Author: Mariann McDonagh

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Data Intelligence erwin Expert Blog

Four Steps to Building a Data-Driven Culture

data-driven culture

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

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

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

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

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

Avoiding Hurdles

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

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

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

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

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

Being a Change Agent

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

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

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

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

Four Steps to Building a Data-Driven Culture

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

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

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

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

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

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

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

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

Request a demo of the erwin Data Intelligence Suite.

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

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erwin Expert Blog

Introducing erwin Insights 2020: Call for Proposals & Engagement

We know these have been unprecedented and challenging times.

While tradeshows and conferences may never be the same, social distancing doesn’t mean we stop learning. In fact, opportunities for personal and professional growth are more important than ever.

I’m pleased to announce that erwin has decided to host an online conference for our customers, partners, prospects and other friends. erwin Insights 2020 will be held on October 13-14, 2020, so save the date!

This free, two-day, entirely virtual event will include live and prerecorded sessions exploring the inherent connections between business, technology and data infrastructures. With synergy between these domains and supporting technologies, organizations have faster speed to insights and the subsequent outcomes that enable them to learn, transform and advance within their industries.

We’re asking for your help in shaping the specific sessions …

  • What industry topics, challenges or best practices would you like us to focus on?
  • Next, are you willing to present a case study? If so, please provide a brief description about the challenges your organization faced, which erwin product you used to tackle it, and the results you’ve seen.
  • Are you interested in leading or being part of a panel discussion? We encourage you to share use cases (e.g., data governance, digital transformation, regulatory compliance, etc.), software tips and tricks, best practices on strategy and implementation, and advice for new users.
  • What other ideas or feedback do you have to help us produce an informative event?

There’s nothing more valuable than hearing from your peers and other erwin customers when it comes to real-world challenges and how other organizations are tackling them.

We need you to share your expertise and insights. See what we did there?

Click here to submit your ideas.

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erwin Expert Blog

Data Equals Truth, and Truth Matters

In these times of great uncertainty and massive disruption, is your enterprise data helping you drive better business outcomes?

The COVID-19 pandemic has forced organizations to tactically adjust their business models, work practices and revenue projections for the short term. But the real challenges will be accelerating recovery and crisis-proofing your business to mitigate the impact of “the next big thing.”

data truth, truth matters

Assure an Unshakable Data Supply Chain to Drive Better Business Outcomes in Turbulent Times

Consider these high-priority scenarios in which the demand for a sound data infrastructure to drive trusted insights is clear and compelling:

  • Organizations contributing to managing the pandemic: (healthcare, government, pharma, etc.)
  • Organizations dealing with major business disruptions in the near and mid-term: (hospitality, retail, transportation)
  • Organizations looking to the post-pandemic future for risk-adverse business models, new opportunities, and/or new approaches to changing markets: (virtually every organization that needs to survive and then thrive)

A data-driven approach has never been more valuable to addressing the complex yet foundational questions enterprises must answer.

However, as we have seen with data surrounding the COVID situation itself, incorrect, incomplete or misunderstood data turn these “what-if” exercises into “WTF” solutions. Organizations that have their data management, data governance and data intelligence houses in order are much better positioned to respond to these challenges and thrive in whatever their new normal turns out to be.

Optimizing data management across the enterprise delivers both tactical and strategic benefits that can mitigate short-term impacts and enable the future-proofing required to ensure stability and success.  Strong data management practices can have:

  • Financial impact (revenue, cash flow, cost structures, etc.)
  • Business capability impact (remote working, lost productivity, restricted access to business-critical infrastructure, supply chain)
  • Market impact (changing customers, market shifts, emerging opportunities)

Turning Data Into a Source of Truth & Regeneration

How can every CEO address the enterprise data dilemma by transforming data into a source of truth and regeneration for post-COVID operations?

  • Accelerate time to value across the data lifecycle (cut time and costs)
    • Decrease data discovery and preparation times
    • Lower the overhead on data related processes and maintenance
    • Reduce latency in the data supply chain
  • Ensure continuity in data capabilities (reduce losses)
    • Automate data management, data intelligence and data governance practices
    • Create always-available and always-transparent data pipelines
    • Reduce necessity for in-person collaboration
  • Ensure company-wide data compliance (reduce risks)
    • Deliver detailed and reliable impact analysis on demand
    • Establish agile and transparent business data governance (policy, classification, rules, usage)
    • Build visibility and traceability into data assets and supporting processes
  • Demand trusted insights based on data truths (Drive innovation and assure veracity)
    • Ensure accurate business context and classification of data
    • Deliver detailed and accurate data lineage on demand
    • Provide visibility into data quality and proven “golden sources”
  • Foster data-driven collaboration (assure agility, visibility and integration of initiatives)
    • Enable navigable data intelligence visualizations and governed feedback loops
    • Govern self-service discovery with rigorous workflows and properly curated data assets
    • Provide visibility across the entire data life cycle (from creation through consumption)

Listen to erwin’s CEO, Adam Famularo, discuss how organizations can use data as a source of truth to navigate current circumstances and determine what’s next on Nasdaq’s Trade Talks.

Data equals truth. #TruthMatters

erwin Rapid Response Resource Center (ERRRC)

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Data Intelligence erwin Expert Blog

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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erwin Expert Blog

Using Enterprise Architecture, Data Modeling & Data Governance for Rapid Crisis Response

Because of the coronavirus pandemic, organizations across the globe are changing how they operate.

Teams need to urgently respond to everything from massive changes in workforce access and management to what-if planning for a variety of grim scenarios, in addition to building and documenting new applications and providing fast, accurate access to data for smart decision-making.

We want to help, so we built the erwin Rapid Response Resource Center. It provides free access to videos, webinars, courseware, simulations, frameworks and expert strategic advice leveraging the erwin EDGE platform for rapid response transformation during the COVID-19 crisis.

How can the erwin EDGE platform help? Here are a few examples specific to enterprise architecture and business process modeling, data modeling and data governance.

Enterprise Architecture & Business Process Modeling

In the face of rapid change, your organization needs to move fast to support the business in a way that provides comprehensive documentation of systems, applications, people and processes. Even though we face a new reality that requires flexibility, the business still has to run with order, documentation and traceability for compliance purposes.

erwin Evolve is purpose-built for these situations and can be used for strategic planning, what-if scenarios, as-is/to-be modeling and its associated impacts and more.

Agility and remote working requires a supporting infrastructure and full documentation. No matter what your role in the company, you need access to the processes you support and the details you most need to get your job done.

erwin Evolve is an enterprise architecture tool that provides a central repository of key processes, the systems that support them, and the business continuity plans for every working environment. This gives all your employees the access and knowledge to operate in a clear and defined way.

Data Modeling

Companies everywhere are building innovative business applications to support their customers, partners and employees in this time of need. But even with the “need for speed” to market, new applications must be modeled and documented for compliance and transparency. Building in the cloud? No problem.

erwin Data Modeler can help you find, visualize, design, deploy and standardize high-quality enterprise data assets. And it’s intuitive, so you can get new modelers up and running quickly as you scale to address this new business reality.

Data Governance

In times of crisis, knowledge is power and nothing fuels decision-making better than your enterprise data. Your data scientists need access to quality data harvested from every data source in your organization to deliver insights and actionable intelligence.

erwin Data Catalog and erwin Data Literacy work in tandem as the erwin Data Intelligence Suite to support data governance and any other data-driven initiative.

Automated metadata harvesting, data cataloging, data mapping and data lineage combined with integrated business glossary management and self-service data discovery gives data scientists and all stakeholders data asset visibility and context so they have the relevant information they need to do their jobs effectively.

Rapid Crisis Response

We stand ready to help with the tools and intelligence you need to navigate these unusual circumstances.

Click here to request access to the erwin Rapid Response Resource Center (ERRRC).

Questions for our experts? You can email them here.

We’ll continue to add content to the ERRRC over the coming days and weeks. How can erwin best help you through these challenging times? Email me directly and let me know.

erwin Rapid Response Resource Center (ERRRC)

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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.

enterprise architecture business process

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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

How Metadata Makes Data Meaningful

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

So most early-stage data governance managers kick off a series of projects to profile data, make inferences about data element structure and format, and store the presumptive metadata in some metadata repository. But are these rampant and often uncontrolled projects to collect metadata properly motivated?

There is rarely a clear directive about how metadata is used. Therefore prior to launching metadata collection tasks, it is important to specifically direct how the knowledge embedded within the corporate metadata should be used.

Managing metadata should not be a sub-goal of data governance. Today, metadata is the heart of enterprise data management and governance/ intelligence efforts and should have a clear strategy – rather than just something you do.

metadata data governance

What Is Metadata?

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. Metadata is valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. It answers these 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?

The Role of Metadata in Data Governance

Organizations don’t know what they don’t know, and this problem is only getting worse. As data continues to proliferate, so does the need for data and analytics initiatives to make sense of it all. Here are some benefits of metadata management for data governance use cases:

  • Better Data Quality: 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.
  • Quicker Project Delivery: Accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc., by up to 70 percent.
  • Faster Speed to Insights: Reverse the current 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: Being able to rely on automated and repeatable metadata management processes results in greater productivity. Some erwin customers report productivity gains of 85+% for coding, 70+% for metadata discovery, up to 50% for data design, up to 70% for data conversion, and up to 80% for data mapping.
  • Regulatory Compliance: Regulations such as GDPR, HIPAA, PII, BCBS and CCPA have data privacy and security mandates, so sensitive data needs to be tagged, its lineage documented, and its flows depicted for traceability.
  • Digital Transformation: Knowing what data exists and its value potential promotes digital transformation by improving digital experiences, enhancing digital operations, driving digital innovation and building digital ecosystems.
  • Enterprise Collaboration: With the business 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 organizational objectives.

Giving Metadata Meaning

So how do you give metadata meaning? While this sounds like a deep philosophical question, the reality is the right tools can make all the difference.

erwin Data Intelligence (erwin DI) combines data management and data governance processes in an automated flow.

It’s unique in its ability to automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable within the context of role-based views.

erwin DI sits on a common metamodel that is open, extensible and comes with a full set of APIs. A comprehensive list of erwin-owned standard data connectors are included for automated harvesting, refreshing and version-controlled metadata management. Optional erwin Smart Data Connectors reverse-engineer ETL code of all types and connect bi-directionally with reporting and other ecosystem tools. These connectors offer the fastest and most accurate path to data lineage, impact analysis and other detailed graphical relationships.

Additionally, erwin DI is part of the larger erwin EDGE platform that integrates data modeling, enterprise architecture, business process modeling, data cataloging and data literacy. We know our customers need an active metadata-driven approach to:

  • Understand their business, technology and data architectures and the relationships between them
  • Create an automate a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage
  • Activate their metadata to drive agile and well-governed data preparation with integrated business glossaries and data dictionaries that provide business context for stakeholder data literacy

erwin was named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”

Click here to get a free copy of the report.

Click here to request a demo of erwin DI.

Gartner Magic Quadrant Metadata Management

 

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erwin Expert Blog

Metadata Management, Data Governance and Automation

Can the 80/20 Rule Be Reversed?

erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect.

This research showed that the majority of responding organizations weren’t actually prepared for GDPR, nor did they have the understanding, executive support and budget for data governance – although they recognized the importance of it.

Of course, data governance has evolved with astonishing speed, both in response to data privacy and security regulations and because organizations see the potential for using it to accomplish other organizational objectives.

But many of the world’s top brands still seem to be challenged in implementing and sustaining effective data governance programs (hello, Facebook).

We wonder why.

Too Much Time, Too Few Insights

According to IDC’s “Data Intelligence in Context” Technology Spotlight sponsored by erwin, “professionals who work with data spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analytics.”

Specifically, 80 percent of data professionals’ time is spent on data discovery, preparation and protection, and only 20 percent on analysis leading to insights.

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.

Often these enterprises are heavily regulated, so they need a well-defined data integration model that will help avoid data discrepancies and remove barriers to enterprise business intelligence and other meaningful use.

IT teams need the ability to smoothly generate hundreds of mappings and ETL jobs. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and data lineage.

But most organizations, especially those competing in the digital economy, don’t have enough time or money for data management using manual processes. Outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too.

The Role of Data Automation

Data governance maturity includes the ability to rely on automated and repeatable processes.

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with automatically generated and meaningful documentation of the mappings, is a powerful way to support governance while providing real insight into data movement — for data lineage and impact analysis — without interrupting system developers’ normal work methods.

GDPR compliance, for instance, requires a business to discover source-to-target mappings with all accompanying transactions, such as what business rules in the repository are applied to it, to comply with audits.

When data movement has been tracked and version-controlled, it’s possible to conduct data archeology — that is, reverse-engineering code from existing XML within the ETL layer — to uncover what has happened in the past and incorporating it into a mapping manager for fast and accurate recovery.

With automation, data professionals can meet the above needs at a fraction of the cost of the traditional, manual way. To summarize, just some of the benefits of data 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

One global pharmaceutical giant reduced costs by 70 percent and generated 95 percent of production code with “zero touch.” With automation, the company improved the time to business value and significantly reduced the costly re-work associated with error-prone manual processes.

Gartner Magic Quadrant Metadata Management

Help Us Help You by Taking a Brief Survey

With 2020 just around the corner and another data regulation about to take effect, the California Consumer Privacy Act (CCPA), we’re working with Dataversity on another research project.

And this time, you guessed it – we’re focusing on data automation and how it could impact metadata management and data governance.

We would appreciate your input and will release the findings in January 2020.

Click here to take the brief survey