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erwin Expert Blog

The Value of Enterprise Architecture to Innovation and Digital Transformation

The value of enterprise architecture to innovation management and digital transformation is clear.

Innovation management is about quickly and effectively implementing your organization’s goals through the adoption of innovative ideas, products, processes and business models.

Most organizations are beginning to realize that to drive business growth and maintain a competitive advantage, innovation needs to be uncovered, documented and socialized rapidly but with care to ensure maximum value.

The process of innovation needs to be managed and governed in the organization because it’s an important facet of a company’s overall function. And ultimately, it is a process in which the business and IT need to collaborate to drive the transformation.

Enterprise Architecture

How Enterprise Architecture Guides Innovation and Transformation

Once you develop a good idea, you need to understand how to implement it successfully, which is why enterprise architecture (EA) is a perennial innovation tool.

Investment in a particular idea requires a degree of confidence that a product, service, IT component or business process is going to make it to market or positively change the business.

Conversely, IT requires traceability back to the innovation that drove it. Without such traceability, it’s difficult to see the value of IT and how it drives the business. And to make it all work seamlessly, it needs to be the business of both those who innovate and those who manage EA.

  • Get the eBook: Enterprise Architecture and Innovation Management

Without EA and an enterprise architecture tool, decision-making expanding from the right ideas and requirements is much more of a lottery.

And while there are more and more projects in progress and a rise in agile development approaches, companies simply do not invest enough time in combining innovation and EA.

DevOps and continuous delivery are prime candidates for connection to innovation management. In the context of speed and time to market, where the frequency, capability and release cycles are key to competitive advantage, EA’s support of decision-making allows innovative ideas to be implemented without costly mistakes.

Strategic Enterprise Architecture Planning Creates Digital Leaders

Innovation management and digital transformation go hand in hand these days, and EA teams can play an integral role, according to a study from McKinsey and Henley Business School.

The study highlighted the need for enterprise architects to facilitate digital transformation by managing technological complexity and setting a course for the development of their companies’ IT landscapes.

One of the stunning results of the study was that 100 percent of respondents from companies that identified themselves as “digital leaders” said their architecture teams develop and update models of what the business’s IT architecture should look like in the future.

In contrast, just 58 percent of respondents from other companies said they adhere to this best practice.

There are three broad states of EA maturity within most enterprises. Where does yours land?

1. Under design

  • Does not exist (or is covered by IT)
  • Information barely managed (or managed on an ad-hoc basis)
  • Knowledge resides mainly in people and disparate other media

2. Existing but needs improvement

  • Efforts have been made to collate and manage information
  • In disparate media, but usually more organized
  • A potential attempt at solutioning has been made
  • Some (manual) reporting is possible

3. Mature and works great!

  • Distinct function within the organization
  • Initial data aggregation and collation is completed
  • There is an EA solution deployed and used
  • Dashboards and reports are available

See also:

Enterprise Architecture Turns Around Inefficiencies

Envision a scenario in which you’re part of the EA team at an energy company with 30,000 wind turbines. When engineers inspect the wind turbines, they record the results on paper forms.

An administrator then uses this paperwork to enter information into the database so repairs can be scheduled. This manual, low-tech approach that relies on good penmanship equates to losing 10 days per year due to manual paperwork that delays necessary repairs; and work-order entry makes up about 25 percent of an admin’s day.

How could technology be used to improve this process? Is there an opportunity for digital transformation? Yes.

By deploying tablets in the field, engineers would be able to review the specs and history of each wind turbine in real time, note the necessary repairs, and then specify the work orders onsite. By driving the innovation process with EA, it’s possible to:

  • Demonstrate how different types and groups of users collaborate within the tool from ideation through execution.
  • Graphically illustrate the ideas, people and support for categories of ideation and innovation
  • Leverage mode 2 activities, such as business scenario planning, persona profiles and strategic value assessments as part of the process.
  • Manage iterative solution or application development projects, leveraging methods such as Kanban, agile, scrum or lean, which help the IT organization pursue a DevOps approach.

Enterprise Architecture at the Heart of Innovation

erwin’s technology roadmap is defined largely by our customers, their needs and requirements, and the trends and initiatives that matter most to their businesses. They are constantly evolving, and so are we.

That’s why we’ve released erwin Evolve, a full-featured, configurable set of EA and business process modeling and analysis tools.

With erwin Evolve, you can map IT capabilities to the business functions they support and determine how people, processes, data, technologies and applications interact to ensure alignment in achieving enterprise objectives.

Such initiatives may include innovation management and digital transformation, as well as cloud migration, portfolio and infrastructure rationalization, and regulatory compliance among other use cases.

Click here to test drive erwin Evolve today.

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erwin Expert Blog Enterprise Architecture

Change Management: Enterprise Architecture for Managing Change

As organization’s technologies and digital strategies mature, enterprise architecture for change management is becoming increasingly relevant. 

Enterprise architecture’s holistic view of the organization is perfect for understanding how an organization’s assets are interconnected. 

This understanding is crucial when an organization is looking to change from within. But it is perhaps even more crucial still, when external factors and disruption force an organization into change.

Ch-ch-ch-ch-changes …

Organizations in every industry are navigating digital transformation, so change management is an important element to consider as part of those efforts.

And organizations that embrace change often achieve greater success.

Whether in the early stages of implementing a digital strategy or in the midst of a new technology deployment, change management plays a crucial role.

What Is Change Management?

Change management describes the process(es) an organization will undertake to ensure changes to business operations, systems and other assets cause as little disruption as possible.

For example, a change in systems might require employees to be retrained, taking them away from more immediate, value-creating tasks.

A systems change also could disrupt business operations more directly – if it turns out a new system is incompatible with the current technology infrastructure.

Why Is Change Management Important?

Organizations are faced with constant change. Even industries historically resistant to it, such as financial services and healthcare, are now transforming proactively and at a rapid rate.

Successfully implementing and managing any change, but especially those involving technology, requires an understanding of how it will impact the business – particularly when there are impacts outside the intended goal.

While good ideas help a business grow, sometimes their implementations cause stumbles. Most often that’s because there’s a disconnect between an innovative idea and how it becomes reality.

Such disconnects result in redundant technology and processes, inefficient use of resources, and/or missed opportunities.

With effective change management, organizations usually realize faster implementations and lower costs. An organization with a better understanding of a proposed change is less likely to run into the problems that can derail new initiatives.

Smart change management also can help organizations future-proof their operations, anticipating issues such as systems becoming redundant or outdated earlier than expected.

Change Management and Enterprise Architecture

In large organizations, enterprise architecture (EA) has long been recognized as an effective mechanism for change management. It facilitates an organization’s efforts in assessing the impact of change and making recommendations for target states that support business objectives.

New solution architectures also are being used to successfully assess solution alternatives to support these target states.

EA often delivers the business use cases that justify the incorporation of ideas into operations. However, organizations may find its success limited if the EA function continues to operate in an ivory tower.

Historically, the EA group often has been disconnected from business stakeholders as well as the IT project teams assigned to deliver the solution. This disconnect can lead to the EA team suffering from a lack of commitment from the wider organization and thus their recommendations are ignored.

As a result, ideas are adopted without rigorous scrutiny, including the impacts of their execution and potential ripple effects on other projects.

What’s needed is an integrated approach that marries the EA team’s knowledge with a process for managing ideas and innovation.

change management enterprise architecture

Using Enterprise Architecture to Manage Ideation Through Implementation

A strategic planning approach – from assessment and impact and investment analysis through delivery – ensures ideas are captured, analyzed and shared in a structured process.

Feedback is provided to the originator, and the right stakeholders are involved in making the right decisions about IT projects based on sound business cases. Then both communities feel empowered to make changes.

An integrated, strategic planning environment brings a federated view of information from across the organization so that it can be shared. It helps organizations analyze and prioritize ideas, feed them into EA for analysis, and compile a business case.

With all stakeholders reviewing information and providing feedback on proposed projects, everyone can understand how the new ideas fit into the corporate strategy and have a voice in systematically managing the changes.

Plus they can be executed in near real time, allowing the organization to react quickly to seize market advantage.

Organizations looking to adopt such an approach to change management would benefit from an enterprise architecture tool.

erwin Evolve is one such enterprise architecture tool and a solution addressing both enterprise architecture and business process modeling and analysis use cases.

Users employ erwin Evolve to effectively tame complexity, manage change and increase operational efficiency. Its many benefits include:

  • Creation & Visualization of Complex Models: Harmonize EA/BP modeling capabilities for greater visibility, control and intelligence in managing any use case.
  • Powerful Analysis: Quickly and easily explore model elements, links and dependencies, plus identify and understand the impact of changes through intuitive impact analysis.
  • Documentation & Knowledge Retention: Capture, document and publish information for key business functions to increase employee education and awareness and maintain institutional knowledge, including standard operating procedures.
  • Democratization & Decision-Making: Break down organizational silos and facilitate enterprise collaboration among those both in IT and business roles for more informed decisions that drive successful outcomes.
  • Agility & Efficiency: Achieve faster time to actionable insights and value with integrated views across initiatives to understand and focus on business outcomes.
  • Lower Risks & Costs: Improve performance and profitability with harmonized, optimized and visible processes to enhance training and lower IT costs.

Recent enhancements include web-based diagramming for non-IT users, stronger document generation and analytics, TOGAF support, improved modeling and navigation through inferred relationships, new API extensions, and modular packaging so customers can choose the components that best meet their needs.

Try erwin Evolve now with a free, cloud-based trial – your work will be saved and carried over when you buy.

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erwin Expert Blog

Enterprise Architecture and Business Process Modeling Tools Have Evolved

Enterprise architecture (EA) and business process (BP) modeling tools are evolving at a rapid pace. They are being employed more strategically across the wider organization to transform some of business’s most important value streams.

Recently, Glassdoor named enterprise architecture the top tech job in the UK, indicating its increasing importance to the enterprise in the tech and data-driven world.

Whether documenting systems and technology, designing processes and value streams, or managing innovation and change, organizations need flexible but powerful EA and BP tools they can rely on for collecting relevant information for decision-making.

It’s like constructing a building or even a city – you need a blueprint to understand what goes where, how everything fits together to support the structure, where you have room to grow, and if it will be feasible to knock down any walls if you need to.

 

Data-Driven Enterprise Architecture

 

Without a picture of what’s what and the interdependencies, your enterprise can’t make changes at speed and scale to serve its needs.

Recognizing this evolution, erwin has enhanced and repackaged its EA/BP platform as erwin Evolve.

The combined solution enables organizations to map IT capabilities to the business functions they support and determine how people, processes, data, technologies and applications interact to ensure alignment in achieving enterprise objectives.

These initiatives can include digital transformation, cloud migration, portfolio and infrastructure rationalization, regulatory compliance, mergers and acquisitions, and innovation management.

Regulatory Compliance Through Enterprise Architecture & Business Process Modeling Software

A North American banking group is using erwin Evolve to integrate information across the organization and provide better governance to boost business agility. Developing a shared repository was key to aligning IT systems to accomplish business strategies, reducing the time it takes to make decisions, and accelerating solution delivery.

It also operationalizes and governs mission-critical information by making it available to the wider enterprise at the right levels to identify synergies and ensure the appropriate collaboration.

EA and BP modeling are both critical for risk management and regulatory compliance, a major concern for financial services customers like the one above when it comes to ever-changing regulations on money laundering, fraud and more. erwin helps model, manage and transform mission-critical value streams across industries, as well as identify sensitive information.

Additionally, when thousands of employees need to know what compliance processes to follow, such as those associated with regulations like the General Data Protection Regulation (GDPR), ensuring not only access to proper documentation but current, updated information is critical.

The Advantages of Enterprise Architecture & Business Process Modeling from erwin

The power to adapt the EA/BP platform leads global giants in critical infrastructure, financial services, healthcare, manufacturing and pharmaceuticals to deploy what is now erwin Evolve for both EA and BP use cases. Its unique advantages are:

  • Integrated, Web-Based Modeling & Diagramming: Harmonize EA/BP capabilities with a robust, flexible and web-based modeling and diagramming interface easy for all stakeholders to use.
  • High-Performance, Scalable & Centralized Repository: See an integrated set of views for EA and BP content in a central, enterprise-strength repository capable of supporting thousands of global users.
  • Configurable Platform with Role-Based Views: Configure the metamodel, frameworks and user interface for an integrated, single source of truth with different views for different stakeholders based on their roles and information needs.
  • Visualizations & Dashboards: View mission-critical data in the central repository in the form of user-friendly automated visualizations, dashboards and diagrams.
  • Third-Party Integrations: Synchronize data with such enterprise applications as CAST, Cloud Health, RSA Archer, ServiceNow and Zendesk.
  • Professional Services: Tap into the knowledge of our veteran EA and BP consultants for help with customizations and integrations, including support for ArchiMate.

erwin Evolve 2020’s specific enhancements include web-based diagramming for non-IT users, stronger document generation and analytics, TOGAF support, improved modeling and navigation through inferred relationships, new API extensions, and modular packaging so customers can choose the components that best meet their needs.

erwin Evolve is also part of the erwin EDGE with data modeling, data catalog and data literacy capabilities for overall data intelligence.

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erwin Expert Blog

Data Governance and Metadata Management: You Can’t Have One Without the Other

When an organization’s data governance and metadata management programs work in harmony, then everything is easier.

Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.

Creating and sustaining an enterprise-wide view of and easy access to underlying metadata is also a tall order.

The numerous data types and data sources that exist today weren’t designed to work together, and data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration.

Therefore, most enterprises have encountered difficulty trying to master data governance and metadata management, but they need a solid data infrastructure on which to build their applications and initiatives.

Without it, they risk faulty analyses and insights that effect not only revenue generation but regulatory compliance and any number of other organizational objectives.

Data Governance Predictions

Data Governance Attitudes Are Shifting

The 2020 State of Data Governance and Automation (DGA) shows that attitudes about data governance and the drivers behind it are changing – arguably for the better.

Regulatory compliance was the biggest driver for data governance implementation, according to the 2018 report. That’s not surprising given the General Data Protection Regulation (GDPR) was going into effect just six months after the survey.

Now better decision-making is the primary reason to implement data governance, cited by 60 percent of survey participants. This shift suggests organizations are using data to improve their overall performance, rather than just trying to tick off a compliance checkbox.

We’re pleased to see this because we’ve always believed that IT-siloed data governance has limited value. Instead, data governance has to be an enterprise initiative with IT and the wider business collaborating to limit data-related risks and determine where greater potential and value can be unleashed.

Metadata Management Takes Time

About 70 percent of DGA report respondents – a combination of roles from data architects to executive managers – say they spend an average of 10 or more hours per week on data-related activities.

Most of that time is spent on data analysis – but only after searching for and preparing data.

A separate study by IDC indicates data professionals actually spend 80 percent of their time on data discovery, preparation and protection and only 20 percent on analysis.

Why such a heavy lift? Finding metadata, “the data about the data,” isn’t easy.

When asked about the most significant bottlenecks in the data value chain, documenting complete data lineage leads with 62 percent followed by understanding the quality of the source data (58 percent), discovery, identification and harvesting (55 percent), and curating data assets with business context (52%.)

So it make sense that the data operations deemed most valuable in terms of automation are:

  • Data Lineage (65%)
  • Data Cataloging (61%)
  • Data Mapping (53%)
  • Impact Analysis (48%)
  • Data Harvesting (38%)
  • Code Generation (21%)

But as suspected, most data operations are still manual and largely dependent on technical resources. They aren’t taking advantage of repeatable, sustainable practices – also known as automation.

The Benefits of Automating Data Governance and Metadata Management Processes

Availability, quality, consistency, usability and reduced latency are requirements at the heart of successful data governance.

And with a solid framework for automation, organizations can generate metadata every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed.

Other benefits of automating data governance and metadata management processes include:

  • Better Data Quality – Identification and repair of data issues and inconsistencies within integrated data sources in real time
  • Quicker Project Delivery – Acceleration of Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc.
  • Faster Speed to Insights – Reversing the 80/20 rule that keeps high-paid knowledge workers too busy finding, understanding and resolving errors or inconsistencies to actually analyze source data
  • Greater Productivity & Reduced Costs – Use of automated, repeatable processes to for metadata discovery, data design, data conversion, data mapping and code generation
  • Digital Transformation – Better understanding of what data exists and its potential value to improve digital experiences, enhance digital operations, drive digital innovation and build digital ecosystems
  • Enterprise Collaboration – The ability for IT and the wider business to find, trust and use data to effectively meet organizational objectives

To learn more about the information we’ve covered in today’s blog, please join us for our webinar with Dataversity on Feb. 18.

Data Governance Webinar

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erwin Expert Blog

8 Tips to Automate Data Management

As organizations deal with managing ever more data, the need to automate data management becomes clear.

Last week erwin issued its 2020 State of Data Governance and Automation (DGA) Report. The research from the survey suggests that companies are still grappling with the challenges of data governance — challenges that will only get worse as they collect more data.

One piece of the research that stuck with me is that 70% of respondents spend 10 or more hours per week on data-related activities. Searching for data was the biggest time-sinking culprit followed by managing, analyzing and preparing data. Protecting data came in last place.

In 2018, IDC predicted that the collective sum of the world’s data would grow from 33 zettabytes (ZB) to 175 ZB by 2025. That’s a lot of data to manage!

Here’s the thing: you do not need to waste precious time, energy and resources to search, manage, analyze, prepare or protect data manually. And unless your data is well-governed, downstream data analysts and data scientists will not be able to generate significant value from it. So, what should you do?  The answer is clear. It’s time to automate data management. But how?

Automate Data Management

How to Automate Data Management

Here are our eight recommendations for how to transition from manual to automated data management:

  • 1) Put Data Quality First:
    Automating and matching business terms with data assets and documenting lineage down to the column level are critical to good decision making.
  • 2) Don’t Ignore Data Lineage Complexity:
    It’s a risky endeavor to support data lineage using a manual approach, and businesses that attempt it that way will find that it’s not sustainable given data’s constant movement from one place to another via multiple routes- and doing it correctly down to the column level.
  • 3) Automate Code Generation:
    Mapping data elements to their sources within a single repository to determine data lineage and harmonize data integration across platforms reduces the need for specialized, technical resources with knowledge of ETL and database procedural code.
  • 4) Use Integrated Impact Analysis to Automate Data Due Diligence:
    This helps IT deliver operational intelligence to the business. Business users benefit from automating impact analysis to better examine value and prioritize individual data sets.
  • 5) Catalog Data:
    Catalog data using a solution with a broad set of metadata connectors so all data sources can be leveraged.
  • 6) Stress Data Literacy Across the Organization:
    There’s a clear connection to data literacy because of its foundation in business glossaries and socializing data so that all stakeholders can view and understand it within the context of their roles.
  • 7) Make Automation Standard Practice:
    Too many companies are still living in a world where data governance is a high-level mandate and not a practically implemented one.
  • 8) Create a Solid Data Governance Strategy:
    Craft your data governance strategy before making any investments. Gather multiple stakeholders—both business and IT—with multiple viewpoints to discover where their needs mesh and where they diverge and what represents the greatest pain points to the business. 

The Benefits of Data Management Automation

With data management automation, data professionals can meet their organization’s data needs at a fraction of the cost of the traditional, manual way.

Some of the benefits of data management automation are:

  • Centralized and standardized code management with all automation templates stored in a governed repository
  • Better quality code and minimized rework
  • Business-driven data movement and transformation specifications
  • Superior data movement job designs based on best practices
  • Greater agility and faster time-to-value in data preparation, deployment and governance
  • Cross-platform support of scripting languages and data movement technologies

For a deeper dive on how to automate data management and to view the full research, download a copy of erwin’s 2020 State of Data Governance and Automation report.

2020 Data Governance and Automation Report

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erwin Expert Blog

Data Governance Automation: What’s the Current State of Data Governance and Automation?

A new study into data governance automation indicates organizations are prioritising value-adding use cases, over efforts concerning regulatory compliance.

Continue reading “Data Governance Automation: What’s the Current State of Data Governance and Automation?”
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erwin Expert Blog

Types of Data Models: Conceptual, Logical & Physical

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose.

  • Conceptual Data Models: High-level, static business structures and concepts
  • Logical Data Models: Entity types, data attributes and relationships between entities
  • Physical Data Models: The internal schema database design

An organization’s approach to data modeling will be influenced by its particular needs and the goals it is trying to reach, as explained here:

 

But with the different types of data models, an organization benefits from using all three, depending on the information it wishes to convey and the use cases it wants to satisfy.

That’s because all three types of data models have their advantages and ideal instances in which they should be applied.

The conceptual data model should be used to organize and define concepts and rules.
Typically, business stakeholders and data architects will create such a model to convey what a system contains.

In contrast, the logical data models and physical data models are concerned with how such systems should be implemented.

Like the conceptual data model, the logical data model is also used by data architects, but also will be used by business analysts, with the purpose of developing a database management system (DBMS)-agnostic technical map of rules and structures.

The physical data model is used to demonstrate the implementation of a system(s) using a specific DBMS and is typically used by database analysts (DBAs) and developers.

Data Modeling Data Goverance

Choosing Between Different Types of Data Models for Business Stakeholders: Focus on What’s Important

Oftentimes, data professionals want the full picture found in logical and physical data models. But data professionals aren’t the sole audience for data models.

Stakeholders from the wider business – business leaders, decision-makers, etc. – are less likely less concerned with the specifics than with the outcomes.

Therefore, when using a data model to communicate with such stakeholders, the conceptual data model should not be ignored.

As outlined above, different types of data models will be most applicable – or effective – depending on their context.

To determine context, you have to look at who the data model is being created for and what it will be used to communicate.

An important part of communication is making concepts understandable and using terms that are meaningful to the audience.

Another key aspect is making the information readily available. While it may be feasible to have working sessions with stakeholders to review a logical and/or physical data model, it’s not always possible to scale these workshops to everyone within the organization.

In any data governance endeavour, it’s a best practice to prioritize business-critical data elements and relate them to key business drivers. This approach helps gain the buy-in and interest of business users – essential factors in getting projects of the ground.

The same mode of thinking can and should be applied to data models.

Although it may be tempting to always include fully realized and in-depth data models to paint the fullest picture possible, that will not resonate with all parties.

When gathering business requirements, for example, it’s often more effective to use a conceptual data model and be creative with its display, as shown below.

Different Types of Data Models: Conceptual Data Model
Figure 1: Conceptual Data Model (from The Business Value of Data Modeling for Data Governance)

The use of icons and graphics help tell the “story” of the model and ultimately the story of the business. In this approach, data models can be read as a sentence, with the entities as the nouns and the relationships as the verbs.

For example, we can group the “customer” and its relationship to/action concerning the “product.” In this case, the model represents that “a customer may buy one or more products” via a visual “story” that makes sense to the business.

This high-level perspective makes it easier to quickly understand information, omitting the more technical information that would only be useful to those in the weeds (e.g., business analysts, DBAs and developers).

In the example above, business leaders will be able to make better informed decisions regarding important distinctions in business rules and definitions.

For instance, in the example above, is a “customer” the same as a “client?”

The support team uses the term “client,” while sales uses the term “customer.” Are the concepts the same? Both buy products and/or services from the company.

But if a product or service has not actually been purchased, perhaps “prospect” would be a better term to use.

Can relationships between customers (or customers and prospects) be evaluated and grouped together by household for better sales and support?

None of these answers can be determined without the input of business stakeholders. By showing the concepts and their interrelationships in an intuitive way, definitions and business rules more easily come to light.

The Right Data Modeling Tool For You …

Different data model types serve different purposes and audiences. erwin Data Modeler (erwin DM) supports all three types of data model to help business and technical stakeholders collaborate on the design of information systems and the databases that power them.

With erwin DM, data models and database designs can be generated automatically to increase efficiency and reduce errors, making the lives of data modelers – and other stakeholders – much more productive.

New to erwin DM? Try the latest version of erwin DM for yourself for free!

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Once you submit the trial request form, an erwin representative will be in touch to verify your request and help you start data modeling.

 

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erwin Expert Blog Data Modeling

Data Modeling Best Practices for Data-Driven Organizations

As data-driven business becomes increasingly prominent, an understanding of data modeling and data modeling best practices is crucial. This posts outlines just that, and other key questions related to data modeling such as “SQL vs. NoSQL.”

What is Data Modeling?

Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface.

Data models provide visualization, create additional metadata and standardize data design across the enterprise.

As the value of data and the way it is used by organizations has changed over the years, so too has data modeling.

In the modern context, data modeling is a function of data governance.

While data modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to accelerate and ensure the overall success of data governance in any organization.

 

 

As well as keeping the business in compliance with data regulations, data governance – and data modeling – also drive innovation.

Companies that want to advance artificial intelligence (AI) initiatives, for instance, won’t get very far without quality data and well-defined data models.

With the right approach, data modeling promotes greater cohesion and success in organizations’ data strategies.

But what is the right data modeling approach?

Data Modeling Data Goverance

Data Modeling Best Practices

The right approach to data modeling is one in which organizations can make the right data available at the right time to the right people. Otherwise, data-driven initiatives can stall.

Thanks to organizations like Amazon, Netflix and Uber, businesses have changed how they leverage their data and are transforming their business models to innovate – or risk becoming obsolete.

According to a 2018 survey by Tech Pro Research, 70 percent of survey respondents said their companies either have a digital transformation strategy in place or are working on one. And 60% of companies that have undertaken digital transformation have created new business models.

But data-driven business success doesn’t happen by accident. Organizations that adapt that strategy without the necessary processes, platforms and solutions quickly realize that data creates a lot of noise but not necessarily the right insights.

This phenomenon is perhaps best articulated through the lens of the “three Vs” of data: volume, variety and velocity.

Data Modeling Tool

Any2 Data Modeling and Navigating Data Chaos

The three Vs describe the volume (amount), variety (type) and velocity (speed at which it must be processed) of data.

Data’s value grows with context, and such context is found within data. That means there’s an incentive to generate and store higher volumes of data.

Typically, an increase in the volume of data leads to more data sources and types. And higher volumes and varieties of data become increasingly difficult to manage in a way that provides insight.

Without due diligence, the above factors can lead to a chaotic environment for data-driven organizations.

Therefore, the data modeling best practice is one that allows users to view any data from anywhere – a data governance and management best practice we dub “any-squared” (Any2).

Organizations that adopt the Any2 approach can expect greater consistency, clarity and artifact reuse across large-scale data integration, master data management, metadata management, Big Data and business intelligence/analytics initiatives.

SQL or NoSQL? The Advantages of NoSQL Data Modeling

For the most part, databases use “structured query language” (SQL) for maintaining and manipulating data. This structured approach and its proficiency in handling complex queries has led to its widespread use.

But despite the advantages of such structure, its inherent sequential nature (“this, then “this”), means it can be hard to operate holistically and deal with large amounts of data at once.

Additionally, as alluded to earlier, the nature of modern, data-driven business and the three VS means organizations are dealing with increasing amounts of unstructured data.

As such in a modern business context, the three Vs have become somewhat of an Achilles’ heel for SQL databases.

The sheer rate at which businesses collect and store data – as well as the various types of data stored – mean organizations have to adapt and adopt databases that can be maintained with greater agility.

That’s where NoSQL comes in.

Benefits of NoSQL

Despite what many might assume, adopting a NoSQL database doesn’t mean abandoning SQL databases altogether. In fact, NoSQL is actually a contraction of “not only SQL.”

The NoSQL approach builds on the traditional SQL approach, bringing old (but still relevant) ideas in line with modern needs.

NoSQL databases are scalable, promote greater agility, and handle changes to data and the storing of new data more easily.

They’re better at dealing with other non-relational data too. NoSQL supports JavaScript Object Notation (JSON), log messages, XML and unstructured documents.

Data Modeling Is Different for Every Organization

It perhaps goes without saying, but different organizations have different needs.

For some, the legacy approach to databases meets the needs of their current data strategy and maturity level.

For others, the greater flexibility offered by NoSQL databases makes NoSQL databases, and by extension NoSQL data modeling, a necessity.

Some organizations may require an approach to data modeling that promotes collaboration.

Bringing data to the business and making it easy to access and understand increases the value of data assets, providing a return-on-investment and a return-on-opportunity. But neither would be possible without data modeling providing the backbone for metadata management and proper data governance.

Whatever the data modeling need, erwin can help you address it.

erwin DM is available in several versions, including erwin DM NoSQL, with additional options to improve the quality and agility of data capabilities.

And we just announced a new version of erwin DM, with a modern and customizable modeling environment, support for Amazon Redshift; updated support for the latest DB2 releases; time-saving modeling task automation, and more.

New to erwin DM? You can try the new erwin Data Modeler for yourself for free!

erwin Data Modeler Free Trial - Data Modeling

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erwin Expert Blog

5 Ways Data Modeling Is Critical to Data Governance

Enterprises are trying to manage data chaos. They might have 300 applications, with 50 different databases and a different schema for each one.

They also face increasing regulatory pressure because of global data regulations, such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. 1.

Then there’s unstructured data with no contextual framework to govern data flows across the enterprise not to mention time-consuming manual data preparation and limited views of data lineage.

For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. It is a tried-and-true practice for lowering data management costs, reducing data-related risks, and improving the quality and agility of an organization’s overall data capability.

And the good news is that it just keeps getting better. Today’s data modeling is not your father’s data modeling software.

While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling continues to expand as the fulcrum of collaboration between data generators, stewards and consumers.

That’s because it’s the best way to visualize metadata, and metadata is now the heart of enterprise data management and data governance/ intelligence efforts.

So here’s why data modeling is so critical to data governance.

1. Uncovering the connections between disparate data elements: Visualize metadata and schema to mitigate complexity and increase data literacy and collaboration across a broad range of data stakeholders. Because data modeling reduces complexity, all members of the team can work around a data model to better understand and contribute to the project.

2. Capturing and sharing how the business describes and uses data: Create and integrate business and semantic metadata to augment and accelerate data intelligence and governance efforts. Data modeling captures how the business uses data and provides context to the data source.

3. Deploying higher quality data sources with the appropriate structural veracity: Automate and enforce data model design tasks to ensure data integrity. From regulatory compliance and business intelligence to target marketing, data modeling maintains an automated connection back to the source.

4. Building a more agile and governable data architecture: Create and implement common data design standards from the start. Data modeling standardizes design tasks to improve business alignment and simplify integration.

5. Governing the design and deployment of data across the enterprise: Manage the design and maintenance lifecycle for data sources. Data modeling provides visibility, management and full version control over the lifecycle for data design, definition and deployment.

Data Modeling Tool

erwin Data Modeler: Where the Magic Happens

erwin has just released a new version of erwin DM, the world’s No. 1 data modeling software for designing, deploying and understanding data sources to meet modern business needs. erwin DM 2020 is an essential source of metadata and a critical enabler of data governance and intelligence efforts.

The new version of erwin DM includes these features:

  • A modern, configurable workspace so users can customize the modeling canvas and optimize access to features and functionality that best support their workflows
  • Support for and model integration from major databases to work effectively across platforms and reuse work product, including native support for Amazon Redshift and updated support for the latest DB2 releases and certification for the latest MS SQL Server releases
  • Model exchange (import/export) to/from a wide variety of data management environments
  • Modeling task automation that saves modelers time, reduces errors and increases work product quality and speed, including a new scheduler to automate the offline reverse-engineering of databases into data models
  • New Quick Compare templates as part of the Complete Compare feature to compare and synchronize data models and sources
  • New ODBC query tool for creating and running custom model and metadata reports
  • Design transformations to customize and automate super-type/sub-type relationships between logical and physical models

erwin DM also integrates with the erwin Data Intelligence Suite (erwin DI) to automatically harvest the metadata in erwin data models for ingestion into the data catalog for better analytics, governance and overall data intelligence.

The role of data modeling in the modern data-driven business continues to expand with the benefits long-realized by database professionals and developers now experienced by a wider range of architects, business analysts and data administrators in a variety of data-centric initiatives.

Click here to test drive of the new erwin DM.

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erwin Expert Blog

Top 10 Data Governance Trends for 2020: Data’s Real Value Comes Into Focus

Understanding the data governance trends for the year ahead will give business leaders and data professionals a competitive edge.

Happy New Year!

Regulatory compliance and data breaches have driven the data governance narrative during the past few years.

While these will remain big data governance trends for 2020, we anticipate organizations will finally begin tapping into the true value of data as the foundation of the digital business model.

In the year ahead, companies with the ability to harness, secure and leverage information effectively will be better equipped than others to promote digital transformation and gain a competitive advantage.

To that end, data is finally no longer just an IT issue. As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IoT, ML, etc.), they will find new ways to get a handle on data quality and focus on data management processes and best practices.

Our predictions for the top data governance trends for 2020

Data Governance Trends

1. More U.S. states and other countries will adopt data regulations:

The General Data Protection Regulation (GDPR) has set the bar, becoming a de facto standard for data security and privacy across Europe as well as other geographies.

On January 1st, the California Consumer Privacy Act (CCPA) went into effect, so more American states and other countries will replicate these types of data regulations.

2. Data catalogs are hot:

While some retail catalogers, like Sears, have fallen on hard times, data catalogs are on the rise because the concept works. Gartner even refers to them as “the new black in data management and analytics.”

Regardless of industry or initiative, all organizations need an organized data catalog to easily find and understand their data sources.

3. Enterprise data gets in the game:

Data literacy, enabling employees to derive meaningful insights from data, is going to emerge as one of the major data governance trends. One way organizations will begin to increase enterprise-wide data literacy is by gamifying it.

Employees who demonstrate analytics expertise, critical thinking and storytelling to promote data literacy throughout the organization will be recognized and rewarded for their efforts.

4. Data finds a soul:

Highly regulated industries will begin to change their philosophies, embracing data ethics as part of their overall business strategy and not just a matter of regulatory compliance.

In addition, ethical artificial intelligence (AI) and machine learning (ML) applications will be used by organizations to ensure their training data sets are well-defined, consistent and of high quality.

5. Government will show the rest of us how to use data to improve service delivery:

Government usually lags behind other industries when it comes to innovation.

However, local government agencies, particularly those responsible for schools and social services, will lead the way in using data governance to drive digital transformation in providing better services, including safety and security.

Gartner Magic Quadrant

6. Data modeling is cool again, seriously:

Today’s data modeling is not your father’s data modeling. While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling will continue to expand as the fulcrum of collaboration between data generators, stewards and consumers.

That’s because it’s the only way to visualize metadata, and metadata is now the heart of enterprise data management and governance/ intelligence efforts.

7. Managing data at the edge:

Adoption has been slower than we thought, but this is the year we believe edge computing will take hold because organizations need to view, manage and secure this data and quickly incorporate it into an automated pipeline.

For example, IoT device data is often integrated and aggregated with other enterprise data sources but still needs to be documented and governed like any other data. Mapping and cataloging these data sources makes this a manageable challenge.

8. Data valuation becomes the holy grail:

Data will finally be treated as a true asset with an actual monetary value assigned to it, just like physical assets, intellectual property and even brands.

The ability to discover, understand, govern and socialize data assets, aka data governance, is crucial to this process especially in ensuring data quality and being able to present compelling, data-dependent use cases.

9. The real CDO stands up:

Does the “CD” stand for “chief data” or “chief digital” officer? These roles have started to blur, but we predict chief data officer will become the more prominent title and in-demand job because data is central to an organization’s success both from a compliance and day-to-day operational perspective.

10. Marketing and enterprise data collide:

As data becomes increasingly democratized throughout the organization, the marketing department will become more connected to the data pipeline and therefore a power player in using data insights to help the enterprise achieve its business goals. Marketing, even, will get its own line item in the IT budget.

Value of Data Intelligence IDC Report