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erwin Expert Blog Data Governance

erwin Positioned as a Leader in Gartner’s 2020 Magic Quadrant for Metadata Management Solutions for Second Year in a Row

erwin has once again been positioned as a Leader in the Gartner “2020 Magic Quadrant for Metadata Management Solutions.”

This year, erwin had the largest move of any player on the Quadrant and moved up significantly in terms of “Ability to Execute” and also in “Vision.”

This recognition affirms our efforts in developing an integrated platform for enterprise modeling and data intelligence to support data governance, digital transformation and any other effort that relies on data for favorable outcomes.

erwin’s metadata management offering, the erwin Data Intelligence Suite (erwin DI), includes data catalog, data literacy and automation capabilities for greater awareness of and access to data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed.

With erwin DI’s automated, metadata-driven framework, organizations have visibility and control over their disparate data streams – from harvesting to aggregation and integration, including transformation with complete upstream and downstream lineage and all the associated documentation.

We’re super proud of this achievement and the value erwin DI provides.

We invite you to download the report and quadrant graphic.

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erwin Expert Blog Data Intelligence

erwin Recognized as a March 2020 Gartner Peer Insights Customers’ Choice for Metadata Management Solutions

We’re excited about our recognition as a March 2020 Gartner Peer Insights Customers’ Choice for Metadata Management Solutions.  Our team here at erwin takes great pride in this distinction because customer feedback has always shaped our products and services.

The Gartner Peer Insights Customers’ Choice is a recognition of vendors in the metadata management solutions market by verified end-user professionals, taking into account both the number of reviews and the overall user ratings. To ensure fair evaluation, Gartner maintains rigorous criteria for recognizing vendors with a high customer satisfaction rate.

erwin’s metadata management offering, the erwin Data Intelligence Suite (erwin DI), is comprised of erwin Data Catalog (erwin DC) and erwin Data Literacy (erwin DL) with built-in automation for greater visibility, understanding and use of enterprise data.

The solutions work in tandem to automate the processes involved in harvesting, integrating, activating and governing enterprise data according to business requirements. This automation results in greater accuracy, faster analysis and better decision-making for data governance and digital transformation initiatives.

Metadata management is key to sustainable data governance and any other organizational effort that is data-driven. erwin DC automates enterprise metadata management, data mapping, data cataloging, code generation, data profiling and data lineage. erwin DL provides integrated business glossary management and self-service data discovery tools so both IT and business users can find data relevant to their roles and understand it within a business context.

Together as erwin DI, these solutions give organizations a complete and clear view of their metadata landscape, including semantic, business and technical elements.

Here are some excerpts from customers:

Everyone at erwin is honored to be named as a March 2020 Customers’ Choice for Metadata Management Solutions. To learn more about this distinction, or to read the reviews written about our products by the IT professionals who use them, please visit Customers’ Choice.

And to all of our customers who submitted reviews, thank you! We appreciate you and look forward to building on the experience that led to this distinction!

Customer input will continue to guide our technology road map and the entire customer journey. In fact, it has influenced our entire corporate direction as we expanded our focus from data modeling to enterprise modeling and data governance/intelligence.

Data underpins every type of architecture – business, technology and data – so it only makes sense that both IT and the wider enterprise collaborate to ensure it’s accurate, in context and available to the right people for the right purposes.

If you have an erwin story to share, we encourage you to join the Gartner Peer Insights crowd and weigh in.

Request a complimentary copy of the Gartner Peer Insights ‘Voice of the Customer’: Metadata Management Solutions (March 2020) report.

Gartner Peer Insights Metadata Management Solutions Report

 

The GARTNER PEER INSIGHTS CUSTOMERS’ CHOICE badge is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. All rights reserved. Gartner Peer Insights Customers’ Choice constitute the subjective opinions of individual end-user reviews, ratings, and data applied against a documented methodology; they neither represent the views of, nor constitute an endorsement by, Gartner or its affiliates.

 

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Very Meta … Unlocking Data’s Potential with Metadata Management Solutions

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata, or the data about the data.

However, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or make other strategic decisions. They don’t know exactly what data they have or even where some of it is.

Quite honestly, knowing what data you have and where it lives is complicated. And to truly understand it, you need to be able to create and sustain an enterprise-wide view of and easy access to underlying metadata.

This isn’t an easy task. Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

As a result, the applications and initiatives that depend on a solid data infrastructure may be compromised, leading to faulty analysis and insights.

Metadata Is the Heart of Data Intelligence

A recent IDC Innovators: Data Intelligence Report says that getting answers to such questions as “where is my data, where has it been, and who has access to it” requires harnessing the power of metadata.

Metadata is generated every time data is captured at a source, accessed by users, moves through an organization, and then is profiled, cleansed, aggregated, augmented and used for analytics to guide operational or strategic decision-making.

In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC.

To flip this 80/20 rule, they need an automated metadata management solution for:

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

Addressing the Complexities of Metadata Management

The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires.

This encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossary maintenance, and metadata management (associations and lineage).

erwin has developed the only data intelligence platform that provides organizations with a complete and contextual depiction of the entire metadata landscape.

It is the only solution that can automatically harvest, transform and feed metadata from operational processes, business applications and data models into a central data catalog and then made accessible and understandable within the context of role-based views.

erwin’s ability to integrate and continuously refresh metadata from an organization’s entire data ecosystem, including business processes, enterprise architecture and data architecture, forms the foundation for enterprise-wide data discovery, literacy, governance and strategic usage.

Organizations then can take a data-driven approach to business transformation, speed to insights, and risk management.
With erwin, organizations can:

1. Deliver a trusted metadata foundation through automated metadata harvesting and cataloging
2. Standardize data management processes through a metadata-driven approach
3. Centralize data-driven projects around centralized metadata for planning and visibility
4. Accelerate data preparation and delivery through metadata-driven automation
5. Master data management platforms through metadata abstraction
6. Accelerate data literacy through contextual metadata enrichment and integration
7. Leverage a metadata repository to derive lineage, impact analysis and enable audit/oversight ability

With erwin Data Intelligence as part of the erwin EDGE platform, you know what data you have, where it is, where it’s been and how it transformed along the way, plus you can understand sensitivities and risks.

With an automated, real-time, high-quality data pipeline, enterprise stakeholders can base strategic decisions on a full inventory of reliable information.

Many of our customers are hard at work addressing metadata management challenges, and that’s why erwin was Named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”

Gartner Magic Quadrant Metadata Management

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erwin Expert Blog

Managing Ideation and Innovation with Enterprise Architecture

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

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

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

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

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

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

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

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

Gartner emphasize the value of enterprise architecture tools

Enterprise Architecture Tools: The Fabric of Your Organization

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

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

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

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

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

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

How Enterprise Architecture Tools Help Communicate the Value of Emerging Technologies

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

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

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

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

Enterprise Architecture Business Process Trial

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erwin Expert Blog

Enterprise Architect: A Role That Keeps Evolving

Enterprise architect is a common job title within IT organizations at large companies, but the term lacks any standard definition. Ask someone on the business side what their organization’s enterprise architects do, and you’ll likely get a response like, “They work with IT,” which is true, but also pretty vague.

What the enterprise architects at your organization do depends in large part on how the IT department is organized. At some organizations, enterprise architects work closely with the software applications in a role that some might refer to as a solution architect.

In other organizations, the role of enterprise architect might carry more traditional IT responsibilities around systems management. Other enterprise architects, especially at large organizations, might specialize in exploring how emerging technologies can be tested and later integrated into the business.

Technology research and advisory firm Gartner predicts that enterprise architects will increasingly move into an internal consultancy function within large organizations. While this use of the role is not currently widespread, it’s easy to see how it could make sense for some businesses.

If, for example, a business sets a goal to increase its website sales by 20 percent in one year’s time, meeting that goal will require that different IT and business functions work together.

The business side might tackle changes to the marketing plan and collect data about website visitors and shoppers, but ultimately they will need to collaborate with someone on the technology side to discuss how IT can help reach that goal. And that’s where an enterprise architect in the role of an internal consultant comes into play.

Each business is going to organize its enterprise architects in a way that best serves the organization and helps achieve its goals.

That’s one of the reasons the enterprise architect role has no standard definition. Most teams consist of members with broad IT experience, but each member will often have some role-specific knowledge. One team member might specialize in security, for example, and another in applications.

Like the tech industry in general, the only constant in enterprise architecture is change. Roles and titles will continue to evolve, and as the business and IT sides of the organization continue to come together in the face of digital transformation, how these teams are organized, where they report, and the types of projects they focus on are sure to change over time.

Enterprise integration architect is one role in enterprise architecture that’s on the rise. These architects specialize in integrating the various cloud and on-premise systems that are now common in the hybrid/multi-cloud infrastructures powering the modern enterprise.

Enterprise Architect: A Role That Keeps Evolving

For the Enterprise Architect, Business Experience Becomes a Valuable Commodity

Regardless of the specific title, enterprise architects need the ability to work with both their business and IT colleagues to help improve business outcomes. As enterprise architecture roles move closer to the business, those with business knowledge are becoming valuable assets. This is especially true for industry-specific business knowledge.

As industry and government compliance regulations, for example, become part of the business fabric in industries like financial services, healthcare and pharmaceuticals, many enterprise architects are developing specializations in these industries that demonstrate their understanding of the business and IT sides of these regulations.

This is important because compliance permeates every area of many of these organizations, from the enterprise architecture to the business processes, and today it’s all enabled by software. Compliance is another area where Gartner’s internal consultancy model for enterprise architects could benefit a number of organizations. The stakes are simply too high to do anything but guarantee all of your processes are compliant.

Enterprise architect is just one role in the modern organization that increasingly stands with one foot on the business side and the other in IT. As your organization navigates its digital transformation, it’s important to use tools that can do the same.

erwin, Inc.’s industry-leading tools for enterprise architecture and business process modeling use a common repository and role-based views, so business users, IT users and those who straddle the line have the visibility they need. When everyone uses the same tools and the same data, they can speak the same language, collaborate more effectively, and produce better business outcomes. That’s something the whole team can support, regardless of job title.

Business Process Modeling Use Cases

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Data Governance Helps Build a Solid Foundation for Analytics

If your business is like many, it’s heavily invested in analytics. We’re living in a data-driven world. Data drives the recommendations we get from retailers, the coupons we get from grocers, and the decisions behind the products and services we’ll build and support at work.

None of the insights we draw from data are possible without analytics. We routinely slice, dice, measure and (try to) predict almost everything today because data is available to be analyzed. In theory, all this analysis should be helping the business. It should ensure we’re creating the right products and services, marketing them to the right people, and charging the right price. It should build a loyal base of customers who become brand ambassadors, amplifying existing marketing efforts to fuel more sales.

We hope all these things happen because all this analysis is expensive. It’s not just the cost of software licenses for the analytics software, but it’s also the people. Estimates for the average salary of data scientists, for example, can be upwards of $118,000 (Glassdoor) to $131,000 (Indeed). Many businesses also are exploring or already use next-generation analytics technology like predictive analytics or analytics supported by artificial intelligence or machine learning, which require even more investment.

If the underlying data your business is analyzing is bad, you’re throwing all this investment away. There’s a saying that scares everyone involved in analytics today: “Garbage in, garbage out.” When bad data is used to drive your strategic and operational decisions, your bad data suddenly becomes a huge problem for the business.

The goal, when it comes to the data you feed your analytics platforms, is what’s often referred to as the “single source of truth,” otherwise known as the data you can trust to analyze and create conclusions that drive your business forward.

“One source of truth means serving up consistent, high-quality data,” says Danny Sandwell, director of product marketing at erwin, Inc.

Despite all of the talk in the industry about data and analytics in recent years, many businesses still fail to reap the rewards of their analytics investments. In fact, Gartner reports that more than 60 percent of data and analytics projects fail. As with any software deployment, there are a number of reasons these projects don’t turn out the way they were planned. Among analytics, however, bad data can turn even a smooth deployment on the technology side into a disaster for the business.

What is bad data? It’s data that isn’t helping your business make the right decisions because it is:

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

More than one-quarter of the respondents to a November 2017 survey by erwin Inc. and UBM said analytics was one of the factors driving their data governance initiatives.

Reputation Management - What's Driving Data Governance

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin-UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organizations but don’t have a formal governance strategy.

Data-driven Analytics: How Important is Data Governance

When data governance helps your organization develop high-quality data with demonstrated value, your IT organizations can build better analytics platforms for the business. Data governance helps enable self-service, which is an important part of analytics for many businesses today because it puts the power of data and analysis into the hands of the people who use the data on a daily basis. A well-functioning data governance program creates that single version of the truth by helping IT organizations identify and present the right data to users and eliminate confusion about the source or quality of the data.

Data governance also enables a system of best practices, subject matter experts, and collaboration that are the hallmarks of today’s analytics-driven businesses.

Like analytics, many early attempts at instituting data governance failed to deliver the expected results. They were narrowly focused, and their advocates often had difficulty articulating the value of data governance to the organization, which made it difficult to secure budget. Some organizations even viewed data governance as part of data security, securing their data to the point where the people who wanted to use it had trouble getting access.

Issues of ownership also hurt early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve these issues of ownership because many are adopting a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy weaves itself into the business and its infrastructure. It is present in the enterprise architecture, the business processes, and it helps organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing because organizations and their data are constantly changing and transforming, so their approach to data governance needs to adjust as they go.

When it comes to analytics, data governance is the best way to ensure you’re using the right data to drive your strategic and operational decisions. It’s easier said than done, especially when you consider all the data that’s flowing into a modern organization and how you’re going to sort through it all to find the good, the bad, and the ugly. But once you do, you’re on the way to using analytics to draw conclusions you can trust.

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You can determine how effective your current data governance initiative is by taking erwin’s DG RediChek.

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Data Modeling in a Jargon-filled World – Big Data & MPP

Big Data & MPP

By now, you’ve likely heard a lot about Big Data. You may have even heard about “the three Vs” of Big Data. Originally defined by Gartner, “Big Data is “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision-making, insight discovery and process optimization.”

Continue reading “Data Modeling in a Jargon-filled World – Big Data & MPP”
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Overcoming Teething Problems in Enterprise Architecture

Historically, the teething problems in enterprise architecture have prevented it from realising its full potential. However, the uptick in data-driven business has made the practice essential, meaning organizations are looking for an enterprise architecture approach that works best for them.

Although they might not always be immediately obvious to the outsider, the value of Enterprise Architects to EAs and even many CIOs is clear. The practice has long been one of the best drivers of business transformation, and IT/business alignment.

Yet over the years, a number of studies indicate hurdles in the early stages of Enterprise Architecture maturity that can stop businesses progressing further with the scheme.

Take Gartner for example. In a 2007 survey from the world renowned tech analyst, Gartner found that 40% of Enterprise Architecture initiatives would be stopped. A later survey (2015) indicates at least a degree of accuracy in the former, as it showed 70% of businesses were looking to either start, or restart an Enterprise Architecture programme.

It seems as if, although businesses are aware of the advantages of an EA practice, actually introducing one can be difficult.

With that said, this blog will covers things to consider when implementing an EA practice to avoid the historical problems in enterprise architecture initiatives and ensure it’s success going forward.

Problems in Enterprise Architecture: EA Needs Time

Businesses that adopt EA on a whim – in that they know they should be doing in EA, but don’t fully understand why – will likely run into this issue.

We must understand that Enterprise Architecture is far from an overnight fix. In fact, it’s the polar opposite. Although EA might highlight areas where overnight and radical change could benefit a business, the initiative itself is a constant and gradual effort in working to align business and IT, aid in strategic planning, and improve processes.

As time goes on, the degree to which these efforts can positively affect the business will also increase, as the EA practice becomes more mature. The added capabilities of EA are indicated in Gartner’s maturity model shown below.

Teething Problems in Enterprise Architecture: EA Maturity Model

This is important for two reasons. Firstly, a maturing Enterprise Architecture practice implies business growth, and so more EA has to be done in order to cope, as there is  more to manage.

Secondly, maturing in EA enables businesses to do a different kind of Enterprise Architecture. The typical, Foundational EA tasks – the one’s we refer to as keeping the lights on – will still be carried out. However, a more mature Enterprise Architecture practice can start using EA more aggressively, actioning what is known as Vanguard Enterprise Architecture Enterprise Architecture.

This kind of EA is more proactive, and it’s practitioners focus more on identifying opportunities and disruptions. This is the EA largely responsible for pushing business transformation and innovation, and so their results often have more lucrative, tangible results.

Most practices that abandon EA, do so without moving too far along the maturity model and so in most cases, are only doing entry level, Foundational Enterprise Architecture.

Problems in Enterprise Architecture: EA Needs Attention

Much of EA consists of strategic planning. Thanks to the practice’s macrocosmic (top down) view of the organization, and business wide responsibility, the planning carried out by EA’s can affect the business as a whole. When dealing with change of this nature, what is implemented cannot be started and left to integrate on its own. This sort of radical change needs to be guided and supervised.

This is why if a business is going to take on EA, they need to think about the EAs wider role in the organization. Who should they report to, who should report to them etc.

Many people make the case that EAs should report directly to the CIO, and in fact, hold an advisory role to the CIO as well. Gartner analyst, Brian Burke echoes this sentiment, stating: “We’ve witnessed a change in mind-set, execution and delivery of EA. The value of EA is not in simply ‘doing EA’, but rather in how it can help evolve the business and enable senior executives to respond to business threats and opportunities.”

Therefore, just implementing the scheme isn’t enough. It needs aftercare. This is why EAs should work closely with CIOs, and the benefits of this come two-fold. On one side, the CIO gains a valuable asset in having an adviser with perhaps the most broad, top down view of the organization and its structure, in the business. On the other, the Enterprise Architect has a role more closely aligned with the top table, and can exercise more pull in decision making.

This relationship, and the extra attention to EA it provides could be the difference between success in EA, and an amassment of half started projects and eventual lapse in investment.

Enterprise Architecture & Data Modeling White Paper