Tag: data-driven business

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The Design Thinking Process: Five Stages to Solving Business Problems

The design thinking process is a method of encouraging and improving creative problem-solving.

The design thinking process is by no means new.

John Edward Arnold, a professor of mechanical engineering and business administration, was one of the first to discuss the concept in as early as the 1950s.

But the wave of digital and data-driven business has created new opportunities for the design thinking process to be applied.

For example, your business is likely collecting, storing and analyzing more information than ever before.

And while the intense focus on analytics in recent years has been good for many businesses, it’s important to remember the human element of making decisions and solving problems.

So with that in mind, the design thinking process can be used to bridge the gap between the data and the people.

But what is the design thinking process, exactly? And how does it work?

Design Thinking Definition: The Five Stages of the Design Thinking Process

There are lots of ways to harness ideas and solve problems. Design thinking is one means to foster and refine creative problem-solving.

While it doesn’t suggest ignoring your data, design thinking is, at its core, human-centered. It encourages organizations to focus on the people they’re creating for in hopes of producing better products, services and internal processes.

5 Stages in the Design Thinking Process

There are five stages in the design thinking process:

1. Empathize – The first stage of the design thinking process is gaining a better understanding of what problems need solving. It puts the end user you are trying to help first and encourages you to work backwards. By consulting and subsequently empathizing with the end user, you ensure your eventual solution is goal-oriented, increasing the likelihood of its effectiveness.

2. Define the problem – Once you have a better understanding of potential issues, it’s time to get specific. At this point, it’s good practice to translate the problem into a “problem statement” – a concise description of the issue that identifies the current state you wish to address and the desired future state you intend to reach.

3. Ideate solutions – This is the time to get creative. Once you have a solid understanding of the problem you can brainstorm ideas to bridge the gap between the current and the desired future state to eliminate it.

4. Prototype – At stage four, it’s time to implement the ideas from stage three in the real world. Typically, the prototype will be a scaled-down example of the solution – or ideally, possible solutions. It goes without saying, but things are rarely perfect in their first iteration, as you’ll likely discover in the next stage.

5. Test – At this point, it’s time to test whether the proposed solution works. In the case of multiple potential solutions, this stage can identify which is most effective and/or efficient. It’s also an opportunity to assess what – if any – new problems the solution might cause.

With this in mind, it’s important to remember that progression through the five stages of the design thinking process isn’t necessarily linear.

Unsuccessful tests could lead your team back to the ideation stage. In some cases, you may want to circle back to stage one to test your new solution with end users. Then you’ll be able to better emphasize and understand how your solution might work in practice.

It’s also important to understand that the design thinking process is not, strictly speaking, the same as innovation. It’s an approach to problem-solving that may ultimately involve innovation or emerging technologies, but innovation is not inherently required.

Design thinking is an iterative process, and the best solutions that come out of it in many organizations will become part of their enterprise architectures.

Incorporating Design Thinking into Your Organization with Enterprise Architecture

The best way to put design thinking into use in your organization is by creating a strategic planning approach that takes ideas from assessment to analysis to delivery.

By employing an iterative approach with a thorough assessment and a feedback loop, everyone in your organization will feel more empowered and engaged.

The reality of business today is that nearly every business problem is going to have a technological solution.

It will fall to the IT organization to take the ideas that come out of your design thinking and figure out how to deliver them as solutions at scale and speed.

This is where enterprise architecture comes into play.

Evaluating, planning and deploying a business solution will require visibility. How will these solutions impact users? Can they be supported by the existing IT infrastructure? How do they fit into the business ecosystem?

When it comes to these important questions, the best place to get answers is from your enterprise architecture team. Be sure to make them a central part of your design thinking process.

In addition to enterprise architecture software, erwin also provides enterprise architecture consulting. You can learn more about those services here.

You also can try all the current features of erwin EA for free via our secure, cloud-based trial environment.

Enterprise Architecture Business Process Trial

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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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Compliance First: How to Protect Sensitive Data

The ability to more efficiently govern, discover and protect sensitive data is something that all prospering data-driven organizations are constantly striving for.

It’s been almost four months since the European Union’s General Data Protection Regulation (GDPR) took effect. While no fines have been issued yet, the Information Commissioner’s Office has received upwards of 500 calls per week since the May 25 effective date.

However, the fine-free streak may be ending soon with British Airways (BA) as the first large company to pay a GDPR penalty because of a data breach. The hack at BA in August and early September lasted for more than two weeks, with intruders getting away with account numbers and personal information of customers making reservations on the carrier’s website and mobile app. If regulators conclude that BA failed to take measures to prevent the incident— a significant fine may follow.

Additionally, complaints against Google in the EU have started. For example, internet browser provider Brave claims that Google and other advertising companies expose user data during a process called “bid request.” A data breach occurs because a bid request fails to protect sensitive data against unauthorized access, which is unlawful under the GDPR.

Per Brave’s announcement, bid request data can include the following personal data:

  • What you are reading or watching
  • Your location
  • Description of your device
  • Unique tracking IDs or a “cookie match,” which allows advertising technology companies to try to identify you the next time you are seen, so that a long-term profile can be built or consolidated with offline data about you
  • Your IP address,depending on the version of “real-time bidding” system
  • Data broker segment ID, if available, which could denote things like your income bracket, age and gender, habits, social media influence, ethnicity, sexual orientation, religion, political leaning, etc., depending on the version of bidding system

Obviously, GDPR isn’t the only regulation that organizations need to comply with. From HIPAA in healthcare to FINRA, PII and BCBS in financial services to the upcoming California Consumer Privacy Act (CCPA) taking effect January 1, 2020, regulatory compliance is part of running – and staying in business.

The common denominator in compliance across all industry sectors is the ability to protect sensitive data. But if organizations are struggling to understand what data they have and where it’s located, how do they protect it? Where do they begin?

Protect sensitive data

Discover and Protect Sensitive Data

Data is a critical asset used to operate, manage and grow a business. While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.

Knowing where sensitive data is located and properly governing it with policy rules, impact analysis and lineage views is critical for risk management, data audits and regulatory compliance.

However, when key data isn’t discovered, harvested, cataloged, defined and standardized as part of integration processes, audits may be flawed and therefore putting your organization at risk.

Sensitive data – at rest or in motion – that exists in various forms across multiple systems must be automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found and its usage across workflows easily traced.

Thankfully, tools are available to help automate the scanning, detection and tagging of sensitive data by:

  • Monitoring and controlling sensitive data: Better visibility and control across the enterprise to identify data security threats and reduce associated risks
  • Enriching business data elements for sensitive data discovery: Comprehensive mechanism to define business data element for PII, PHI and PCI across database systems, cloud and Big Data stores to easily identify sensitive data based on a set of algorithms and data patterns
  • Providing metadata and value-based analysis: Discovery and classification of sensitive data based on metadata and data value patterns and algorithms. Organizations can define business data elements and rules to identify and locate sensitive data including PII, PHI, PCI and other sensitive information.


A Regulatory Rationale for Integrating Data Management and Data Governance

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

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.

But how is all this possible? Again, it comes back to the right technology for IT and business collaboration that will enable you to:

  • Discover data: Identify and interrogate metadata from various data management silos
  • Harvest data: Automate the collection of metadata from various data management silos and consolidate it into a single source
  • Structure 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 all stakeholders to see data in one place in their own context
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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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Data Governance Helps Build a Solid Foundation for Analytics

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

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

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

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

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

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

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

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

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

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

Reputation Management - What's Driving Data Governance

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

Data-driven Analytics: How Important is Data Governance

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

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

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

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

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

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

Previous posts:

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

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Five Pillars of Data Governance Readiness: Delivery Capability

The five pillars of data governance readiness should be the starting point for implementing or revamping any DG initiative.

In a recent CSO Magazine article, “Why data governance should be corporate policy,” the author states: “Data is like water, and water is a fundamental resource for life, so data is an essential resource for the business. Data governance ensures this resource is protected and managed correctly enabling us to meet our customer’s expectations.”

Over the past few weeks, we’ve been exploring the five pillars of data governance (DG) readiness, and this week we turn our attention to the fifth and final pillar, delivery capability.

Together, the five pillars of data governance readiness work as a step-by-step guide to a successful DG implementation and ongoing initiative.

As a refresher, the first four pillars are:

  1. The starting point is garnering initiative sponsorship from executives, before fostering support from the wider organization.

 

  1. Organizations should then appoint a dedicated team to oversee and manage the initiative. Although DG is an organization-wide strategic initiative, it needs experience and leadership to guide it.

 

  1. Once the above pillars are accounted for, the next step is to understand how data governance fits with the wider data management suite so that all components of a data strategy work together for maximum benefits.

 

  1. And then enterprise data management methodology as a plan of action to assemble the necessary tools.

Once you’ve completed these steps, how do you go about picking the right solution for enterprise-wide data governance?

Five Pillars of Data Governance: Delivery Capability – What’s the Right Solution?

Many organizations don’t think about enterprise data governance technologies when they begin a data governance initiative. They believe that using some general-purpose tool suite like those from Microsoft can support their DG initiative. That’s simply not the case.

Selecting the proper data governance solution should be part of developing the data governance initiative’s technical requirements. However, the first thing to understand is that the “right” solution is subjective.

Data stewards work with metadata rather than data 80 percent of the time. As a result, successful and sustainable data governance initiatives are supported by a full-scale, enterprise-grade metadata management tool.

Additionally, many organizations haven’t implemented data quality products when they begin a DG initiative. Product selections, including those for data quality management, should be based on the organization’s business goals, its current state of data quality and enterprise data management, and best practices as promoted by the data quality management team.

If your organization doesn’t have an existing data quality management product, a data governance initiative can support the need for data quality and the eventual evaluation and selection of the proper data quality management product.

Enterprise data modeling is also important. A component of enterprise data architecture, it’s an enabling force in the performance of data management and successful data governance. Having the capability to manage data architecture and data modeling with the optimal products can have a positive effect on DG by providing the initiative architectural support for the policies, practices, standards and processes that data governance creates.

Finally, and perhaps most important, the lack of a formal data governance team/unit has been cited as a leading cause of DG failure. Having the capability to manage all data governance and data stewardship activities has a positive effect.

Shopping for Data Governance Technology

DG is part of a larger data puzzle. Although it’s a key enabler of data-driven business, it’s only effective in the context of the data management suite in which it belongs.

Therefore when shopping for a data governance solution, organizations should look for DG tools that unify critical data governance domains, leverage role-appropriate interfaces to bring together stakeholders and processes to support a culture committed to acknowledging data as the mission-critical asset that it is, and orchestrate the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

Data Governance Readiness: Delivery Capability

Here’s an initial checklist of questions to ask in your evaluation of a DG solution. Does it support:

  • Relational, unstructured, on-premise and cloud data?
  • Business-friendly environment to build business glossaries with taxonomies of data standards?
  • Unified capabilities to integrate business glossaries, data dictionaries and reference data, data quality metrics, business rules and data usage policies?
  • Regulating data and managing data collaboration through assigned roles, business rules and responsibilities, and defined governance processes and workflows?
  • Viewing data dashboards, KPIs and more via configurable role-based interfaces?
  • Providing key integrations with enterprise architecture, business process modeling/management and data modeling?
  • A SaaS model for rapid deployment and low TCO?

To assess your data governance readiness, especially with the General Data Protection Regulation about to take effect, click here.

You also can try erwin DG for free. Click here to start your free trial.

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Data Governance Readiness: The Five Pillars

In light of the General Data Protection Regulation (GDPR) taking effect in just three months, an understanding of data governance readiness has become paramount. Organizations need to make sure they’re ready to meet the world’s most comprehensive data privacy law’s requirements:

  • Understanding all the systems in which personal data is located and all the interactions that touch it
  • Knowing the original instance of the data plus its entire lineage and how it’s handled across the complete ecosystem
  • Ensuring changes, purges or other customer requests are adhered to in a timely manner
  • Notifying customers of a data breach within 72 hours

GDPR becomes effective in an age of rapidly proliferating customer data. For organizations to meet its demands, data governance (DG) must become operational. Done right, it holds great promise not only for regulatory compliance but also for creating data-driven opportunities that drive innovation and greater value.

The 2018 State of Data Governance Report shows that customer trust/satisfaction, decision-making, reputation management, analytics and Big Data are the key drivers of data governance adoption, behind meeting regulatory obligations.

Data Governance Readiness: Data Governance Drivers

A Question of Approach

There’s no question data governance is important and should be the cornerstone of data management to both reduce risks and realize larger organizational results, such as increasing customer satisfaction, improving decision-making, enhancing operational efficiency and growing revenue. The question is how to implement DG, so it does all that.

The boom in data-driven business, as well as new regulatory pressures, have thrust DG into a new spotlight. But the historical approach to DG, being housed in IT siloed from the parties who could use it the most, won’t work in the age of digital power brands like Airbnb, Amazon and Uber.

Data governance done right requires the participation of the entire enterprise and should be measured and measurable in the context of the business. Fortunately, Data Governance 2.0 builds on the principle that everyone in the organization has a role in the initiative, which is ongoing.

IT handles the technical mechanics of data management, but data governance is everyone’s business with stakeholders outside IT responsible for aligning DG with strategic organizational goals.

This creates an environment in which data is treated as an organizational asset that must be inventoried and protected as any physical asset, but it also can be understood in context and shared to unleash greater potential.

The Pillars of Data Governance Readiness

If you accept that data governance is a must for understanding critical data within a business context, tracking its physical existence and lineage, and maximizing its security, quality and value, are you ready to implement it as an enterprise initiative?

We’ve identified what we believe to be the five pillars of data governance readiness.

  1. Initiative Sponsorship

Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG. 

  1. Organizational Support

DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.

  1. Team Resources

Most successful organizations have established a formal data management group at the enterprise level. As a foundational component of enterprise data management, DG would reside in such a group.

  1. Enterprise Data Management Methodology

DG is foundational to enterprise data management. Without the other essential components (e.g., metadata management, enterprise data architecture, data quality management), DG will be struggle.

  1. Delivery Capability

Successful and sustainable DG initiatives are supported by specialized tools, which are scoped as part of the DG initiative’s technical requirements.

We’re going to explore these pillars of data governance readiness in future blog posts and through a new, free app to help you build – or shore up – your data governance initiative. By applying them, you’ll establish a solid data governance foundation to achieve the desired outcomes, from limiting the risk of data exposures to growing revenue.

In the meantime, you might want to check out our latest white paper that focuses on the impending GDPR and how to increase DG expertise because no organization with even one customer in the EU is outside its grasp. Click here to get the white paper.

Data Governance and GDPR: GDPR and Your Business Whitepaper

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Data Governance 2.0 for Financial Services

The tempo of change for data-driven business is increasing, with the financial services industry under particular pressure. For banks, credit card, insurance, mortgage companies and the like, data governance must be done right.

Consumer trust is waning across the board, and after several high-profile data breaches, trust in the way in which organizations handle and process data is lower still.

Equifax suffered 2017’s largest breach and the fifth largest in history. The subsequent plummet in stock value should have sent a stark warning to other financial service organizations. As of November, the credit bureau reported $87.5 million in expenses following the breach, and the PR fallout plummeted profits by 27 percent.

But it could be said that Equifax was lucky. If the breach had occurred following the implementation of the General Data Protection Regulation (GDPR), it also would have been hit with hefty sanctions. Come May of 2018, fines for GDPR noncompliance will reach an upper limit of €20 million or 4 percent of annual turnover – whichever is greater.

Data governance’s purpose – knowing where your data is and who is accountable for it – is a critical factor in preventing such breaches. It’s also a prerequisite for compliance as organizations need to demonstrate they have taken reasonable precautions in governing.

Equifax’s situation clearly implies that financial services organizations need to review and improve their data governance. As a concept, data governance for regulatory compliance is widely understood. Such regulations were introduced a decade ago in response to the financial crisis.

However, data governance’s role goes far beyond just preventing data breaches and meeting compliance standards.

Data Governance 2.0 for Financial Services

Data governance has struggled to gain a foothold because the value-adds have been unclear and largely untested. After new regulations for DG were introduced for the financial services industry, most organizations didn’t bother implementing company-wide approaches, instead opting to leave it as an IT-managed program.

So IT was responsible for cataloging data elements to support search and discovery, yet they rarely knew which bits of data were related or important to the wider business. This resulted in poor data quality and completeness, and left data and its governance siloed so data-driven business was hard to do.

Now data-driven business is more common – truly data-driven business with data at the core of strategy. The precedent has been set thanks to Airbnb, Amazon and Uber being some of the first businesses to use data to turn their respective markets on their heads.

These businesses don’t just use data to target new customers, they use data to help dictate strategy, find new gaps in the market, and highlight areas for performance improvement.

With that in mind, there’s a lot the financial services industry can learn and apply. FinTech start-ups continue to shake up the sector, and although the financial services industry is a more difficult industry to topple, traditional financial organizations need to innovate to stay competitive.

Alongside compliance, the aforementioned purpose of DG – knowing where data is stored and who is accountable for it – is also a critical factor in fostering agility, squashing times to market, and improving overall business efficiency, especially in the financial services industry.

In fact, the biggest advantage of data governance for financial services is making quality and reliable data readily available to the right people, so the right decisions can be made faster. Good DG also helps these companies better capitalize on revenue opportunities, solve customer issues, and identify fraud while improving the standard for reporting on such data.

These benefits are especially important within financial services because their big decisions have big financial impacts. To make such decisions, they need to trust that the data they use is sound and efficiently traceable.

Such data accountability is paramount. To achieve it, organizations must move away from the old, ineffective Data Governance 1.0 approach to the collaborative, outcome-driven Data Governance 2.0.

This means introducing data governance to the wider business, not just leaving it to IT. It means line-of-business managers and C-level executives take leading roles in data governance. But most importantly, it means a more efficient approach to data-driven business for increased revenue. A BCG study implies that financial services could be leaving up to $30 billion on the table.

Although the temptation to just meet regulatory compliance might be strong, the financial services industry clearly has a lot to gain from taking the extra step. Therefore, new regulations don’t have to be seen as a burden but as a catalyst for greater, proactive and forward-thinking change.

For more best practices in business and IT alignment, and successfully implementing data governance, click here.

Data governance is everyone's business