Tag: data modeling

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms, including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management.

The problem usually starts by relying on manual integration methods for data preparation and mapping. It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

To effectively promote business transformation, as well as fulfil regulatory and compliance mandates, there can’t be any mishaps.

It’s obvious that the manual road is very challenging to discover and synthesize data that resides in different formats in thousands of unharvested, undocumented databases, applications, ETL processes and procedural code.

Consider the problematic issue of manually mapping source system fields (typically source files or database tables) to target system fields (such as different tables in target data warehouses or data marts).

These source mappings generally are documented across a slew of unwieldy spreadsheets in their “pre-ETL” stage as the input for ETL development and testing. However, the ETL design process often suffers as it evolves because spreadsheet mapping data isn’t updated or may be incorrectly updated thanks to human error. So questions linger about whether transformed data can be trusted.

Data Quality Obstacles

The sad truth is that 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.

Statistics are similar when looking at major data integration projects, such as data warehousing and master data management with data stewards challenged to identify and document data lineage and sensitive data elements.

So how can businesses produce value from their data when errors are introduced through manual integration processes? How can enterprise stakeholders gain accurate and actionable insights when data can’t be easily and correctly translated into business-friendly terms?

How can organizations master seamless data discovery, movement, transformation and IT and business collaboration to reverse the ratio of preparation to value delivered.

What’s needed to overcome these obstacles is establishing an automated, real-time, high-quality and metadata- driven pipeline useful for everyone, from data scientists to enterprise architects to business analysts to C-level execs.

Doing so will require a hearty data management strategy and technology for automating the timely delivery of quality data that measures up to business demands.

From there, they need a sturdy data governance strategy and technology to automatically link and sync well-managed data with core capabilities for auditing, statutory reporting and compliance requirements as well as to drive business insights.

Creating a High-Quality Data Pipeline

Working hand-in-hand, data management and data governance provide a real-time, accurate picture of the data landscape, including “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

With a metadata-driven, automated, real-time, high-quality data pipeline, all stakeholders can access data that they now are able to understand and trust and which they are authorized to use. At last they can base strategic decisions on what is a full inventory of reliable information.

The integration of data management and governance also supports industry needs to fulfill regulatory and compliance mandates, ensuring that audits are not compromised by the inability to discover key data or by failing to tag sensitive data as part of integration processes.

Data-driven insights, agile innovation, business transformation and regulatory compliance are the fruits of data preparation/mapping and enterprise modeling (business process, enterprise architecture and data modeling) that revolves around a data governance hub.

erwin Mapping Manager (MM) combines data management and data governance processes in an automated flow through the integration lifecycle from data mapping for harmonization and aggregation to generating the physical embodiment of data lineage – that is the creation, movement and transformation of transactional and operational data.

Its hallmark is a consistent approach to data delivery (business glossaries connect physical metadata to specific business terms and definitions) and metadata management (via data mappings).

Automate Data Mapping

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Massive Marriott Data Breach: Data Governance for Data Security

Organizations have been served yet another reminder of the value of data governance for data security.

Hotel and hospitality powerhouse Marriott recently revealed a massive data breach that led to the theft of personal data for an astonishing 500 million customers of its Starwood hotels. This is the second largest data breach in recent history, surpassed only by Yahoo’s breach of 3 billion accounts in 2013 for which it has agreed to pay a $50 million settlement to more than 200 million customers.

Now that Marriott has taken a major hit to its corporate reputation, it has two moves:

  1. Respond: Marriott’s response to its data breach so far has not received glowing reviews. But beyond how it communicates to effected customers, the company must examine how the breach occurred in the first place. This means understanding the context of its data – what assets exist and where, the relationship between them and enterprise systems and processes, and how and by what parties the data is used – to determine the specific vulnerability.
  2. Fix it: Marriott must fix the problem, and quickly, to ensure it doesn’t happen again. This step involves a lot of analysis. A data governance solution would make it a lot less painful by providing visibility into the full data landscape – linkages, processes, people and so on. Then more context-sensitive data security architectures can put in place to for corporate and consumer data privacy.

The GDPR Factor

It’s been six months since the General Data Protection Regulation (GDPR) took effect. While fines for noncompliance have been minimal to date, we anticipate them to dramatically increase in the coming year. Marriott’s bad situation could potentially worsen in this regard, without holistic data governance in place to identify whose and what data was taken.

Data management and data governance, together, play a vital role in compliance, including GDPR. It’s easier to protect sensitive data when you know what it is, where it’s stored and how it needs to be governed.

FREE GUIDE: THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE 

Truly understanding an organization’s data, including the data’s value and quality, requires a harmonized approach embedded in business processes and enterprise architecture. Such an integrated enterprise data governance experience helps organizations understand what data they have, where it is, where it came from, its value, its quality and how it’s used and accessed by people and applications.

Data Governance for Data Security

Data Governance for Data Security: Lessons Learned

Other companies should learn (like pronto) that they need to be prepared. At this point it’s not if, but when, a data breach will rear its ugly head. Preparation is your best bet for avoiding the entire fiasco – from the painstaking process of identifying what happened and why to notifying customers their data and trust in your organization have been compromised.

A well-formed security architecture that is driven by and aligned by data intelligence is your best defense. However, if there is nefarious intent, a hacker will find a way. So being prepared means you can minimize your risk exposure and the damage to your reputation.

Multiple components must be considered to effectively support a data governance, security and privacy trinity. They are:

  1. Data models
  2. Enterprise architecture
  3. Business process models

What’s key to remember is that these components act as links in the data governance chain by making it possible to understand what data serves the organization, its connection to the enterprise architecture, and all the business processes it touches.

THE EXPERT GUIDE TO DATA GOVERNANCE, SECURITY AND PRIVACY

Creating policies for data handling and accountability and driving culture change so people understand how to properly work with data are two important components of a data governance initiative, as is the technology for proactively managing data assets.

Without the ability to harvest metadata schemas and business terms; analyze data attributes and relationships; impose structure on definitions; and view all data in one place according to each user’s role within the enterprise, businesses will be hard pressed to stay in step with governance standards and best practices around security and privacy.

As a consequence, the private information held within organizations will continue to be at risk. Organizations suffering data breaches will be deprived of the benefits they had hoped to realize from the money spent on security technologies and the time invested in developing data privacy classifications. They also may face heavy fines and other financial, not to mention PR, penalties.

Less Pain, More Gain

Most organizations don’t have enough time or money for data management using manual processes. And outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too. Furthermore, manual processes require manual analysis and auditing, which is always more expensive and time consuming.

So the more processes an organization can automate, the less risk of human error, which is actually the primary cause of most data breaches. And automated processes are much easier to analyze and audit because everything is captured, versioned and available for review in a log somewhere. You can read more about automation in our 10 Reasons to Automate Data Mapping and Data Preparation.

And to learn more about how data governance underpins data security and privacy, click here.

Automate Data Mapping

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Data Modeling and Data Mapping: Results from Any Data Anywhere

A unified approach to data modeling and data mapping could be the breakthrough that many data-driven organizations need.

In most of the conversations I have with clients, they express the need for a viable solution to model their data, as well as the ability to capture and document the metadata within their environments.

Data modeling is an integral part of any data management initiative. Organizations use data models to tame “data at rest” for business use, governance and technical management of databases of all types.

However, once an organization understands what data it has and how it’s structured via data models, it needs answers to other critical questions: Where did it come from? Did it change along the journey? Where does it go from here?

Data Mapping: Taming “Data in Motion”

Knowing how data moves throughout technical and business data architectures is key for true visibility, context and control of all data assets.

Managing data in motion has been a difficult, time-consuming task that involves mapping source elements to the data model, defining the required transformations, and/or providing the same for downstream targets.

Historically, it either has been outsourced to ETL/ELT developers who often create a siloed, technical infrastructure opaque to the business, or business-friendly mappings have been kept in an assortment of unwieldy spreadsheets difficult to consolidate and reuse much less capable of accommodating new requirements.

What if you could combine data at rest and data in motion to create an efficient, accurate and real-time data pipeline that also includes lineage? Then you can spend your time finding the data you need and using it to produce meaningful business outcomes.

Good news … you can.

erwin Mapping Manager: Connected Data Platform

Automated Data Mapping

Your data modelers can continue to use erwin Data Modeler (DM) as the foundation of your database management system, documenting, enforcing and improving those standards. But instead of relying on data models to disseminate metadata information, you can scan and integrate any data source and present it to all interested parties – automatically.

erwin Mapping Manager (MM) shifts the management of metadata away from data models to a dedicated, automated platform. It can collect metadata from any source, including JSON documents, erwin data models, databases and ERP systems, out of the box.

This functionality underscores our Any2 data approach by collecting any data from anywhere. And erwin MM can schedule data collection and create versions for comparison to clearly identify any changes.

Metadata definitions can be enhanced using extended data properties, and detailed data lineages can be created based on collected metadata. End users can quickly search for information and see specific data in the context of business processes.

To summarize the key features current data modeling customers seem to be most excited about:

  • Easy import of legacy mappings, plus share and reuse mappings and transformations
  • Metadata catalog to automatically harvest any data from anywhere
  • Comprehensive upstream and downstream data lineage
  • Versioning with comparison features
  • Impact analysis

And all of these features support and can be integrated with erwin Data Governance. The end result is knowing what data you have and where it is so you can fuel a fast, high-quality and complete pipeline of any data from anywhere to accomplish your organizational objectives.

Want to learn more about a unified approach to data modeling and data mapping? Join us for our weekly demo to see erwin MM in action for yourself.

erwin Mapping Manager

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Defining DG: What Can Data Governance Do for You?

Data governance (DG) is becoming more commonplace because of data-driven business, yet defining DG and putting into sound practice is still difficult for many organizations.

Defining DG

The absence of a standard approach to defining DG could be down to its history of missed expectations, false starts and negative perceptions about it being expensive, intrusive, impeding innovation and not delivering any value. Without success stories to point to, the best way of doing and defining DG wasn’t clear.

On the flip side, the absence of a standard approach to defining DG could be the reason for its history of lacklustre implementation efforts, because those responsible for overseeing it had different ideas about what should be done.

Therefore, it’s been difficult to fully fund a data governance initiative that is underpinned by an effective data management capability. And many organizations don’t distinguish between data governance and data management, using the terms interchangeably and so adding to the confusion.

Defining DG: The Data Governance Conundrum

While research indicates most view data governance as “critically important” or they recognize the value of data, the large percentage without a formal data governance strategy in place indicates there are still significant teething problems.

How Important is Data Governance

And that’s the data governance conundrum. It is essential but unwanted and/or painful.

It is a complex chore, so organizations have lacked the motivation to start and effectively sustain it. But faced with the General Data Protection Regulation (GDPR) and other compliance requirements, they have been doing the bare minimum to avoid the fines and reputational damage.

And arguably, herein lies the problem. Organizations look at data governance as something they have to do rather than seeing what it could do for them.

Data governance has its roots in the structure of business terms and technical metadata, but it has tendrils and deep associations with many other components of a data management strategy and should serve as the foundation of that platform.

With data governance at the heart of data management, data can be discovered and made available throughout the organization for both IT and business stakeholders with approved access. This means enterprise architecture, business process, data modeling and data mapping all can draw from a central metadata repository for a single source of data truth, which improves data quality, trust and use to support organizational objectives.

But this “data nirvana” requires a change in approach to data governance. First, recognizing that Data Governance 1.0 was made for a different time when the volume, variety and velocity of the data an organization had to manage was far lower and when data governance’s reach only extended to cataloging data to support search and discovery. 

Data Governance Evolution

Modern data governance needs to meet the needs of data-driven business. We call this adaptation “Evolving DG.” It is the journey to a cost-effective, mature, repeatable process that permeates the whole organization.

The primary components of Evolving DG are:

  • Evaluate
  • Plan
  • Configure
  • Deliver
  • Feedback

The final step in such an evolution is the implementation of the erwin Enterprise Data Governance Experience (EDGE) platform.

The erwin EDGE places data governance at the heart of the larger data management suite. By unifying the data management suite at a fundamental level, an organization’s data is no longer marred by departmental and software silos. It brings together both IT and the business for data-driven insights, regulatory compliance, agile innovation and business transformation.

It allows every critical piece of the data management and data governance lifecycle to draw from a single source of data truth and ensure quality throughout the data pipeline, helping organizations achieve their strategic objectives including:

  • Operational efficiency
  • Revenue growth
  • Compliance, security and privacy
  • Increased customer satisfaction
  • Improved decision-making

To learn how you can evolve your data governance practice and get an EDGE on your competition, click here.

Solving the Enterprise Data Dilemma

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The Data Governance (R)Evolution

Data governance continues to evolve – and quickly.

Historically, Data Governance 1.0 was siloed within IT and mainly concerned with cataloging data to support search and discovery. However, it fell short in adding value because it neglected the meaning of data assets and their relationships within the wider data landscape.

Then the push for digital transformation and Big Data created the need for DG to come out of IT’s shadows – Data Governance 2.0 was ushered in with principles designed for  modern, data-driven business. This approach acknowledged the demand for collaborative data governance, the tearing down of organizational silos, and spreading responsibilities across more roles.

But this past year we all witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Aetna. The list goes on and on. And then, the European Union’s General Data Protection Regulation (GDPR) took effect, with many organizations scrambling to become compliant.

So where are we today?

Simply put, data governance needs to be a ubiquitous part of your company’s culture. Your stakeholders encompass both IT and business users in collaborative relationships, so that makes data governance everyone’s business.

Data Governance is Everyone's Business

Data governance underpins data privacy, security and compliance. Additionally, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to grow revenue, achieve regulatory compliance, or make strategic decisions. They face a data dilemma: not knowing what data they have or where some of it is—plus integrating known data in various formats from numerous systems without a way to automate that process.

To accelerate the transformation of business-critical information into accurate and actionable insights, organizations need an automated, real-time, high-quality data pipeline. Then every stakeholder—data scientist, ETL developer, enterprise architect, business analyst, compliance officer, CDO and CEO—can fuel the desired outcomes based on reliable information.

Connecting Data Governance to Your Organization

  1. Data Mapping & Data Governance

The automated generation of the physical embodiment of data lineage—the creation, movement and transformation of transactional and operational data for harmonization and aggregation—provides the best route for enabling stakeholders to understand their data, trust it as a well-governed asset and use it effectively. Being able to quickly document lineage for a standardized, non-technical environment brings business alignment and agility to the task of building and maintaining analytics platforms.

  1. Data Modeling & Data Governance

Data modeling discovers and harvests data schema, and analyzes, represents and communicates data requirements. It synthesizes and standardizes data sources for clarity and consistency to back up governance requirements to use only controlled data. It benefits from the ability to automatically map integrated and cataloged data to and from models, where they can be stored in a central repository for re-use across the organization.

  1. Business Process Modeling & Data Governance

Business process modeling reveals the workflows, business capabilities and applications that use particular data elements. That requires that these assets be appropriately governed components of an integrated data pipeline that rests on automated data lineage and business glossary creation.

  1. Enterprise Architecture & Data Governance

Data flows and architectural diagrams within enterprise architecture benefit from the ability to automatically assess and document the current data architecture. Automatically providing and continuously maintaining business glossary ontologies and integrated data catalogs inform a key part of the governance process.

The EDGE Revolution

 By bringing together enterprise architecture, business process, data mapping and data modeling, erwin’s approach to data governance enables organizations to get a handle on how they handle their data and realize its maximum value. With the broadest set of metadata connectors and automated code generation, data mapping and cataloging tools, the erwin EDGE Platform simplifies the total data management and data governance lifecycle.

This single, integrated solution makes it possible to gather business intelligence, conduct IT audits, ensure regulatory compliance and accomplish any other organizational objective by fueling an automated, high-quality and real-time data pipeline.

The erwin EDGE creates an “enterprise data governance experience” that facilitates collaboration between both IT and the business to discover, understand and unlock the value of data both at rest and in motion.

With the erwin EDGE, data management and data governance are unified and mutually supportive of business stakeholders and IT to:

  • Discover data: Identify and integrate 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 data: Connect physical metadata to specific business terms and definitions and reusable design standards.
  • Analyze data: 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 and policies and set best practices.
  • Socialize data: Enable stakeholders to see data in one place and in the context of their roles.

If you’ve enjoyed this latest blog series, then you’ll want to request a copy of Solving the Enterprise Data Dilemma, our new e-book that highlights how to answer the three most important data management and data governance questions: What data do we have? Where is it? And how do we get value from it?

Solving the Enterprise Data Dilemma

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Healthy Co-Dependency: Data Management and Data Governance

Data management and data governance are now more important than ever before. The hyper competitive nature of data-driven business means organizations need to get more out of their data than ever before – and fast.

A few data-driven exemplars have led the way, turning data into actionable insights that influence everything from corporate structure to new products and pricing. “Few” being the operative word.

It’s true, data-driven business is big business. Huge actually. But it’s dominated by a handful of organizations that realized early on what a powerful and disruptive force data can be.

The benefits of such data-driven strategies speak for themselves: Netflix has replaced Blockbuster, and Uber continues to shake up the taxi business. Organizations indiscriminate of industry are following suit, fighting to become the next big, disruptive players.

But in many cases, these attempts have failed or are on the verge of doing so.

Now with the General Data Protection Regulation (GDPR) in effect, data that is unaccounted for is a potential data disaster waiting to happen.

So organizations need to understand that getting more out of their data isn’t necessarily about collecting more data. It’s about unlocking the value of the data they already have.

Data Management and Data Governance Co-Dependency

The Enterprise Data Dilemma

However, most organizations don’t know exactly what data they have or even where some of it is. And some of the data they can account for is going to waste because they don’t have the means to process it. This is especially true of unstructured data types, which organizations are collecting more frequently.

Considering that 73 percent of company data goes unused, it’s safe to assume your organization is dealing with some if not all of these issues.

Big picture, this means your enterprise is missing out on thousands, perhaps millions in revenue.

The smaller picture? You’re struggling to establish a single source of data truth, which contributes to a host of problems:

  • Inaccurate analysis and discrepancies in departmental reporting
  • Inability to manage the amount and variety of data your organization collects
  • Duplications and redundancies in processes
  • Issues determining data ownership, lineage and access
  • Achieving and sustaining compliance

To avoid such circumstances and get more value out of data, organizations need to harmonize their approach to data management and data governance, using a platform of established tools that work in tandem while also enabling collaboration across the enterprise.

Data management drives the design, deployment and operation of systems that deliver operational data assets for analytics purposes.

Data governance delivers these data assets within a business context, tracking their physical existence and lineage, and maximizing their security, quality and value.

Although these two disciplines approach data from different perspectives (IT-driven and business-oriented), they depend on each other. And this co-dependency helps an organization make the most of its data.

The P-M-G Hub

Together, data management and data governance form a critical hub for data preparation, modeling and data governance. How?

It starts with a real-time, accurate picture of the data landscape, including “data at rest” in databases, data warehouses and data lakes and “data in motion” as it is integrated with and used by key applications. That landscape also must be controlled to facilitate collaboration and limit risk.

But knowing what data you have and where it lives is complicated, so you need to create and sustain an enterprise-wide view of and easy access to underlying metadata. That’s a tall order with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. So the applications and initiatives that depend on a solid data infrastructure may be compromised, and data analysis based on faulty insights.

However, these issues can be addressed with a strong data management strategy and technology to enable the data quality required by the business, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Being able to pinpoint what data exists and where must be accompanied by an agreed-upon business understanding of what it all means in common terms that are adopted across the enterprise. Having that consistency is the only way to assure that insights generated by analyses are useful and actionable, regardless of business department or user exploring a question. Additionally, policies, processes and tools that define and control access to data by roles and across workflows are critical for security purposes.

These issues can be addressed with a comprehensive data governance strategy and technology to determine master data sets, discover the impact of potential glossary changes across the enterprise, audit and score adherence to rules, discover risks, and appropriately and cost-effectively apply security to data flows, as well as publish data to people/roles in ways that are meaningful to them.

Data Management and Data Governance: Play Together, Stay Together

When data management and data governance work in concert empowered by the right technology, they inform, guide and optimize each other. The result for an organization that takes such a harmonized approach is automated, real-time, high-quality data pipeline.

Then all stakeholders — data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access the data they’re authorized to use and base strategic decisions on what is now a full inventory of reliable information.

The erwin EDGE creates an “enterprise data governance experience” through integrated data mapping, business process modeling, enterprise architecture modeling, data modeling and data governance. No other software platform on the market touches every aspect of the data management and data governance lifecycle to automate and accelerate the speed to actionable business insights.

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Solving the Enterprise Data Dilemma

Due to the adoption of data-driven business, organizations across the board are facing their own enterprise data dilemmas.

This week erwin announced its acquisition of metadata management and data governance provider AnalytiX DS. The combined company touches every piece of the data management and governance lifecycle, enabling enterprises to fuel automated, high-quality data pipelines for faster speed to accurate, actionable insights.

Why Is This a Big Deal?

From digital transformation to AI, and everything in between, organizations are flooded with data. So, companies are investing heavily in initiatives to use all the data at their disposal, but they face some challenges. Chiefly, deriving meaningful insights from their data – and turning them into actions that improve the bottom line.

This enterprise data dilemma stems from three important but difficult questions to answer: What data do we have? Where is it? And how do we get value from it?

Large enterprises use thousands of unharvested, undocumented databases, applications, ETL processes and procedural code that make it difficult to gather business intelligence, conduct IT audits, and ensure regulatory compliance – not to mention accomplish other objectives around customer satisfaction, revenue growth and overall efficiency and decision-making.

The lack of visibility and control around “data at rest” combined with “data in motion”, as well as difficulties with legacy architectures, means these organizations spend more time finding the data they need rather than using it to produce meaningful business outcomes.

To remedy this, enterprises need smarter and faster data management and data governance capabilities, including the ability to efficiently catalog and document their systems, processes and the associated data without errors. In addition, business and IT must collaborate outside their traditional operational silos.

But this coveted state of data nirvana isn’t possible without the right approach and technology platform.

Enterprise Data: Making the Data Management-Data Governance Love Connection

Enterprise Data: Making the Data Management-Data Governance Love Connection

Bringing together data management and data governance delivers greater efficiencies to technical users and better analytics to business users. It’s like two sides of the same coin:

  • Data management drives the design, deployment and operation of systems that deliver operational and analytical data assets.
  • Data governance delivers these data assets within a business context, tracks their physical existence and lineage, and maximizes their security, quality and value.

Although these disciplines approach data from different perspectives and are used to produce different outcomes, they have a lot in common. Both require a real-time, accurate picture of an organization’s data landscape, including data at rest in data warehouses and data lakes and data in motion as it is integrated with and used by key applications.

However, creating and maintaining this metadata landscape is challenging because this data in its various forms and from numerous sources was never designed to work in concert. Data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration, so the applications and initiatives that depend on data infrastructure are often out-of-date and inaccurate, rendering faulty insights and analyses.

Organizations need to know what data they have and where it’s located, where it came from and how it got there, what it means in common business terms [or standardized business terms] and be able to transform it into useful information they can act on – all while controlling its access.

To support the total enterprise data management and governance lifecycle, they need an automated, real-time, high-quality data pipeline. Then every stakeholder – data scientist, ETL developer, enterprise architect, business analyst, compliance officer, CDO and CEO – can fuel the desired outcomes with reliable information on which to base strategic decisions.

Enterprise Data: Creating Your “EDGE”

At the end of the day, all industries are in the data business and all employees are data people. The success of an organization is not measured by how much data it has, but by how well it’s used.

Data governance enables organizations to use their data to fuel compliance, innovation and transformation initiatives with greater agility, efficiency and cost-effectiveness.

Organizations need to understand their data from different perspectives, identify how it flows through and impacts the business, aligns this business view with a technical view of the data management infrastructure, and synchronizes efforts across both disciplines for accuracy, agility and efficiency in building a data capability that impacts the business in a meaningful and sustainable fashion.

The persona-based erwin EDGE creates an “enterprise data governance experience” that facilitates collaboration between both IT and the business to discover, understand and unlock the value of data both at rest and in motion.

By bringing together enterprise architecture, business process, data mapping and data modeling, erwin’s approach to data governance enables organizations to get a handle on how they handle their data. With the broadest set of metadata connectors and automated code generation, data mapping and cataloging tools, the erwin EDGE Platform simplifies the total data management and data governance lifecycle.

This single, integrated solution makes it possible to gather business intelligence, conduct IT audits, ensure regulatory compliance and accomplish any other organizational objective by fueling an automated, high-quality and real-time data pipeline.

With the erwin EDGE, data management and data governance are unified and mutually supportive, with one hand aware and informed by the efforts of the other to:

  • Discover data: Identify and integrate 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 data: Connect physical metadata to specific business terms and definitions and reusable design standards.
  • Analyze data: 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 and policies and set best practices.
  • Socialize data: Enable stakeholders to see data in one place and in the context of their roles.

An integrated solution with data preparation, modeling and governance helps businesses reach data governance maturity – which equals a role-based, collaborative data governance system that serves both IT and business users equally. Such maturity may not happen overnight, but it will ultimately deliver the accurate and actionable insights your organization needs to compete and win.

Your journey to data nirvana begins with a demo of the enhanced erwin Data Governance solution. Register now.

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Big Data Posing Challenges? Data Governance Offers Solutions

Big Data is causing complexity for many organizations, not just because of the volume of data they’re collecting, but because of the variety of data they’re collecting.

Big Data often consists of unstructured data that streams into businesses from social media networks, internet-connected sensors, and more. But the data operations at many organizations were not designed to handle this flood of unstructured data.

Dealing with the volume, velocity and variety of Big Data is causing many organizations to re-think how they store and govern their data. A perfect example is the data warehouse. The people who built and manage the data warehouse at your organization built something that made sense to them at the time. They understood what data was stored where and why, as well how it was used by business units and applications.

The era of Big Data introduced inexpensive data lakes to some organizations’ data operations, but as vast amounts of data pour into these lakes, many IT departments found themselves managing a data swamp instead.

In a perfect world, your organization would treat Big Data like any other type of data. But, alas, the world is not perfect. In reality, practicality and human nature intervene. Many new technologies, when first adopted, are separated from the rest of the infrastructure.

“New technologies are often looked at in a vacuum, and then built in a silo,” says Danny Sandwell, director of product marketing for erwin, Inc.

That leaves many organizations with parallel collections of data: one for so-called “traditional” data and one for the Big Data.

There are a few problems with this outcome. For one, silos in IT have a long history of keeping organizations from understanding what they have, where it is, why they need it, and whether it’s of any value. They also have a tendency to increase costs because they don’t share common IT resources, leading to redundant infrastructure and complexity. Finally, silos usually mean increased risk.

But there’s another reason why parallel operations for Big Data and traditional data don’t make much sense: The users simply don’t care.

At the end of the day, your users want access to the data they need to do their jobs, and whether IT considers it Big Data, little data, or medium-sized data isn’t important. What’s most important is that the data is the right data – meaning it’s accurate, relevant and can be used to support or oppose a decision.

Reputation Management - What's Driving Data Governance

How Data Governance Turns Big Data into Just Plain Data

According to a November 2017 survey by erwin and UBM, 21 percent of respondents cited Big Data as a driver of their data governance initiatives.

In today’s data-driven world, data governance can help your business understand what data it has, how good it is, where it is, and how it’s used. The erwin/UBM survey found that 52 percent of respondents said data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) said they recognize the value of data to their organization but don’t have a formal governance strategy.

A holistic approach to data governance includes thesekey components.

  • An enterprise architecture component is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable. By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes.
  • A business process and analysis component defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible. Enterprises can clearly define, map and analyze workflows and build models to drive process improvements, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.
  • A data modeling component is the best way to design and deploy new databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

When data governance is done right, and it’s woven into the structure and architecture of your business, it helps your organization accept new technologies and the new sources of data they provide as they come along. This makes it easier to see ROI and ROO from your Big Data initiatives by managing Big Data in the same manner your organization treats all of its data – by understanding its metadata, defining its relationships, and defining its quality.

Furthermore, businesses that apply sound data governance will find themselves with a template or roadmap they can use to integrate Big Data throughout their organizations.

If your business isn’t capitalizing on the Big Data it’s collecting, then it’s throwing away dollars spent on data collection, storage and analysis. Just as bad, however, is a situation where all of that data and analysis is leading to the wrong decisions and poor business outcomes because the data isn’t properly governed.

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Data Governance Tackles the Top Three Reasons for Bad Data

In modern, data-driven busienss, it’s integral that organizations understand the reasons for bad data and how best to address them. Data has revolutionized how organizations operate, from customer relationships to strategic decision-making and everything in between. And with more emphasis on automation and artificial intelligence, the need for data/digital trust also has risen. Even minor errors in an organization’s data can cause massive headaches because the inaccuracies don’t involve just one corrupt data unit.

Inaccurate or “bad” data also affects relationships to other units of data, making the business context difficult or impossible to determine. For example, are data units tagged according to their sensitivity [i.e., personally identifiable information subject to the General Data Protection Regulation (GDPR)], and is data ownership and lineage discernable (i.e., who has access, where did it originate)?

Relying on inaccurate data will hamper decisions, decrease productivity, and yield suboptimal results. Given these risks, organizations must increase their data’s integrity. But how?

Integrated Data Governance

Modern, data-driven organizations are essentially data production lines. And like physical production lines, their associated systems and processes must run smoothly to produce the desired results. Sound data governance provides the framework to address data quality at its source, ensuring any data recorded and stored is done so correctly, securely and in line with organizational requirements. But it needs to integrate all the data disciplines.

By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes. A business process and analysis component enables an organization to clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

And data modeling remains the best way to design and deploy new relational databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

Let’s look at some of the main reasons for bad data and how data governance helps confront these issues …

Reasons for Bad Data

Reasons for Bad Data: Data Entry

The concept of “garbage in, garbage out” explains the most common cause of inaccurate data: mistakes made at data entry. While this concept is easy to understand, totally eliminating errors isn’t feasible so organizations need standards and systems to limit the extent of their damage.

With the right data governance approach, organizations can ensure the right people aren’t left out of the cataloging process, so the right context is applied. Plus you can ensure critical fields are not left blank, so data is recorded with as much context as possible.

With the business process integration discussed above, you’ll also have a single metadata repository.

All of this ensures sensitive data doesn’t fall through the cracks.

Reasons for Bad Data: Data Migration

Data migration is another key reason for bad data. Modern organizations often juggle a plethora of data systems that process data from an abundance of disparate sources, creating a melting pot for potential issues as data moves through the pipeline, from tool to tool and system to system.

The solution is to introduce a predetermined standard of accuracy through a centralized metadata repository with data governance at the helm. In essence, metadata describes data about data, ensuring that no matter where data is in relation to the pipeline, it still has the necessary context to be deciphered, analyzed and then used strategically.

The potential fallout of using inaccurate data has become even more severe with the GDPR’s implementation. A simple case of tagging and subsequently storing personally identifiable information incorrectly could lead to a serious breach in compliance and significant fines.

Such fines must be considered along with the costs resulting from any PR fallout.

Reasons for Bad Data: Data Integration

The proliferation of data sources, types, and stores increases the challenge of combining data into meaningful, valuable information. While companies are investing heavily in initiatives to increase the amount of data at their disposal, most information workers are spending more time finding the data they need rather than putting it to work, according to Database Trends and Applications (DBTA). erwin is co-sponsoring a DBTA webinar on this topic on July 17. To register, click here.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver business value, people need information they can trust to act on, so balancing governance is absolutely critical, especially with new regulations.

Organizations often invest heavily in individual software development tools for managing projects, requirements, designs, development, testing, deployment, releases, etc. Tools lacking inter-operability often result in cumbersome manual processes and heavy time investments to synchronize data or processes between these disparate tools.

Data integration combines data from several various sources into a unified view, making it more actionable and valuable to those accessing it.

Getting the Data Governance “EDGE”

The benefits of integrated data governance discussed above won’t be realized if it is isolated within IT with no input from other stakeholders, the day-to-day data users – from sales and customer service to the C-suite. Every data citizen has DG roles and responsibilities to ensure data units have context, meaning they are labeled, cataloged and secured correctly so they can be analyzed and used properly. In other words, the data can be trusted.

Once an organization understands that IT and the business are both responsible for data, it can develop comprehensive, holistic data governance capable of:

  • Reaching every stakeholder in the process
  • Providing a platform for understanding and governing trusted data assets
  • Delivering the greatest benefit from data wherever it lives, while minimizing risk
  • Helping users understand the impact of changes made to a specific data element across the enterprise.

To reduce the risks of and tackle the reasons for bad data and realize larger organizational objectives, organizations must make data governance everyone’s business.

To learn more about the collaborative approach to data governance and how it helps compliance in addition to adding value and reducing costs, get the free e-book here.

Data governance is everyone's business

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The Role of An Effective Data Governance Initiative in Customer Purchase Decisions

A data governance initiative will maximize the security, quality and value of data, all of which build customer trust.

Without data, modern business would cease to function. Data helps guide decisions about products and services, makes it easier to identify customers, and serves as the foundation for everything businesses do today. The problem for many organizations is that data enters from any number of angles and gets stored in different places by different people and different applications.

Getting the most out of your data requires that you know what you have, where you have it, and that you understand its quality and value to the organization. This is where data governance comes into play. You can’t optimize your data if it’s scattered across different silos and lurking in various applications.

For about 150 years, manufacturers relied on their machinery and its ability to run reliably, properly and safely, to keep customers happy and revenue flowing. A data governance initiative has a similar role today, except its aim is to maximize the security, quality and value of data instead of machinery.

Customers are increasingly concerned about the safety and privacy of their data. According to a survey by Research+Data Insights, 85 percent of respondents worry about technology compromising their personal privacy. In a survey of 2,000 U.S. adults in 2016, researchers from Vanson Bourne found that 76 percent of respondents said they would move away from companies with a high record of data breaches.

For years, buying decisions were driven mainly by cost and quality, says Danny Sandwell, director of product marketing at erwin, Inc. But today’s businesses must consider their reputations in terms of both cost/quality and how well they protect their customers’ data when trying to win business.

Once the reputation is tarnished because of a breach or misuse of data, customers will question those relationships.

Unfortunately for consumers, examples of companies failing to properly govern their data aren’t difficult to find. Look no further than Under Armour, which announced this spring that 150 million accounts at its MyFitnessPal diet and exercise tracking app were breached, and Facebook, where the data of millions of users was harvested by third parties hoping to influence the 2016 presidential election in the United States.

Customers Hate Breaches, But They Love Data

While consumers are quick to report concerns about data privacy, customers also yearn for (and increasingly expect) efficient, personalized and relevant experiences when they interact with businesses. These experiences are, of course, built on data.

In this area, customers and businesses are on the same page. Businesses want to collect data that helps them build the omnichannel, 360-degree customer views that make their customers happy.

These experiences allow businesses to connect with their customers and demonstrate how well they understand them and know their preferences, like and dislikes – essentially taking the personalized service of the neighborhood market to the internet.

The only way to manage that effectively at scale is to properly govern your data.

Delivering personalized service is also valuable to businesses because it helps turn customers into brand ambassadors, and it’s a fact that it’s much easier to build on existing customer relationships than to find new customers.

Here’s the upshot: If your organization is doing data governance right, it’s helping create happy, loyal customers, while at the same time avoiding the bad press and financial penalties associated with poor data practices.

Putting A Data Governance Initiative Into Action

The good news is that 76 percent of respondents to a November 2017 survey we conducted with UBM said understanding and governing the data assets in the organization was either important or very important to the executives in their organization. Nearly half (49 percent) of respondents said that customer trust/satisfaction was driving their data governance initiatives.

Importance of a data governance initiative

What stops organizations from creating an effective data governance initiative? At some businesses, it’s a cultural issue. Both the business and IT sides of the organization play important roles in data, with the IT side storing and protecting it, and the business side consuming data and analyzing it.

For years, however, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. Our study found signs this is changing. More than half (57 percent) of the respondents said both and IT and the business/corporate teams were responsible for data in their organization.

Who's responsible for a data governance initiative

Once an organization understands that IT and the business are both responsible for data, it still needs to develop a comprehensive, holistic strategy for data governance that is capable of:

  • Reaching every stakeholder in the process
  • Providing a platform for understanding and governing trusted data assets
  • Delivering the greatest benefit from data wherever it lives, while minimizing risk
  • Helping users understand the impact of changes made to a specific data element across the enterprise.

To accomplish this, a modern data governance initiative needs to be interdisciplinary. It should include not only data governance, which is ongoing because organizations are constantly changing and transforming, but other disciples as well.

Enterprise architecture is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable.

By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes.

A business process and analysis component is also vital to modern data governance. It defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible.

Enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Finally, data modeling remains the best way to design and deploy new relational databases with high-quality data sources and support application development.

Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech. This content originally appeared as a sponsored post on http://www.eweek.com/.

Read the previous post on how compliance concerns and the EU’s GDPR are driving businesses to implement data governance.

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