Author: Bunny Tharpe

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A Guide to Enterprise Architecture Tools

Enterprise architecture tools are becoming more important than ever.

The International Enterprise Architecture Institute (IEAI) defines enterprise architecture (EA) as “the analysis and documentation of an enterprise in its current and future states from an integrated strategy, business and technology perspective.”

In the era of data-driven business, such perspective is critical.

IT has graduated from a support department to a proactive, value-driving function. As such, fostering alignment between IT and the wider organization has become more important than ever.

As the IEAI’s definition indicates, enterprise architecture tools are key drivers in ensuring such alignment because they help organizations understand their systems, applications and assets from a holistic, top-down perspective.

An organization can better identify gaps in its current architecture to better understand how to reach the desired future-state objectives and architecture.

Enterprise Architecture Tools

EA also enables a better understanding of change, or impact analysis – which is essential considering the agile, data-driven landscape and its state of flux.

Enterprise architecture tools allow an organization to map its applications – complete with their associated technologies and data – to the business functions they power.

For this reason, enterprise architecture tools also are key to a data governance initiative, and part of the technologies used as data governance tools.

EA leads to a greater understanding of the interdependencies of its data assets and enables an organization to better plan, budget and execute new strategy and ideas.

In addition to better impact analysis and ensuring IT-business alignment, enterprise architecture tools help organizations:

  • Model and integrate complex strategy, process, application, data and technology architectures
  • Collaborate with all stakeholders on innovation and transformation initiatives
  • Retain organizational knowledge

Enterprise architecture initially was housed within IT and therefore acted in an enterprise support role as well.

However, this led to the perception (and arguably, a reality) of enterprise architecture operating in an ivory tower, siloed from the wider business.

As problematic as that was in the years prior to the data-driven business surge, such problems have intensified in its wake.

Changing such a perception is critical for organizations looking to implement or mature an EA initiative.

Enterprise architecture tools with a greater emphasis on collaboration have been an excellent driver of such change.

With such enterprise architecture tools in place, organizations and their enterprise architects can employ more proactive, business outcome-oriented and value-driving applications for EA.

The Changing Role of the Enterprise Architect

The centralization of enterprise architecture has presented enterprise architects with new opportunities.

The role itself has become less pigeon-holed since outgrowing its IT silo. In fact, the enterprise architecture role itself has become less definable.

Now, organizations tend to organize enterprise architects in whatever way best serves their goals.

In an enterprise architecture team, each team member often will have some role-specific knowledge and then take the lead in managing that particular area.

For example, cases have been made for enterprise architects taking a seat at the security table.

And considering the growing importance of EA in the constantly changing data-driven business landscape, strong arguments can be made for enterprise architects reporting directly to the C-suite.

Like the tech industry in general, the only constant in enterprise architecture is change. Roles and titles will continue to evolve to meet new challenges in the face of digital transformation.

In recent years, enterprise architects and enterprise architecture tools are increasingly more involved in ideation and innovation management.

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

But changes to the way EA is applied require enterprise architects to change also. Thus, enterprise architects now have to ensure they’re not solely focussed on the standard EA framework.

Although such an approach might be useful to enterprise architects, it doesn’t necessarily translate to the wider business.

Enterprise architects adopting a more business-outcome approach to the way they work helps them better demonstrate the value of EA people outside its echo chamber.

Additionally, enterprise architects must recognize that today their work is never “finished.”

Too many enterprise architecture initiatives stall because of what we call “analysis paralysis.”

In a blog for Medium, Believe Success defined analysis paralysis as “an anti-pattern, the state of over-analyzing (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome.”

To avoid such a state, enterprise architects in the data-driven world must adopt a “just enough” approach to enterprise architecture.

The “just-enough” approach ensures EA is always focused on improving operations for the right business outcomes, not bogged down in analysis and jargon that does not translate to the wider organization.

As part of our wider Enterprise Data Governance Experience (EDGE) platform, erwin provides enterprise architecture tools tailor-made to meet the needs of the modern enterprise architect, as outlined above.

Click here for a free, full-featured, cloud-based trial of erwin EA powered by Casewise.

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Data Governance Tools: What Are They? Are They Optional?

Data governance tools used to occupy a niche in an organization’s tech stack, but those days are gone.

The rise of data-driven business and the complexities that come with it ushered in a soft mandate for data governance and data governance tools.

Data governance refers to the strategic and ongoing efforts by an organization to ensure that data is discoverable and its quality is good. It is also used to make data more easily understood and secure.

The technology that makes end-to-end data governance possible includes data cataloging, data literacy, business process modeling, enterprise architecture and data modeling.

Research indicates business leaders recognize the need for data governance tools. In fact, 98 percent of participants in erwin’s “2018 State of Data Governance Report” consider data governance either “important” or “critically important” to their organizations.

Over the years, organizations have faced a number of challenges pointing to the need for data governance tools, including:

  • the increasing volume, variety and velocity of data (the “three Vs”)
  • the potential revenue that well-governed data can drive
  • the employees and systems responsible for data, diversifying (or, data democratization)

Additionally, the unprecedented industry disruption of such data-driven companies as Airbnb, Netflix and Uber demonstrates the benefits of well-governed data.

Such examples were persuasive and pervasive, leading to the rise of data governance adoption.

Data Governance Tools

Data Governance Tools for Regulatory Compliance

In recent years, hard mandates for data governance also have increased.

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) requires organizations in the healthcare space to protect the privacy and security of certain health information.

Other highly regulated industries, like financial services, also face strict data privacy mandates, including those from the Basel Committee on Banking Supervision (BCBS) and the Financial Industry Regulatory Authority (FINRA).

Now new, industry-agnostic regulations such as the General Data Protection Regulation (GDPR) and the forthcoming California Consumer Protection Act (CCPA) leave little room for data-driven businesses to operate without data governance.

So it’s not surprising that the “2018 State of Data Governance Report” revealed regulatory compliance to be the leading driver in data governance adoption.

Data Governance Tools and Data Ethics

Customer trust/satisfaction is also a key driver for data governance. Given the landscape of modern business – in which data breaches make big and lasting news – this this is also not a surprise.

Fines levied against both Facebook and Google earlier this year are a reminder that regulators are serious, and the fines can be serious also.

But even without penalties from regulatory bodies, the cost of poor data governance is still huge.

IBM’s annual “Cost of a Data Breach” report found that the biggest cost of a data breach to an organization is a loss of business. It also found that, on average, a data breach can cost a business a staggering $3.9 million.

And perhaps more worrisome is that those figures are increasing. Costs have risen by 12 percent during the last five years.

It’s not just breaches. The prominence of data-related stories in the news is leading more and more people to be skeptical of how their personal data is handled.

Because of this, organizations with good data governance can make data ethics part of their brand. Some organizations are even beginning to hire “data ethicists” – employees dedicated to overseeing data ethics.

As the use cases for data-driven tech, such as AI, grow, you can expect the calls for ethical data practices to grow too.

Data Governance Tools Aren’t Optional

Considering the revenue potential, regulatory mandates and data-conscious consumers, a comprehensive data governance practice supported by robust data governance tools should no longer be seen as optional.

But what’s the best way to set up and sustain a data governance program?

Data Governance 1.0 was an isolated domain, managed by IT so it largely disconnected from the wider enterprise.

As data and the responsibilities for discovering, understanding and using it for strategic decision-making have become more democratized, a new approach for IT and business collaboration has taken hold.

Data Governance 2.0, as defined by Forrester, is as “an agile approach to data governance focused on just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem.”

At erwin, we believe in this approach and have incorporated it into what we refer to as the erwin Enterprise Data Governance Experience – or the erwin EDGE, for short.

The erwin EDGE empowers organizations with visibility and control over their data, both at rest and in motion.

It enables enterprises to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. It also helps organizations operationalize these steps.

Therefore the speed and quality of the data pipeline increases. Of course, metadata management is at the heart of any data governance initiative.

Gartner Magic Quadrant Metadata Management

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Top Use Cases for Enterprise Architecture: Architect Everything

Architect Everything: New use cases for enterprise architecture are increasing enterprise architect’s stock in data-driven business

As enterprise architecture has evolved, so to have the use cases for enterprise architecture.

Analyst firm Ovum recently released a new report titled Ovum Market Radar: Enterprise Architecture. In it, they make the case that enterprise architecture (EA) is becoming AE – or “architect everything”.

The transition highlights enterprise architecture’s evolution from being solely an IT function to being more closely aligned with the business. As such, the function has changed from EA to AE.

At erwin, we’re definitely witnessing this EA evolution as more and more as organizations undertake digital transformation initiatives, including rearchitecting their business models and value streams, as well as responding to increasing regulatory pressures.

This is because EA provides the right information to the right people at the right time for smarter decision-making.

Following are some of the top use cases for enterprise architecture that demonstrate how EA is moving beyond IT and into the business.

Enterprise Architecture Use Cases

Top 7 Use Cases for Enterprise Architecture

Compliance. Enterprise architecture is critical for regulatory compliance. It helps model, manage and transform mission-critical value streams across industries, as well as identify sensitive information. When thousands of employees need to know what compliance processes to follow, such as those associated with regulations (e.g., GDPR, HIPAA, SOX, CCPA, etc.) it ensures not only access to proper documentation but also current, updated information.

The Regulatory Rationale for Integrating Data Management & Data Governance

Data security/risk management. EA should be commonplace in data security planning. Any flaw in the way data is stored or monitored is a potential ‘in’ for a breach, and so businesses have to ensure security surrounding sensitive information is thorough and covers the whole business. Security should be proactive, not reactive, which is why EA should be a huge part of security planning.

Data governance. Today’s enterprise embraces data governance to drive data opportunities, including growing revenue, and limit data risks, including regulatory and compliance gaffes.

EA solutions that provide much-needed insight into the relationship between data assets and applications make it possible to appropriately direct data usage and flows, as well as focus greater attention, if warranted, on applications where data use delivers optimal business value.

Digital transformation. For an organization to successfully embrace change, innovation, EA and project delivery need to be intertwined and traceable. Enterprise architects are crucial to delivering innovation. Taking an idea from concept to delivery requires strategic planning and the ability to execute. An enterprise architecture roadmap can help focus such plans and many organizations are now utilizing them to prepare their enterprise architectures for 5G.

Mergers & acquisitions. Enterprise architecture is essential to successful mergers and acquisitions. It helps alignment by providing a business- outcome perspective for IT and guiding transformation. It also helps define strategy and models, improving interdepartmental cohesion and communication.

In an M&A scenario, businesses need to ensure their systems are fully documented and rationalized. This way they can comb through their inventories to make more informed decisions about which systems to cut or phase out to operate more efficiently.

Innovation management. EA is crucial to innovation and project delivery. Using open standards to link to other products within the overall project lifecycle, integrating agile enterprise architecture with agile development and connecting project delivery with effective governance.

It takes a rigorous approach to ensure that current and future states are published for a wider audience for consumption and collaboration – from modeling to generating road maps with meaningful insights provided to both technical and business stakeholders during every step.

Knowledge retention. Unlocking knowledge and then putting systems in place to retain that knowledge is a key benefit of EA. Many organizations lack a structured approach for gathering and investigating employee ideas. Ideas can fall into a black hole where they don’t get feedback and employees become less engaged.

When your enterprise architecture is aligned with your business outcomes, it provides a way to help your business ideate and investigate the viability of ideas on both the technical and business level.

If the benefits of enterprise architecture would help your business, here’s how you can try erwin EA for free.

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Top 3 Benefits of Enterprise Architecture

Benefits of Enterprise Architecture

Enterprise architecture (EA) benefits modern organizations in many ways. It provides a holistic, top down view of structure and systems, making it invaluable in managing the complexities of data-driven business.

Once considered solely a function of IT, enterprise architecture has historically operated from an ivory tower. It was often siloed from the business at large, stifling the potential benefits of the holistic view it could have provided.

Now, the growing importance of EA is reflected in its evolving position in the business. Instead of being considered just a function of IT, EA now plays a leading role in bridging the gap between IT and the business.

The practice has evolved in approach, too. In the past, enterprise architecture has played a foundational, support role – largely focused with “keeping the lights on.”

Today its scope is more progressive and business outcome-focused to identify opportunities for growth and change.

As a matter of fact, Gartner has said that EA is becoming a “form of internal management consulting” because it helps define and shape business and operating models, identify risk and opportunities, and create technology roadmaps to suit.

Analyst firm Ovum also recognizes EA’s evolution, referring to today’s EA as AE, or “architect everything,” further demonstrating its newfound scope.

 

Top Three Enterprise Architecture Benefits

Of course, enterprise architecture can’t sit at the strategy table without results. Following are what we believe to be the top three benefits of enterprise architecture:

1. Manage complexity

Modern organizations are a complicated mesh of different systems and applications of varying degrees of importance and prominence.

The top-down, holistic view of an organization provided by enterprise architecture means that organizations are more able to efficiently and confidently assess such assets. For example, impact analysis might identify areas where an organization can streamline its tech stack and cut costs.

It might uncover redundancies where multiple applications address the same process.

Alternatively, impact analysis might find that a seemingly less prominent application is actual integral to operations in circumstances where leadership are considering phasing it out.

In short, enterprise architecture helps business and IT leaders capture, understand and articulate opportunities, challenges and risks – including security.

2. Supporting the creation of actionable, signature-ready EA deliverables

As well as assessing an organization’s current capabilities, the holistic, top-down view provided by enterprise architecture also helps identify gaps.

A better understanding of its enterprise architecture means an organization can make more informed investment decisions. Of course, this means organizations have a better understanding of what they should invest in.

However, it also helps them better understand when, as more pressing concerns can be identified and roadmaps can be created to reflect an organization’s priorities. 

This approach helps an organization meet its current operational demands and opportunities, whilst navigating and mitigating disruptions. It can also ensure it does this in accordance with the longer-term strategic vision of the organization.

3. Increasing agility and speeding time to value

In the era of rapidly evolving technology and rampant – often disruptive – digital transformation, the need for enterprise architecture tools is abundantly clear. Organizations with a healthy understanding of their enterprise architecture are better equipped to evaluate and implement new technology in a timely and efficient manner. 

EA tools accelerate analysis and decision support for alternative investment, rationalization, and optimization opportunities and plans and for assessing risk, change and the impact on the organization.

Maturing Enterprise Architecture

To reap such benefits of this new approach to EA, many organizations will have to work to mature their practices.

To be effective, business outcome-focused enterprise architecture needs to be consistent. It needs to be communicable and discernible. It needs to be up to date and accurate.

For many organizations, these standards have been impossible to meet as their enterprise architectures are burdened by the use of systems that were not built for purpose.

Basic visualization tools, spreadsheets and even word processors have typically played stand-in for dedicated EA solutions. The non-purpose-built systems lacked the industry standards needed to accurately capture and align business and IT elements and how they link together.

Additionally, collaboration was often marred by issues with outdated, and even disparate file versions and types. This being due to business’ lacking the systems necessary to continuously and methodically maintain models, frameworks and concepts as they evolve.

Therefore, a key milestone in maturing a modern enterprise architecture initiative, is developing a single source of truth, consistent across the enterprise. This requires the implementation of a dedicated, centralized and collaborative enterprise architecture tool, be that on-premise, or via the cloud.

Of course, such a tool should cover enterprise architecture’s legacy capabilities and expectations. Those include support for industry standard frameworks and notation, the ability to perform impact analysis and the streamlining of systems and applications.

But to mature the practice, organizations should implement an EA tool with a shared, centralized metadata repository and role-based access.

It should have the ability to share an integrated set of views and information on strategy, business capabilities, applications, information assets, technologies, etc., to help provide stakeholders with a thorough understanding of the enterprise.

Once this milestone has been met, organizations can really begin to enjoy the benefits of enterprise architecture, in the modern, data-driven business context.

If the benefits of enterprise architecture would help your business, and you’d like to be the next erwin EA success story, try erwin’s enterprise architecture and business process modeling software for free.

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5G Roadmap: Preparing Your Enterprise Architecture

Why planning your 5G roadmap requires significant input from enterprise architects

5G is coming and bringing with it the promise to transform any industry. And while the focus has been on the benefits to consumers,  the effects on the enterprise are far-reaching.

Few examples of emerging technology have the potential to disrupt and downright revolutionize certain markets and processes than 5G.

For enterprise architects, it’s important to understand how a potentially disruptive emerging technology like 5G might be incorporated into an organization, in advance.

A 5G roadmap could be the difference between such disruptions being an obstruction or an opportunity.

As with any emerging technology,  organizations need to test and pilot their projects to answer some important questions before going into production:

  • How do these technologies disrupt?
  • How do they provide value?

While the transition from 3G to 4G wasn’t all that eventful – or all that long ago – 5G is expected to buck the trend.

But how exactly?

5G: What to expect

5G promises dramatically faster download and upload speeds and reduced latency.

For context, average 4G speeds peak at around 45 Mbps (megabits per second); the industry goal is to hit 1 Gb (gigabit per second = 1,000 Mbps).

Telecom company Qualcomm believes real-world applications of 5G could be 10 to 20 times faster than that.

For consumers, this will mean dramatically faster downloads and uploads. Currently, downloading a two-hour movie takes around six  minutes on 4G. A 5G connection would achieve the same in just 3.6 seconds.

Organizations will, of course, enjoy the same benefits but will be burdened by the need to manage new levels of data, starting with telecommunications companies (telcos).

5G – A disruptive force vs. a catalyst for disruption

Usually, when we think of emerging disruptive technologies, the technology (or process, product, etc.) itself is the primary cause of the disruption.

With 5G, that’s still somewhat true. At least for telcos …

For example, 5G-driven disruption is forcing telecommunications companies to upgrade their infrastructure to cope with new volumes and velocities of data.

On a base level, these higher data volumes and velocities will be attributable to the fact that by making something happen faster, more of it can happen in a shorter amount of time.

But the increase in data speeds will be a catalyst for products and services that are currently not feasible becoming completely viable in the near future.

Of course, enterprise architecture is already integral to organizations with Internet of Things (IoT) devices in their portfolios.

5G enterprise architecture roadmap

But companies involved in internet-connected product market, as well as telcos, will need a 5G roadmap to ensure their enterprise architectures can cope with the additional data burden.

In addition to faster connection speeds, 5G will grant telcos more control over networks.

One such example of this control is the potential for network slicing, whereby multiple virtual networks can be generated within one physical 5G network, in turn allowing greater control of the service provided.

For example, self-driving cars would benefit from a network slice that offered exceptionally fast, low-latency connections to better accommodate their real-time data processing and transmitting needs.

Such a set up would go to waste for less-interactive, internet-connected devices. A smart fridge for example, could make do with far slower connection speeds.

This would mean telecommunications companies would start to look more like public-cloud providers and offer scalable services to their user bases.

However, realizing this potential would require more agile-oriented infrastructures than telcos typically have – which will of course require further input from enterprise architects to encourage an efficient implementation.

Another red pin to account for on the 5G roadmap.

So the answer to “Is 5G a disruptive force in and of itself, or is it a catalyst for disruption?” is actually … well, both. With telcos directly impacted by 5G disruption, and IoT product/service providers and digital business on the whole being disrupted by what 5G ultimately enables.

What does this mean for enterprise architects?

As addressed above, many of the business benefits of 5G are directly tied to increasing the amount of data that can be transferred at one time.

This presents a number of challenges for enterprise architects going forward.

As well as the increased volume of data itself, enterprise architects will need to prepare for faster times to market.

Radically improved data transfer speeds will encourage more agile product rollouts and updates, especially in connected devices that will feedback data insights about their performance.

The reduced latency will likely lead to a new influx of remote working, collaboration- enabling tools as well as products and services currently unaccounted for. Organizations with more agile enterprise architectures will be better placed to implement these smoothly when the time comes.

To better understand how your organization can prepare for 5G by adopting an agile enterprise architecture approach, click here.

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Choosing the Right Data Modeling Tool

The need for an effective data modeling tool is more significant than ever.

For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development. But it provides even greater value for modern enterprises where critical data exists in both structured and unstructured formats and lives both on premise and in the cloud.

In today’s hyper-competitive, data-driven business landscape, organizations are awash with data and the applications, databases and schema required to manage it.

For example, an organization may have 300 applications, with 50 different databases and a different schema for each. Additional challenges, such as increasing regulatory pressures – from the General Data Protection Regulation (GDPR) to the Health Insurance Privacy and Portability Act (HIPPA) – and growing stores of unstructured data also underscore the increasing importance of a data modeling tool.

Data modeling, quite simply, describes the process of discovering, analyzing, representing and communicating data requirements in a precise form called the data model. There’s an expression: measure twice, cut once. Data modeling is the upfront “measuring tool” that helps organizations reduce time and avoid guesswork in a low-cost environment.

From a business-outcome perspective, a data modeling tool is used to help organizations:

  • Effectively manage and govern massive volumes of data
  • Consolidate and build applications with hybrid architectures, including traditional, Big Data, cloud and on premise
  • Support expanding regulatory requirements, such as GDPR and the California Consumer Privacy Act (CCPA)
  • Simplify collaboration across key roles and improve information alignment
  • Improve business processes for operational efficiency and compliance
  • Empower employees with self-service access for enterprise data capability, fluency and accountability

Data Modeling Tool

Evaluating a Data Modeling Tool – Key Features

Organizations seeking to invest in a new data modeling tool should consider these four key features.

  1. Ability to visualize business and technical database structures through an integrated, graphical model.

Due to the amount of database platforms available, it’s important that an organization’s data modeling tool supports a sufficient (to your organization) array of platforms. The chosen data modeling tool should be able to read the technical formats of each of these platforms and translate them into highly graphical models rich in metadata. Schema can be deployed from models in an automated fashion and iteratively updated so that new development can take place via model-driven design.

  1. Empowering of end-user BI/analytics by data source discovery, analysis and integration. 

A data modeling tool should give business users confidence in the information they use to make decisions. Such confidence comes from the ability to provide a common, contextual, easily accessible source of data element definitions to ensure they are able to draw upon the correct data; understand what it represents, including where it comes from; and know how it’s connected to other entities.

A data modeling tool can also be used to pull in data sources via self-service BI and analytics dashboards. The data modeling tool should also have the ability to integrate its models into whatever format is required for downstream consumption.

  1. The ability to store business definitions and data-centric business rules in the model along with technical database schemas, procedures and other information.

With business definitions and rules on board, technical implementations can be better aligned with the needs of the organization. Using an advanced design layer architecture, model “layers” can be created with one or more models focused on the business requirements that then can be linked to one or more database implementations. Design-layer metadata can also be connected from conceptual through logical to physical data models.

  1. Rationalize platform inconsistencies and deliver a single source of truth for all enterprise business data.

Many organizations struggle to breakdown data silos and unify data into a single source of truth, due in large part to varying data sources and difficulty managing unstructured data. Being able to model any data from anywhere accounts for this with on-demand modeling for non-relational databases that offer speed, horizontal scalability and other real-time application advantages.

With NoSQL support, model structures from non-relational databases, such as Couchbase and MongoDB can be created automatically. Existing Couchbase and MongoDB data sources can be easily discovered, understood and documented through modeling and visualization. Existing entity-relationship diagrams and SQL databases can be migrated to Couchbase and MongoDB too. Relational schema also will be transformed to query-optimized NoSQL constructs.

Other considerations include the ability to:

  • Compare models and databases.
  • Increase enterprise collaboration.
  • Perform impact analysis.
  • Enable business and IT infrastructure interoperability.

When it comes to data modeling, no one knows it better. For more than 30 years, erwin Data Modeler has been the market leader. It is built on the vision and experience of data modelers worldwide and is the de-facto standard in data model integration.

You can learn more about driving business value and underpinning governance with erwin DM in this free white paper.

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Digital Transformation Examples: Three Industries Dominating Digital Transformation

Digital transformation examples can be found almost anywhere, in almost any industry. Its past successes – and future potential – are well documented, chronicled in the billion-dollar valuations of the frontrunners in the practice.

Amazon began as a disruptor to brick-and-mortar bookstores, eventually becoming one of the most obvious digital transformation examples as it went on to revolutionize online shopping.

Netflix’s origins were similar – annihilating its former rival Blockbuster and the entire DVD rental market to become a dominant streaming platform and media publisher.

Disruption is the common theme. Netflix decimated the DVD rental market while Amazon continues to play a role in “high-street” shopping’s decline.

As technology continues to disrupt markets, digital transformation is do or die.

According to IDC’s digital transformation predictions report for 2019, these types of initiatives are going to flood the enterprise during the next five years.

The following three examples highlight the extent to which digital transformation is reshaping the nature of business and government and how we – as a society – interact with the world.

Digital Transformation in Retail

The inherently competitive nature of retail has made the sector a leader in adopting data-driven strategy.

From loyalty cards to targeted online ads, retail has always had to adapt to stay relevant.

Four main areas in retail demonstrate digital transformation, with a healthy data governance initiative driving them all.

Digital transformation examples

With accurate, relevant and accessible data, organizations can address the following:

  • Customer experience: If your data shows a lot of abandoned carts from mobile app users, then that’s an area to investigate, and good data will identify it.
  • Competitive differentiation: Are personalized offers increasing sales and creating customer loyalty? This is an important data point for marketing strategy.
  • Supply chain:Can a problem with quality be related to items shipping from a certain warehouse? Data will zero in on the location of the problem.
  • Partnerships:Are your partnerships helping grow other parts of your business and creating new customers? Or are your existing customers using partners in place of visiting your store? Data can tell you.

This article further explores digital transformation and data governance in retail.

Digital Transformation in Hospitality

Hospitality is another industry awash in digital transformation examples. Brick-and-mortar travel agencies are ceding ground to mobile-first (and mobile-only) businesses.

Their offerings range from purchasing vacation packages to the ability to check in and order room service via mobile devices.

With augmented and virtual reality, it even may be possible to one day “test drive” holiday plans from the comfort of the sofa – say before swimming with sharks or going on safari.

The extent of digitization now possible in the hospitality industry means these businesses have to account for and manage an abundance of data types and sources to glean insights to fuel the best customer experiences.

Unsurprisingly, this is yet another area where a healthy data governance initiative can be the difference between industry-disrupting success and abject failure.

This piece further discusses how data is transforming the hospitality industry and the role of data governance in it.

Digital Transformation in Municipal Government

Historically, municipal government isn’t seen as an area at the forefront of adopting emerging technology.

But the emergence of “smart cities” is a prominent example of digital transformation.

Even the concept of a smart city is a response to existing digital transformation in the private sector, as governments have been coerced into updating infrastructure to reflect the modern world.

Today, municipal governments around the world are using digital transformation to improve residents’ quality of life, from improving transportation and public safety to making it convenient to pay bills or request services online.

Of course, when going “smart,” municipal governments will need an understanding of data governance best practices.

This article analyzes how municipal governments can be “smart” about their transformation efforts.

Mitigating Digital Transformation Risks

Risks come with any investment. But in the context of digital transformation, taking risks is both a necessity and an inevitability.

Organizations also will need to consult their data to ensure they transform themselves the right way – and not just for transformation’s sake.

A recent PwC study found that successful digital transformation risk-takers “find the right fit for emerging technologies.”

Doing so points to the need for both effective data governance to find, understand and socialize the most relevant data assets and healthy enterprise architecture to learn what systems and applications create, store and use those data assets.

With application portfolio management and impact analysis, organizations can identify immediate opportunities for digital transformation and areas where more consideration and planning may be necessary before making changes.

As the data governance company, we provide data governance as well as enterprise architecture software, plus tools for business process and data modeling, data cataloging and data literacy. As an integrated software platform, organizations ensure IT and business collaboration to drive risk management, innovation and transformation efforts.

If you’d like to learn more about digital transformation and other use cases for data governance technologies, stay up to date with the erwin Experts here.

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Democratizing Data and the Rise of the Citizen Analyst

Data innovation is flourishing, driven by the confluence of exploding data production, a lowered barrier to entry for big data, as well as advanced analytics, artificial intelligence and machine learning.

Additionally, the ability to access and analyze all of this information has given rise to the “citizen analyst” – a business-oriented problem-solver with enough technical knowledge to understand how to apply analytical techniques to collections of massive data sets to identify business opportunities.

Empowering the citizen analyst relies on, or rather demands, data democratization – making shared enterprise assets available to a set of data consumer communities in a governed way.

This idea of democratizing data has become increasingly popular as more organizations realize that data is everyone’s business in a data-driven organization. Those that embrace digital transformation, regardless of industry, experience new levels of relevance and success.

Securing the Asset

Consumers and businesses alike have started to view data as an asset they must take steps to secure. It’s both a lucrative target for cyber criminals and a combustible spark for PR fires.

However, siloing data can be just as costly.

For some perspective, we can draw parallels between a data pipeline and a factory production line.

In the latter example, not being able to get the right parts to the right people at the right time leads to bottlenecks that stall both production and potential profits.

The exact same logic can be applied to data. To ensure efficient processes, organizations need to make the right data available to the right people at the right time.

In essence, this is data democratization. And the importance of democratized data governance cannot be stressed enough. Data security is imperative, so organizations need both technology and personnel to achieve it.

And in regard to the human element, organizations need to ensure the relevant parties understand what particular data assets can be used and for what. Assuming that employees know when, what and how to use data can make otherwise extremely valuable data resources useless due to not understanding its potential.

The objectives of governed data democratization include:

  • Raising data awareness among the different data consumer communities to increase awareness of the data assets that can be used for reporting and analysis,
  • Improving data literacy so that individuals will understand how the different data assets can be used,
  • Supporting observance of data policies to support regulatory compliance, and
  • Simplifying data accessibility and use to support citizen analysts’ needs.

Democratizing Data: Introducing Democratized Data

To successfully introduce and oversee the idea of democratized data, organizations must ensure that information about data assets is accumulated, documented and published for context-rich use across the organization.

This knowledge and understanding are a huge part of data intelligence.

Data intelligence is produced by coordinated processes to survey the data landscape to collect, collate and publish critical information, namely:

  • Reconnaissance: Understanding the data environment and the corresponding business contexts and collecting as much information as possible;
  • Surveillance: Monitoring the environment for changes to data sources;
  • Logistics and Planning: Mapping the collected information production flows and mapping how data moves across the enterprise
  • Impact Assessment: Using what you have learned to assess how external changes impact the environment
  • Synthesis: Empowering data consumers by providing a holistic perspective associated with specific business terms
  • Sustainability: Embracing automation to always provide up-to-date and correct intelligence; and
  • Auditability: Providing oversight and being able to explain what you have learned and why

erwin recently sponsored a white paper about data intelligence and democratizing data.

Written by David Loshin of Knowledge Integrity, Inc., it take a deep dive into this topic and includes crucial advice on how organizations should evaluate data intelligence software prior to investment.

Data Intelligence: Democratizing Data

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erwin Expert Blog

A Guide to CCPA Compliance and How the California Consumer Privacy Act Compares to GDPR

California Consumer Privacy Act (CCPA) compliance shares many of the same requirements in the European Unions’ General Data Protection Regulation (GDPR).

While the CCPA has been signed into law, organizations have until Jan. 1, 2020, to enact its mandates. Luckily, many organizations have already laid the regulatory groundwork for it because of their efforts to comply with GDPR.

However, there are some key differences that we’ll explore in the Q&A below.

Data governance, thankfully, provides a framework for compliance with either or both – in addition to other regulatory mandates your organization may be subject to.

CCPA Compliance Requirements vs. GDPR FAQ

Does CCPA apply to not-for-profit organizations? 

No, CCPA compliance only applies to for-profit organizations. GDPR compliance is required for any organization, public or private (including not-for-profit).

What for-profit businesses does CCPA apply to?

The mandate for CCPA compliance only applies if a for-profit organization:

  • Has an annual gross revenue exceeding $25 million
  • Collects, sells or shares the personal data of 50,000 or more consumers, households or devices
  • Earns 50% of more of its annual revenue by selling consumers’ personal information

Does the CCPA apply outside of California?

As the name suggests, the legislation is designed to protect the personal data of consumers who reside in the state of California.

But like GDPR, CCPA compliance has impacts outside the area of origin. This means businesses located outside of California, but selling to (or collecting the data of) California residents must also comply.

Does the CCPA exclude anything that GDPR doesn’t? 

GDPR encompasses all categories of “personal data,” with no distinctions.

CCPA does make distinctions, particularly when other regulations may overlap. These include:

  • Medical information covered by the Confidentiality of Medical Information Act (CMIA) and the Health Insurance Portability and Accountability Act (HIPAA)
  • Personal information covered by the Gramm-Leach-Bliley Act (GLBA)
  • Personal information covered by the Driver’s Privacy Protection Act (DPPA)
  • Clinical trial data
  • Information sold to or by consumer reporting agencies
  • Publicly available personal information (federal, state and local government records)

What about access requests? 

Under the GDPR, organizations must make any personal data collected from an EU citizen available upon request.

CCPA compliance only requires data collected within the last 12 months to be shared upon request.

Does the CCPA include the right to opt out?

CCPA, like GDPR, empowers gives consumers/citizens the right to opt out in regard to the processing of their personal data.

However, CCPA compliance only requires an organization to observe an opt-out request when it comes to the sale of personal data. GDPR does not make any distinctions between “selling” personal data and any other kind of data processing.

To meet CCPA compliance opt-out standards, organizations must provide a “Do Not Sell My Personal Information” link on their home pages.

Does the CCPA require individuals to willingly opt in?

No. Whereas the GDPR requires informed consent before an organization sells an individual’s information, organizations under the scope of the CCPA can still assume consent. The only exception involves the personal information of children (under 16). Children over 13 can consent themselves, but if the consumer is a child under 13, a parent or guardian must authorize the sale of said child’s personal data.

What about fines for CCPA non-compliance? 

In theory, fines for CCPA non-compliance are potentially more far reaching than those of GDPR because there is no ceiling for CCPA penalties. Under GDPR, penalties have a ceiling of 4% of global annual revenue or €20 million, whichever is greater. GDPR recently resulted in a record fine for Google.

Organizations outside of CCPA compliance can only be fined up to $7,500 per violation, but there is no upper ceiling.

CCPA compliance is a data governance issue

Data Governance for Regulatory Compliance

While CCPA has a more narrow geography and focus than GDPR, compliance is still a serious effort for organizations under its scope. And as data-driven business continues to expand, so too will the pressure on lawmakers to regulate how organizations process data. Remember the Facebook hearings and now inquiries into Google and Twitter, for example?

Regulatory compliance remains a key driver for data governance. After all, to understand how to meet data regulations, an organization must first understand its data.

An effective data governance initiative should enable just that, by giving an organization the tools 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

A Regulatory EDGE

The erwin EDGE software platform creates an “enterprise data governance experience” to transform how all stakeholders discover, understand, govern and socialize data assets. It includes enterprise modeling, data cataloging and data literacy capabilities, giving organizations visibility and control over their disparate architectures and all the supporting data.

Both IT and business stakeholders have role-based, self-service access to the information they need to collaborate in making strategic decisions. And because many of the associated processes can be automated, you reduce errors and increase the speed and quality of your data pipeline. This data intelligence unlocks knowledge and value.

The erwin EDGE provides the most agile, efficient and cost-effective means of launching and sustaining a strategic and comprehensive data governance initiative, whether you wish to deploy on premise or in the cloud. But you don’t have to implement every component of the erwin EDGE all at once to see strategic value.

Because of the platform’s federated design, you can address your organization’s most urgent needs, such as regulatory compliance, first. Then you can proactively address other organization objectives, such as operational efficiency, revenue growth, increasing customer satisfaction and improving overall decision-making.

You can learn more about leveraging data governance to navigate the changing tide of data regulations here.

Are you compliant with data regulations?

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erwin Expert Blog

Keeping Up with New Data Protection Regulations

Keeping up with new data protection regulations can be difficult, and the latest – the General Data Protection Regulation (GDPR) – isn’t the only new data protection regulation organizations should be aware of.

California recently passed a law that gives residents the right to control the data companies collect about them. Some suggest the California Consumer Privacy Act (CCPA), which takes effect January 1, 2020, sets a precedent other states will follow by empowering consumers to set limits on how companies can use their personal information.

In fact, organizations should expect increasing pressure on lawmakers to introduce new data protection regulations. A number of high-profile data breaches and scandals have increased public awareness of the issue.

Facebook was in the news again last week for another major problem around the transparency of its user data, and the tech-giant also is reportedly facing 10 GDPR investigations in Ireland – along with Apple, LinkedIn and Twitter.

Some industries, such as healthcare and financial services, have been subject to stringent data regulations for years: GDPR now joins the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS) and the Basel Committee on Banking Supervision (BCBS).

Due to these pre-existing regulations, organizations operating within these sectors, as well as insurance, had some of the GDPR compliance bases covered in advance.

Other industries had their own levels of preparedness, based on the nature of their operations. For example, many retailers have robust, data-driven e-commerce operations that are international. Such businesses are bound to comply with varying local standards, especially when dealing with personally identifiable information (PII).

Smaller, more brick-and-mortar-focussed retailers may have had to start from scratch.

But starting position aside, every data-driven organization should strive for a better standard of data management — and not just for compliance sake. After all, organizations are now realizing that data is one of their most valuable assets.

New Data Protection Regulations – Always Be Prepared

When it comes to new data protection regulations in the face of constant data-driven change, it’s a matter of when, not if.

As they say, the best defense is a good offense. Fortunately, whenever the time comes, the first point of call will always be data governance, so organizations can prepare.

Effective compliance with new data protection regulations requires a robust understanding of the “what, where and who” in terms of data and the stakeholders with access to it (i.e., employees).

The Regulatory Rationale for Integrating Data Management & Data Governance

This is also true for existing data regulations. Compliance is an on-going requirement, so efforts to become compliant should not be treated as static events.

Less than four months before GDPR came into effect, only 6 percent of enterprises claimed they were prepared for it. Many of these organizations will recall a number of stressful weeks – or even months – tidying up their databases and their data management processes and policies.

This time and money was spent reactionarily, at the behest of proactive efforts to grow the business.

The implementation and subsequent observation of a strong data governance initiative ensures organizations won’t be put on the spot going forward. Should an audit come up, current projects aren’t suddenly derailed as they reenact pre-GDPR panic.

New Data Regulations

Data Governance: The Foundation for Compliance

The first step to compliance with new – or old – data protection regulations is data governance.

A robust and effective data governance initiative ensures an organization understands where security should be focussed.

By adopting a data governance platform that enables you to automatically tag sensitive data and track its lineage, you can ensure nothing falls through the cracks.

Your chosen data governance solution should enable you to automate the scanning, detection and tagging of sensitive data by:

  • Monitoring and controlling sensitive data – Gain better visibility and control across the enterprise to identify data security threats and reduce associated risks.
  • Enriching business data elements for sensitive data discovery – By leveraging a comprehensive mechanism to define business data elements for PII, PHI and PCI across database systems, cloud and Big Data stores, you can easily identify sensitive data based on a set of algorithms and data patterns.
  • Providing metadata and value-based analysis – Simplify the 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 and PCI.

With these precautionary steps, organizations are primed to respond if a data breach occurs. Having a well governed data ecosystem with data lineage capabilities means issues can be quickly identified.

Additionally, if any follow-up is necessary –  such as with GDPR’s data breach reporting time requirements – it can be handles swiftly and in accordance with regulations.

It’s also important to understand that the benefits of data governance don’t stop with regulatory compliance.

A better understanding of what data you have, where it’s stored and the history of its use and access isn’t only beneficial in fending off non-compliance repercussions. In fact, such an understanding is arguably better put to use proactively.

Data governance improves data quality standards, it enables better decision-making and ensures businesses can have more confidence in the data informing those decisions.

The same mechanisms that protect data by controlling its access also can be leveraged to make data more easily discoverable to approved parties – improving operational efficiency.

All in all, the cumulative result of data governance’s influence on data-driven businesses both drives revenue (through greater efficiency) and reduces costs (less errors, false starts, etc.).

To learn more about data governance and the regulatory rationale for its implementation, get our free guide here.

DG RediChek