where enterprise architecture’s nomenclature is derived. As with enterprise architecture, architects in the construction space build and label diagrams based on pre-established frameworks.

This means that the project can be reviewed by different stakeholders, and the diagrams can be untethered from any one architect.

As well as insulating the project from stalling should the/an architect leave, the approach speeds up time-to-markets by making communication more efficient.

How it Works

With ArchiMate, organizations can use visual notations as a representation of their EA over time, by using “layers” and “aspects.”

ArchiMate Specification - Aspects & Layers

Layers:

Layers are broken down into business (yellow), application (blue) and technology (green), and in each layer, three aspects are noted.

Aspects:

  1. Active structure elements can be subdivided into internal and external elements.
    1. Internal active structure elements are subjects that can perform behavior.
    2. External active elements represent a point of access where one or more services are provided to the environment.
  2. Behavior elements can also be subdivided into internal and external elements.
    1. Internal behavior elements represent a unit of activity that can be performed by one or more active structure elements.
    2. External behavior elements, called a service, represent an explicitly defined exposed behavior.
  3. Passive structure elements represent an element on which a behavior is performed.

The framework is populated with “concepts,” which act as visual indications of the nature of elements.

The following is an example of an ArchiMate Core Metamodel, demonstrating how concepts are structured across aspects and layers:

ArchiMate Core Metamodel
ArchiMate 3.0.1 Core Metamodel

 

Getting ArchiMate Certified

As with The Open Group’s Architecture Framework (TOGAF), a certification program is available for ArchiMate users.

The certification program helps maintain the standard and instills organizations with greater confidence in the enterprise architects they employ or contract.

Due to ArchiMate’s recognition within the EA discipline, ArchiMate certified architects are in greater demand and can command better salaries.

The Open Group have a number of resources that address how you can obtain an ArchiMate accreditation.

The erwin Expert Guide to Enterprise Architecture

Although ArchiMate helps standardize the language we use to describe an organization’s enterprise architecture, it’s just one piece of the puzzle.

The benefits of implementing an enterprise architecture management suite (EAMS)  go beyond just the benefits of using the ArchiMate modeling language.

With an EAMS, organizations can introduce more structure into the way they manage EA. Frameworks and common modeling languages help introduce efficiency, enable agility and improve collaboration.

Some enterprise architecture tools come with an array of collaborative features that make ad-hoc collaboration such as sharing PDFs look primitive in comparison.

For a more complete understanding of enterprise architecture, including its implementation and its benefits, get the erwin Expert’s Guide to Enterprise Architecture.

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erwin Expert Blog Data Intelligence

Four Steps to Building a Data-Driven Culture

data-driven culture

Fostering organizational support for a data-driven culture might require a change in the organization’s culture. But how?

Recently, I co-hosted a webinar with our client E.ON, a global energy company that reinvented how it conducts business from branding to customer engagement – with data as the conduit.

There’s no doubt E.ON, based in Essen, Germany, has established one of the most comprehensive and successful data governance programs in modern business.

For E.ON, data governance is not just about data management but also about using information to increase efficiencies. The company needed to help its data scientists and engineers improve their knowledge of the data, find the best data for use at the best time, and put the data in the most appropriate business context.

As an example, E.ON was able to improve data quality, detect redundancies, and create a needs-based, data-use environment by applying a common set of business terms across the enterprise.

Avoiding Hurdles

Businesses have not been able to get as much mileage out of their data governance efforts as hoped, chiefly because of how it’s been handled. And data governance initiatives sometimes fail because organizations tend to treat them as siloed IT programs rather than multi-stakeholder imperatives.

Even when business groups recognize the value of a data governance program and the potential benefits to be derived from it, the IT group traditionally has owned the effort and paid for it.

Despite enterprise-wide awareness of the importance of data governance, a troublingly large number of organizations continue to stumble because of a lack of executive support.

IT and the business will need to take responsibility for selling the benefits of data governance across the enterprise and ensure all stakeholders are properly educated about it.

IT may have to go it alone, at least initially, educating the business on the risks and rewards of data governance and the expectations and accountabilities in implementing it. The business needs to have a role in the justification.

Being a Change Agent

Becoming a data-driven enterprise means making decisions based on facts. It requires a clear vision, strategy and disciplined execution. It also must be well thought out, understood and communicated to others – from the C-suite on down.

For E.ON, the board supported and drove a lot of the thinking that data has to be at the center of everything to reimagine the company. But the data team still needed to convince the head of every one of the company’s hundreds of legal entities to support the digital transformation journey. As a result, the team went on a mission to spread the message.

“The biggest challenge was change management — convincing people to be part of the journey. It is very often underestimated,” said Romina Medici, E.ON’s Program Manager for Data Management and Governance. “Technology is logical, so you can always understand it. Culture is more complex and more diverse.”

She said that ultimately the “communication (across the organization) was bottom up and top down.”

Four Steps to Building a Data-Driven Culture

1. Accelerate Time to Value: Data governance isn’t a one-off project with a defined endpoint. It’s an on-going initiative that requires active engagement from executives and business leaders. The ability to make faster decisions based on data is one way to make the organization pay attention.

2. Ensure Company-Wide Compliance: Compliance isn’t just about government regulations. In today’s business environment, we’re all data people. Everyone in the organization needs to commit to data compliance to ensure high-quality data.

3. Demand Trusted Insights Based on Data Truths: To make smart decisions, you can’t have multiple sets of numbers. Everyone needs to be in lockstep, using and basing decisions on the same data.

4. Foster Data-Driven Collaboration: We call this “social data governance,” meaning you foster collaboration across the business, all the time. 

A data-driven approach has never been more valuable to addressing the complex yet foundational questions enterprises must answer. Organizations that have their data management, data governance and data intelligence houses in order are much better positioned to respond to challenges and thrive moving forward.

As demonstrated by E.ON, data-driven cultures start at the top – but need to proliferate up and down, even sideways.

Business transformation has to be based on accurate data assets within the right context, so organizations have a reliable source of truth on which to base their decisions.

erwin provides a with the data catalog, lineage, glossary and visualization capabilities needed to evaluate the business in its current state and then evolve it to serve new objectives.

Request a demo of the erwin Data Intelligence Suite.

Data Intelligence Solution: Data Catalog, Data Literacy and Automation Tools

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Enterprise Architecture erwin Expert Blog

What Is TOGAF? The Open Group Architecture Framework

The Open Group Architecture Framework (TOGAF) is a type of enterprise architecture (EA) framework.

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Enterprise Architecture erwin Expert Blog

Enterprise Architect Salary: What to Expect and Why

Enterprise architecture plays a key role in the modern enterprise, so the average enterprise architect salary reflects the demand.

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Average Enterprise Architect Salary

LinkedIn data from 808 self-reporting enterprise architects indicates that the average enterprise architect’s salary is $146,000.

As with most professions, enterprise architect salaries tend to increase with years of experience.

Enterprise architects who add enterprise architecture certifications to their resume also report higher earnings.

In Glassdoor’s 25 Best Jobs in the UK for 2020 report, enterprise architect came out on top.

It is “the first technology role to be named the ‘best job in the UK,’ beating marketing, finance and ops roles that have traditionally taken the top spot,” according to Amanda Stansell, Senior Economic Research Analyst at Glassdoor.

The report looked beyond just salary as a factor, encompassing job openings and job satisfaction as well.

Interestingly, DevOps engineer is one of 11 new roles to make the list.

Considering the trends of digital transformation and agile business, DevOps engineer and enterprise architect’s inclusion among the top jobs is likely linked.

What Does an Enterprise Architect Do?

An enterprise architect – not to be confused with a solutions architect, technical architect or data architect – is a specialist in collaborating to establish desired business outcomes by introducing the infrastructure necessary to achieve them.

 

For more info about how enterprise architecture differs from solutions, technical and data architecture, see:

Enterprise architects typically delegate the technical- and solution-specific tasks to technical and solution architects. This makes leadership, management and communication skills vital arrows in the enterprise architect’s quiver.

Often reporting to C-level roles such as the chief information officer, enterprise architects:

  • Align business and IT functions with the organization’s goals
  • Assess/analyze an enterprise’s systems and assets to establish both redundancies and current architecture gaps
  • Analyze risk and impact related to the acquisition of new systems or phasing out/changing current systems
  • Collaborate with stakeholders from different areas of the business to ensure a complete view of the enterprise architecture

Enterprise Architect Salary and Job Description

Enterprise Architect Salary Expectations

As with any career, enterprise architect salary expectations are driven by their value to an organization and the responsibilities they are expected to take on.

Enterprise architects help organizations develop a holistic, informative view of the organization to inform strategic planning.

Increasingly, the latter half of the above statement is foregrounded. Data-driven business has seen enterprise architects evolve from providing a support-focused, foundational function to one that is forward-thinking and business-outcome oriented.

Of course, enterprise architects have always been concerned with the latter but were hamstrung by their perception as working from an ivory tower.

Now the changing business landscape, as well as improvements to enterprise architecture management systems (EAMS) that have made them more collaborative, is helping organizations see them in a new light.

What’s Influencing Enterprise Architecture Salaries?

The demand for enterprise architects is increasing because of colossal changes in the way most enterprises do business.

And from SMBs to large, multinational corporations, most organizations now manage more increasingly complex architectures, more so than just a decade ago.

Organizations striving to optimize and not just manage their enterprise architectures need both the personnel and systems to facilitate this.

The Tools Enterprise Architects Need to Thrive

The demand for enterprise architects isn’t increasing because organizations want to recycle past approaches to the domain.

No, the days of enterprise architecture being viewed primarily as a support function are over.

Today, enterprise architects serve a valuable strategic function within the organization and should be equipped to succeed.

Enterprise architecture management systems (EAMS), as opposed to limited or repurposed Office tools that have to be updated and managed independently, enable enterprise architects to create a central source of truth for as-is and to-be states.

This prevents oversights and errors, allowing enterprise architects to be proactive in guiding an organization in fulfilling its mission with the necessary systems.

Learn more about enterprise architecture management systems.