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erwin Expert Blog Data Governance

The What & Why of Data Governance

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value.

It is the foundation for regulatory compliance and de-risking operations for competitive differentiation and growth.

However, while digital transformation and other data-driven initiatives are desired outcomes, few organizations know what data they have or where it is, and they struggle to integrate known data in various formats and numerous systems – especially if they don’t have a way to automate those processes.

But when IT-driven data management and business-oriented data governance work together in terms of both personnel, processes and technology, decisions can be made and their impacts determined based on a full inventory of reliable information.

Recently, erwin held the first in a six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, it examined “The What & Why of Data Governance.”

The What: Data Governance Defined

Data governance has no standard definition. However, Dataversity defines it as “the practices and processes which help to ensure the formal management of data assets within an organization.”

At erwin by Quest, we further break down this definition by viewing data governance as a strategic, continuous commitment to ensuring organizations are able to discover and track data, accurately place it within the appropriate business context(s), and maximize its security, quality and value.

Mr. Pörschmann asked webinar attendees to stop trying to explain what data governance is to executives and clients. Instead, he suggests they put data governance in real-world scenarios to answer these questions: “What is the problem you believe data governance is the answer to?” Or “How would you recognize having effective data governance in place?”

In essence, Mr. Pörschmann laid out the “enterprise data dilemma,” which stems from three important but difficult questions for an enterprise to answer: What data do we have? Where is it? And how do we get value from it?

Asking how you recognize having effective data governance in place is quite helpful in executive discussions, according to Mr. Pörschmann. And when you talk about that question at a high level, he says, you get a very “simple answer,”– which is ‘the only thing we want to have is the right data with the right quality to the right person at the right time at the right cost.’

The Why: Data Governance Drivers

Why should companies care about data governance?

erwin’s 2020 State of Data Governance and Automation report found that better decision-making is the primary driver for data governance (62 percent), with analytics secondary (51 percent), and regulatory compliance coming in third (48 percent).

In the webinar, Mr. Pörschmann called out that the drivers of data governance are the same as those for digital transformation initiatives. “This is not surprising at all,” he said. “Because data is one of the success elements of a digital agenda or digital transformation agenda. So without having data governance and data management in place, no full digital transformation will be possible.”

Drivers of data governance

Data Privacy Regulations

While compliance is not the No. 1 driver for data governance, it’s still a major factor – especially since the rollout of the European Union’s General Data Protection Regulation (GDPR) in 2018.

According to Mr. Pörschmann, many decision-makers believe that if they get GDPR right, they’ll be fine and can move onto other projects. But he cautions “this [notion] is something which is not really likely to happen.”

For the EU, he warned, organizations need to prepare for the Digital Single Market, agreed on last year by the European Parliament and commission. With it comes clear definitions or rules on data access and exchange, especially across digital platforms, as well as clear regulations and also instruments to execute on data ownership. He noted, “Companies will be forced to share some specific data which is relevant for public security, i.e., reduction of carbon dioxide. So companies will be forced to classify their data and to find mechanisms to share it with such platforms.”

GDPR is also proving to be the de facto model for data privacy across the United States. The new Virginia Consumer Data Privacy Act, which was modeled on the California Consumer Privacy Act (CCPA), and the California Privacy Rights Act (CPRA), all share many of the same requirements as GDPR.

Like CCPA, the Virginia bill would give consumers the right to access their data, correct inaccuracies, and request the deletion of information. Virginia residents also would be able to opt out of data collection.

Nevada, Vermont, Maine, New York, Washington, Oklahoma and Utah also are leading the way with some type of consumer privacy regulation. Several other bills are on the legislative docket in Alabama, Arizona, Florida, Connecticut and Kentucky, all of which follow a similar format to the CCPA.

Stop Wasting Time

In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity.

Respondents to McKinsey’s 2019 Global Data Transformation Survey reported that an average of 30 percent of their total enterprise time was spent on non-value-added tasks because of poor data quality and availability.

Wasted time is also an unfortunate reality for many data stewards, who spend 80 percent of their time finding, cleaning and reorganizing huge amounts of data, and only 20 percent of their time on actual data analysis.

According to erwin’s 2020 report, about 70 percent of respondents – a combination of roles from data architects to executive managers – said they spent an average of 10 or more hours per week on data-related activities.

The Benefits of erwin Data Intelligence

erwin Data Intelligence by Quest supports enterprise data governance, digital transformation and any effort that relies on data for favorable outcomes.

The software suite 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 Intelligence automatically harvests, transforms and feeds metadata from a wide array of data sources, operational processes, business applications and data models into a central catalog. Then it is accessible and understandable via role-based, contextual views so stakeholders can make strategic decisions based on accurate insights.

You can request a demo of erwin Data Intelligence here.

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erwin Expert Blog Enterprise Architecture

6 Steps to Building a Great Enterprise Architecture Practice

Enterprise architecture provides business and IT alignment by mapping applications, technologies and data to the value streams and business functions they support. It defines business capabilities and interdependencies as they relate to enterprise strategy, bridging the gap between ideation and implementation.

An effective enterprise architecture framework provides a blueprint for business and operating models, identifies risks and opportunities, and enables the creation of technology roadmaps. Simply put, it enables IT and business transformation by helping technology and business innovation leaders focus on achieving successful, value-driven outcomes.

As an enterprise moves and shifts, 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.

 

How Enterprise Architecture Guides QAD

Scott Lawson, Director of IT Architecture for QAD, which provides ERP and other adaptive, cloud-based enterprise software and services for global manufacturing companies, recently shared how he and his company use enterprise architecture for “X-ray vision into the enterprise.”

“We use the architecture of the moment, the stuff that we have in our website to understand what the enterprise is today. It is what it is today, and then we move and use that information to figure out what it’s going to be tomorrow. But we don’t have this compare and contrast because it’s a reference,” he said.

QAD uses the Zachman Framework, which is considered an “ontology” or “schema” to help organize enterprise architecture artifacts, such as documents, specifications and models, which has helped them build a strong practice.

Based on QAD’s success, Lawson explains the six steps that any organization can take to solidify its enterprise architecture:

1. Define your goals. (WHO) While Zachman poses this as the final question, QAD opted to address it first. The reason for the “why” was not only to have a vision into the enterprise, but to change it, to do something about it, to make it better and more efficient. The goal for enterprise architecture for QAD was to add visibility. They cataloged all their systems and what departments used them, and how they communicated with one another, and built a large physical map with all of the information.

2. Define the objects you will collect. (WHAT) Lawson says, “the zero step there is to determine what things you’re going to make a list of. You can’t make a list of everything.”

3. Define your team and the methods to build the pieces. (HOW) There are fundamental questions to ask: How are you going to create it? Are you going to do it manually? Are you going to buy a tool that will collect all the information? Are you going to hire consultants? What are the methods you’re going to use, and how are you going to build those pieces together? Lawson advises that enterprise architecture needs to be a consistent practice. His team does some architecture every day.

4. Define your team and stakeholders. (WHO) Who is going to be the recipient of your architecture, and who is going to be the creator of your architecture? When building a great practice, involve other departments, suggests Lawson. While his department is IT, they reach out to a lot of other departments around the company and ask them about their processes and document those processes for them.

5. Define the tools, artifacts and deliverables. (WHERE) According to Lawson, you have to define where this information is going to exist, what tools you are going to use, and what artifacts and deliverables you are going to produce. He pointed out that an artifact is different than a deliverable. It’s a single unit of things (e.g., one artifact might be a list of servers), while deliverables are typically sent out as diagrams and reports, but it’s a good idea to define them upfront.

6. Define time scale of models: As is, to be, both or one off. (WHEN) What time scale do you want? QAD does an “as-is” architecture (e.g., what is happening today). The company keeps it up to date by collecting information from multiple systems in an automated fashion.

Using erwin Evolve

QAD is an erwin Evolve customer. erwin Evolve is a full-featured, configurable set of enterprise architecture and business process modeling and analysis tools. With it, 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.

With erwin Evolve you can:

  • Harmonize enterprise architecture/business process modeling capabilities for greater visibility, control and intelligence in managing any use case.
  • Quickly and easily explore model elements, links and dependencies.
  • Identify and understand the impact of changes. Increase employee education and awareness, helping maintain institutional knowledge.
  • Democratize content to facilitate broader enterprise collaboration for better decision-making.
  • Achieve faster time to actionable insights and value with integrated views across initiatives.
  • Record end-to-end processes and assign responsibilities and owners to them.
  • Improve performance and profitability with harmonized, optimized and visible processes.

To replay QAD’s session from the erwin Insights global conference on enterprise modeling and data governance and intelligence, which covers the six steps above and more about their use of enterprise architecture and erwin Evolve, click here.

[blog-cta header=”Free no-risk trial of erwin Evolve” body=”If you’d like to start turning your enterprise architecture and business process artifacts into insights for better decisions, you can start a no-risk trial of erwin Evolve” button=”Start Free Trial” button_link=”https://s38605.p1254.sites.pressdns.com/erwin-evolve-free-trial/” image=”https://s38605.p1254.sites.pressdns.com/wp-content/uploads/2020/02/evolve-pic.jpg” ]

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erwin Expert Blog Metadata Management

7 Benefits of Metadata Management

Metadata management is key to wringing all the value possible from data assets.

However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives.

What Is Metadata?

Analyst firm Gartner defines metadata as “information that describes various facets of an information asset to improve its usability throughout its life cycle. It is metadata that turns information into an asset.”

Quite simply, metadata is data about data. It’s generated 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.

It’s valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. Metadata management is the administration of data that describes other data, with an emphasis on associations and lineage. It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization.

Metadata Answers Key Questions

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Metadata answers a lot of important questions:

  • What data do we have?
  • Where did it come from?
  • Where is it now?
  • How has it changed since it was originally created or captured?
  • Who is authorized to use it and how?
  • Is it sensitive or are there any risks associated with it?

Metadata also helps your organization to:

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

Metadata management

The Benefits of Metadata Management

1. Better data quality. With automation, data quality is systemically assured with the data pipeline seamlessly governed and operationalized to the benefit of all stakeholders. Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair. It’s easier to map, move and test data for regular maintenance of existing structures, movement from legacy systems to new systems during a merger or acquisition or a modernization effort.

2. Quicker project delivery. Automated enterprise metadata management provides greater accuracy and up to 70 percent acceleration in project delivery for data movement and/or deployment projects. It harvests metadata from various data sources and maps any data element from source to target and harmonize data integration across platforms. With this accurate picture of your metadata landscape, you can accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc.

3. Faster speed to insights. High-paid knowledge workers like data scientists spend up to 80 percent of their time finding and understanding source data and resolving errors or inconsistencies, rather than analyzing it for real value. That equation can be reversed with stronger data operations and analytics leading to insights more quickly, with access/connectivity to underlying metadata and its lineage. Technical resources are free to concentrate on the highest-value projects, while business analysts, data architects, ETL developers, testers and project managers can collaborate more easily for faster decision-making.

4. Greater productivity & reduced costs. Being able to rely on automated and repeatable metadata management processes results in greater productivity. For example, one erwin DI customer has experienced a steep improvement in productivity – more than 85 percent – because manually intensive and complex coding efforts have been automated and 70+ percent because of seamless access to and visibility of all metadata, including end-to-end lineage. Significant data design and conversion savings, up to 50 percent and 70 percent respectively, also are possible with data mapping costs going down as much as 80 percent.

5. Regulatory compliance. Regulations such as the General Data Protection Regulation (GDPR), Health Insurance and Portability Accountability Act (HIPAA), Basel Committee on Banking Supervision (BCBS) and The California Consumer Privacy Act (CCPA) particularly affect sectors such as finance, retail, healthcare and pharmaceutical/life sciences. When key data isn’t discovered, harvested, cataloged, defined and standardized as part of integration processes, audits may be flawed. Sensitive data is automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found and its use across workflows easily traced.

6. Digital transformation. Knowing what data exists and its value potential promotes digital transformation by 1) improving digital experiences because you understand how the organization interacts with and supports customers, 2) enhancing digital operations because data preparation and analysis projects happen faster, 3) driving digital innovation because data can be used to deliver new products and services, and 4) building digital ecosystems because organizations need to establish platforms and partnerships to scale and grow.

7. An enterprise data governance experience. Stakeholders include both IT and business users in collaborative relationships, so that makes data governance everyone’s business. Modern, strategic data governance must be an ongoing initiative, and it requires everyone from executives on down to rethink their data duties and assume new levels of cooperation and accountability. With business data stakeholders driving alignment between data governance and strategic enterprise goals and IT handling the technical mechanics of data management, the door opens to finding, trusting and using data to effectively meet any organizational objective.

An Automated Solution

When approached manually, metadata management is expensive, time-consuming, error-prone and can’t keep pace with a dynamic enterprise data management infrastructure.

And while integrating and automating data management and data governance is still a new concept for many organizations, its advantages are clear.

erwin’s metadata management offering, the erwin Data Intelligence Suite (erwin DI), includes data catalogdata 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. Its automated, metadata-driven framework gives organizations 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.

erwin has been named a leader in the Gartner 2020 “Magic Quadrant for Metadata Management Solutions” for two consecutive years. Click here to download the full Gartner 2020 “Magic Quadrant for Metadata Management Solutions” report.

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erwin Expert Blog Data Governance

Are Data Governance Bottlenecks Holding You Back?

Better decision-making has now topped compliance as the primary driver of data governance. However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights.

While acknowledging that data governance is about more than risk management and regulatory compliance may indicate that companies are more confident in their data, the data governance practice is nonetheless growing in complexity because of more:

  • Data to handle, much of it unstructured
  • Sources, like IoT
  • Points of integration
  • Regulations

Without an accurate, high-quality, real-time enterprise data pipeline, it will be difficult to uncover the necessary intelligence to make optimal business decisions.

So what’s holding organizations back from fully using their data to make better, smarter business decisions?

Data Governance Bottlenecks

erwin’s 2020 State of Data Governance and Automation report, based on a survey of business and technology professionals at organizations of various sizes and across numerous industries, examined the role of automation in  data governance and intelligence  efforts.  It uncovered a number of obstacles that organizations have to overcome to improve their data operations.

The No.1 bottleneck, according to 62 percent of respondents, was documenting complete data lineage. Understanding the quality of source data is the next most serious bottleneck (58 percent); followed by finding, identifying, and harvesting data (55 percent); and curating assets with business context (52 percent).

The report revealed that all but two of the possible bottlenecks were marked by more than 50 percent of respondents. Clearly, there’s a massive need for a data governance framework to keep these obstacles from stymying enterprise innovation.

As we zeroed in on the bottlenecks of day-to-day operations, 25 percent of respondents said length of project/delivery time was the most significant challenge, followed by data quality/accuracy is next at 24 percent, time to value at 16 percent, and reliance on developer and other technical resources at 13 percent.

Are Data Governance Bottlenecks Holding You Back?

Overcoming Data Governance Bottlenecks

The 80/20 rule describes the unfortunate reality for many data stewards: they spend 80 percent of their time finding, cleaning and reorganizing huge amounts of data and only 20 percent on actual data analysis.

In fact, we found that close to 70 percent of our survey respondents spent an average of 10 or more hours per week on data-related activities, most of it searching for and preparing data.

What can you do to reverse the 80/20 rule and subsequently overcome data governance bottlenecks?

1. Don’t ignore the complexity of data lineage: It’s a risky endeavor to support data lineage using a manual approach, and businesses that attempt it that way will find 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. Adopting automated end-to-end lineage makes it possible to view data movement from the source to reporting structures, providing a comprehensive and detailed view of data in motion.

2. Automate code generation: Alleviate the need for developers to hand code connections from data sources to target schema. 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. It also makes it easier for business analysts, data architects, ETL developers, testers and project managers to collaborate for faster decision-making.

3. Use an integrated impact analysis solution: By automating data due diligence for IT you can deliver operational intelligence to the business. Business users benefit from automating impact analysis to better examine value and prioritize individual data sets. Impact analysis has equal importance to IT for automatically tracking changes and understanding how data from one system feeds other systems and reports. This is an aspect of data lineage, created from technical metadata, ensuring nothing “breaks” along the change train.

4. Put data quality first: Users must have confidence in the data they use for analytics. Automating and matching business terms with data assets and documenting lineage down to the column level are critical to good decision-making. If this approach hasn’t been the case to date, enterprises should take a few steps back to review data quality measures before jumping into automating data analytics.

5. Catalog data using a solution with a broad set of metadata connectors: All data sources will be leveraged, including big data, ETL platforms, BI reports, modeling tools, mainframe, and relational data as well as data from many other types of systems. Don’t settle for a data catalog from an emerging vendor that only supports a narrow swath of newer technologies, and don’t rely on a catalog from a legacy provider that may supply only connectors for standard, more mature data sources.

6. Stress data literacy: You want to ensure that data assets are used strategically. Automation expedites the benefits of data cataloging. Curated internal and external datasets for a range of content authors doubles business benefits and ensures effective management and monetization of data assets in the long-term if linked to broader data governance, data quality and metadata management initiatives. There’s a clear connection to data literacy here because of its foundation in business glossaries and socializing data so all stakeholders can view and understand it within the context of their roles.

7. Make automation the norm across all data governance processes: Too many companies still live in a world where data governance is a high-level mandate, not practically implemented. To fully realize the advantages of data governance and the power of data intelligence, data operations must be automated across the board. Without automated data management, the governance housekeeping load on the business will be so great that data quality will inevitably suffer. Being able to account for all enterprise data and resolve disparity in data sources and silos using manual approaches is wishful thinking.

8. 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. Solve for these first, but build buy-in by creating a layered, comprehensive strategy that ultimately will address most issues. From there, it’s on to matching your needs to an automated data governance solution that squares with business and IT – both for immediate requirements and future plans.

Register now for the first of a new, six-part webinar series on the practice of data governance and how to proactively deal with the complexities. “The What & Why of Data Governance” webinar on Tuesday, Feb. 23rd at 3 pm GMT/10 am ET.

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erwin Expert Blog Data Intelligence

From Chaos to Control with Data Intelligence

As the amount of data grows exponentially, organizations turn to data intelligence to reach deeper conclusions about driving revenue, achieving regulatory compliance and accomplishing other strategic objectives.

It’s no secret that data has grown in volume, variety and velocity, with 2.5 quintillion bytes generated every day and 90 percent of the world’s data volume created just in the last two years. This data explosion has overwhelmed most organizations, making it nearly impossible for them to manage much less put to smart, strategic use. How do you identify the time-sensitive, relevant insights that could mean the difference between the life and death of your business?

Data Intelligence

Time sensitivity in data management and analytics is a massive issue. Data needs to fuel rapid decisions that make your organization more effective, customer-centric and competitive. That was true before COVID 19, and it’s even more important in the face of the radical disruption it’s caused. The answer is radical transformation, made possible by an intelligent, data-driven approach to:

  • New business models
  • New products and services
  • Hyper-competition
  • Market expansion

One Customer’s Journey to Controlling Data Chaos

Ultra Mobile recently shared how it uses erwin Data Intelligence (erwin DI) as part of a modern, ongoing approach to data governance and therefore control versus chaos.

To manage not only risk but also to grow and compete effectively requires the ability to deal with both planned and unplanned change.

With erwin DI as its data governance platform, Ultra Mobile has a one-stop shop to see all of its data – and data changes – in one place thanks to forward and reverse lineage. Now, questions about the health of the business can be answered, including how best to retain customers, and it can explore ways to grow the subscriber base.

Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key. This metadata is ingested into the data catalog, definitions are added within a business glossary, and the searchable repository enables users to understand how data is used and stored.

erwin DI’s mind map also has proved helpful with being able to see associations and entity relationships, especially in terms of impact analysis for evaluating planned changes and their downstream effects.

Watch the full webinar.

Data Intelligence Just Got Smarter

erwin just released a new version of erwin DI. The enhancements include improvements to the user interface (UI), plus new artificial intelligence (AI) and self-service data discovery capabilities.

 

The new erwin DI makes it easier for organizations to tailor the solution to meet the unique needs of their data governance frameworks, identify and socialize the most valuable data assets, and expand metadata scanning and sensitive data tracking.

Using erwin DI, customers are powering comprehensive data governance initiatives, cloud migration and other massive digital transformation projects.

It facilitates both IT- and business-friendly discovery, navigation and understanding of data assets within context and in line with governance protocols.

And it provides organizations with even more flexibility to ensure the software fits their unique frameworks and workflows because one size does not fit all when it comes to data governance.

Backed by a flexible metamodel and deep metadata-driven automation, the updated erwin DI uniquely addresses both IT and business data governance needs to safeguard against risks and harness opportunities.

It combines and then raises the visibility of business and physical data assets in a framework that is flexible but always in sync and therefore sustainable. Then stakeholders from across the enterprise can discover, manage and collaborate on the most relevant and valuable data assets.

The latest erwin DI release builds on prior 2020 updates with:

  • New role-based and governance assignment capabilities, making it easier for an organization to tailor erwin DI to its data governance needs and framework
  • Enhanced UI, workflow and search to speed navigation, asset discovery, contextual understanding and data governance management
  • Expanded AI capabilities to enrich metadata scanning and speed the handling of sensitive data for automated GDPR and CCPA compliance programs
  • Greater visibility into business and data lineage through new vantage points, filters and drilldowns
  • Improved socialization and collaboration features to increase business user engagement and capitalize on organizational data quality knowledge
  • More administrative tools to efficiently onboard new users and roles, manage access rights, and address audit requests

Additionally, erwin DI was recently evaluated by Gartner for the 2020 “Metadata Management Solutions Magic Quadrant,” which named erwin as a “Leader” for the second consecutive year. Click here to download a copy of the Gartner Magic Quadrant Report.

You also can request a free demo of erwin DI here.

Gartner Magic Quadrant

Categories
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 Governance

Automating Data Governance

Automating Data Governance

Automating data governance is key to addressing the exponentially growing volume and variety of data.

erwin CMO Mariann McDonagh recounts erwin’s vision to automate everything from day 1 of erwin Insights 2020.

Data readiness is everything. Whether driving digital experiences, mapping customer journeys, enhancing digital operations, developing digital innovations, finding new ways to interact with customers, or building digital ecosystems or marketplaces – all of this digital transformation is powered by data.

In a COVID and post-COVID world, organizations need to radically change as we look to reimagine business models and reform the way we approach almost everything.

The State of Data Automation

Data readiness depends on automation to create the data pipeline. Earlier this year, erwin conducted a research project in partnership with Dataversity, the 2020 State of Data Governance and Automation.

We asked participants to “talk to us about data value chain bottlenecks.” They told us their number one challenge is documenting complete data lineage (62%), followed by understanding the quality of the data source (58%).

Two other significant bottlenecks are finding, identifying and harvesting data (55%) curating data assets with business content for context and semantics (52%). Every item mentioned here are recurring themes we hear from our customers in terms of what led them to erwin.

We also looked at data preparation, governance and intelligence to see where organizations might be getting stuck and spending lots of time. We found that project length, slow delivery time, is one of the biggest inhibitors. Data quality and accuracy are recurring themes as well.

Reliance on developers and technical resources is another barrier to productivity. Even with data scientists in the front office, the lack of people in the back office to harvest and prepare the data means  time to value is prolonged.

Last but not least, we looked at the amount of time spent on data activities. The great news is that most organizations spend more than 10 hours a week on data-related activities. But the problem is that not enough of that time is spent on analysis because of being stuck in data prep.

IDC talks about this reverse 80/20 rule: 80% of time and effort is spent on data preparation, with only 20% focused on data analysis. This means 80% of your time is left on the cutting-room floor and can’t be used to drive your business forward.

2020 Data Governance and Automation Report

Data Automation Adds Value

Automating data operations adds a lot of value by making a solution more effective and more powerful. Consider a smart home’s thermostat, smoke detectors, lights, doorbell, etc. You have centralized access and control – from anywhere.

At erwin, our goal is to automate the entire data governance journey, whether top down or bottom up. We’re on a mission to automate all the tasks data stewards typically perform so they spend less time building and populating the data governance framework and more time using the framework to realize value and ROI.

Automation also ensures that the data governance framework is always up to date and never stale. Because without current and accurate data, a data governance initiative will fall apart.

Here are some ways erwin adds value by automating the data governance journey:

  • Metadata ingestion into the erwin Data Intelligence Suite (erwin DI) through our standard data connectors. And you can schedule metadata scans to ensure it’s always refreshed and up to date.
  • erwin Smart Data Connectors address data in motion, how it travels and transforms across the enterprise. These custom software solutions document all the traversing and transformations of data and populate the erwin DI’s Metadata Manager with the technical metadata. erwin Smart Data Connectors also document ETL scripts work with the tool of your choice.
  • erwin Lineage Analyzer puts everything together in an easy-to-understand format, making it easy for both business and technical users to visualize how data is traversing the enterprise, how it is getting transformed and the different hops it is taking along the way.
  • erwin DM Connect for DI makes it easy for metadata to be ingested from erwin Data Modeler to erwin DI. erwin DM customers can take advantage of all the rich metadata created and stored in their erwin data models. With just a couple of clicks, some or all data models can be configured and pushed erwin DI’s Metadata Manager.

The automation and integration of erwin DM and erwin DI ensures that your data models are always updated and uploaded, providing a single source of truth for your data governance journey.

This is part one of a two-part series on how erwin is automating data governance. Learn more by watching this session from erwin Insights 2020, which now is available on demand.

erwin Insights 2020

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erwin Expert Blog

Is Climbing the Corporate Ladder Still a Thing?

Thoughts on erwin Insights Day No. 2 Keynote

If you didn’t watch New York Times Best-Selling Author Keith Ferrazzi’s keynote from erwin Insights 2020, what are you waiting for?

I was blown away by Keith’s perspective on “Leading Without Authority” and it got me thinking about my own career, our employees here at erwin, work as we knew it, and work as we’ll know it in a post-COVID world.

Here are my takeaways from Keith’s session from erwin Insights Day No. 2 called “Leadership in Times of Radical Change”:

erwin Insights Leadership in Challenging Times - free access

  1. Don’t Ask for Permission

Keith asked, “How do you become transformational … how do you find the courage to say what’s possible to be that tipping point your organization needs?” The idea of having a vision within the organization and not waiting for the organization to tell you what to do really resonated with me.

So many people see things that need to be done but just sit there waiting – waiting for someone to tell them what they should do. Don’t wait. If needed, ask for forgiveness not permission to do the things that need to be done to create the type of organization you want work for.

  1. Be Your Authentic Self

We all know people who have two personalities – their work face/persona and the moment they leave the office (or log off Zoom) they become their true self. There’s an old-school notion of keeping your personal and professional lives separate. Fortunately, those days have changed.

Today, you need to bring your whole self to work. As employers, we need to give employees the space to be who are they are. And as leaders, it’s ok to be vulnerable – it’s what makes people want to follow you. Our personal lives make us who we are as people and employees, the good and the bad. It’s ok to be vulnerable at work – it builds empathy and trust in the workplace.

  1. Servant Leadership

Being a leader is multifaceted. Servant leadership flips the organizational hierarchy on its head. It puts the employees first – or at the top of the pyramid – and the executives at the bottom.

A book called “The Customer Comes Second” by Hal Rosenbluth focuses on this idea of servant leadership and the principle holds true (if not truer) today – if you put your employees first it will improve morale, performance and ultimately your bottom line.

  1. You’re the CEO of Your Own Career

As Keith points out, leadership has nothing to do with titles. Your career is in your hands, so ask yourself: Am I working with the right people? Is this job good for me right now?

Once you answer those two questions, you can make decisions that best suit you. Additionally, you must be able to cocreate/collaborate. I’ve always paid attention to the people asking the smart questions – and sought out like-minded people to create the type of organization that I envisioned and wanted to be a part of.

Going Forward (Not Back) to Work

What will work life look like in a post-COVID world? Keith says that we shouldn’t think about “going back to work” but rather “going forward to work.”

I love this concept, and I think it’s fair to say that everything has changed and will continue to evolve for quite some time. As more employees work from home or re-evaluate their careers, I believe organizations will become flatter. Therefore, climbing the corporate ladder will become a thing of the past.

Keith’s latest book is “Leading Without Authority: How the New Power of Co-Elevation Can Break Down Silos, Transform Teams, and Reinvent Collaboration.”

You can watch Keith’s presentation as well as all other erwin Insights 2020 sessions on demand.

erwin Insights 2020

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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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erwin Expert Blog Data Intelligence

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