Tag: business transformation

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erwin Expert Blog

Why EA Needs to Be Part of Your Digital Transformation Strategy

Enterprise architecture (EA) isn’t dead, you’re just using it wrong. Part three of erwin’s digital transformation blog series.  

I’ll let you in on a little secret: the rumor of enterprise architecture’s demise has been greatly exaggerated. However, the truth for many of today’s fast-moving businesses is that enterprise architecture fails. But why?

Enterprise architecture is invaluable for internal business intelligence (but is rarely used for real intelligence), governance (but often has a very narrow focus), management insights (but doesn’t typically provide useful insights), and transformation and planning (ok, now we have something!).

In reality, most organizations do not leverage EA teams to their true potential. Instead they rely on consultants, trends, regulations and legislation to drive strategy.

Why does this happen?

Don’t Put Enterprise Architecture in a Corner

EA has remained in its traditional comfort zone of IT. EA is not only about IT …  but yet, EA lives within IT, focuses on IT and therefore loses its business dimension and support.

It remains isolated and is rarely, if ever, involved in:

  • Assessing, planning and running business transformation initiatives
  • Providing real, enterprise-wide insights
  • Producing actionable initiatives

Instead, it focuses on managing “stuff”:

  • Understanding existing “stuff” by gathering exhaustively detailed information
  • Running “stuff”-deployment projects
  • Managing cost “stuff”
  • “Moving to the cloud” (the solution to … everything)

Enterprise Architecture

What Prevents Enterprise Architecture from Being Successful?

There are three main reasons why EA has been pigeon-holed:

  1. Lack of trust in the available information
    • Information is mostly collected, entered and maintained manually
    • Automated data collection and connection is costly and error-prone
    • Identification of issues can be very difficult and time-consuming
  1. Lack of true asset governance and collaboration
    • Enterprise architecture becomes ring-fenced within a department
    • Few stakeholders willing to be actively involved in owning assets and be responsible for them
    • Collaboration on EA is seen as secondary and mostly focused on reports and status updates
  1. Lack of practical insights (insights, analyses and management views)
    • Too small and narrow thinking of what EA can provide
    • The few analyses performed focus on immediate questions, rarely planning and strategy
    • Collaboration on EA is seen as secondary and mostly focused on reports and status updates

Because of this, EA fails to deliver the relevant insights that management needs to make decisions – in a timely manner – and loses its credibility.

But the fact is EA should be, and was designed to be, about actionable insights leading to innovative architecture, not about only managing “stuff!”

Don’t Slow Your Roll. Elevate Your Role.

It’s clear that the role of EA in driving digital transformation needs to be elevated. It needs to be a strategic partner with the business.

According to a McKinsey report on the “Five Enterprise-Architecture Practices That Add Value to Digital Transformations,” EA teams need to:

“Translate architecture issues into terms that senior executives will understand. Enterprise architects can promote closer alignment between business and IT by helping to translate architecture issues for business leaders and managers who aren’t technology savvy. Engaging senior management in discussions about enterprise architecture requires management to dedicate time and actively work on technology topics. It also requires the EA team to explain technology matters in terms that business leaders can relate to.”

With that said, to further change the perception of EA within the organization you need to serve what management needs. To do this, enterprise architects need to develop innovative business, not IT insights, and make them dynamic. Next, enterprise architects need to gather information you can trust and then maintain.

To provide these strategic insights, you don’t need to focus on everything — you need to focus on what management wants you to focus on. The rest is just IT being IT. And, finally, you need to collaborate – like your life depends on it.

Giving Digital Transformation an Enterprise Architecture EDGE

The job of the enterprise architecture is to provide the tools and insights for the C-suite, and other business stakeholders, to help deploy strategies for business transformation.

Let’s say the CEO has a brilliant idea and wants to test it. This is EA’s sweet spot and opportunity to shine. And this is where erwin lives by providing an easy, automated way to deliver collaboration, speed and responsiveness.

erwin is about providing the right information to the right people at the right time. We are focused on empowering the forward-thinking enterprise architect by providing:

  • Superb, near real-time understanding of information
  • Excellent, intuitive collaboration
  • Dynamic, interactive dashboards (vertical and horizontal)
  • Actual, realistic, business-oriented insights
  • Assessment, planning and implementation support

Data-Driven Business Transformation

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erwin Expert Blog

Using Strategic Data Governance to Manage GDPR/CCPA Complexity

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements.

News broke earlier this week of British Airways being fined 183 million pounds – or $228 million – by the U.K. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). While not the first, it is the largest penalty levied since the GDPR went into effect in May 2018.

Given this, Oppenheimer & Co. cautions:

“European regulators could accelerate the crackdown on GDPR violators, which in turn could accelerate demand for GDPR readiness. Although the CCPA [California Consumer Privacy Act, the U.S. equivalent of GDPR] will not become effective until 2020, we believe that new developments in GDPR enforcement may influence the regulatory framework of the still fluid CCPA.”

With all the advance notice and significant chatter for GDPR/CCPA,  why aren’t organizations more prepared to deal with data regulations?

In a word? Complexity.

The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises.

So it’s important to understand how to use strategic data governance to manage the complexity of regulatory compliance and other business objectives …

Designing and Operationalizing Regulatory Compliance Strategy

It’s not easy to design and deploy compliance in an environment that’s not well understood and difficult in which to maneuver. First you need to analyze and design your compliance strategy and tactics, and then you need to operationalize them.

Modern, strategic data governance, which involves both IT and the business, enables organizations 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 enterprises put these strategic capabilities into action by:

  • Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance.
  • Creating and automating a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage.
  • Activating their metadata to drive agile data preparation and governance through integrated data glossaries and dictionaries that associate policies to enable stakeholder data literacy.

Strategic Data Governance for GDPR/CCPA

Five Steps to GDPR/CCPA Compliance

With the right technology, GDPR/CCPA compliance can be automated and accelerated in these five steps:

  1. Catalog systems

Harvest, enrich/transform and catalog data from a wide array of sources to enable any stakeholder to see the interrelationships of data assets across the organization.

  1. Govern PII “at rest”

Classify, flag and socialize the use and governance of personally identifiable information regardless of where it is stored.

  1. Govern PII “in motion”

Scan, catalog and map personally identifiable information to understand how it moves inside and outside the organization and how it changes along the way.

  1. Manage policies and rules

Govern business terminology in addition to data policies and rules, depicting relationships to physical data catalogs and the applications that use them with lineage and impact analysis views.

  1. Strengthen data security

Identify regulatory risks and guide the fortification of network and encryption security standards and policies by understanding where all personally identifiable information is stored, processed and used.

How erwin Can Help

erwin is the only software provider with a complete, metadata-driven approach to data governance through our integrated enterprise modeling and data intelligence suites. We help customers overcome their data governance challenges, with risk management and regulatory compliance being primary concerns.

However, the erwin EDGE also delivers an “enterprise data governance experience” in terms of agile innovation and business transformation – from creating new products and services to keeping customers happy to generating more revenue.

Whatever your organization’s key drivers are, a strategic data governance approach – through  business process, enterprise architecture and data modeling combined with data cataloging and data literacy – is key to success in our modern, digital world.

If you’d like to get a handle on handling your data, you can sign up for a free, one-on-one demo of erwin Data Intelligence.

For more information on GDPR/CCPA, we’ve also published a white paper on the Regulatory Rationale for Integrating Data Management and Data Governance.

GDPR White Paper

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erwin Expert Blog

Business Architecture and Process Modeling for Digital Transformation

At a fundamental level, digital transformation is about further synthesizing an organization’s operations and technology, so involving business architecture and process modeling is a best practice organizations cannot ignore.

This post outlines how business architecture and process modeling come together to facilitate efficient and successful digital transformation efforts.

Business Process Modeling: The First Step to Giving Customers What They Expect

Salesforce recently released the State of the Connected Customer report, with 75 percent of customers saying they expect companies to use new technologies to create better experiences. So the business and digital transformation playbook has to be updated.

These efforts must be carried out with continuous improvement in mind. Today’s constantly evolving business environment totally reinforces the old adage that change is the only constant.

Even historically reluctant-to-change banks now realize they need to innovate, adopting digital transformation to acquire and retain customers. Innovate or die is another adage that holds truer than ever before.

Fidelity International is an example of a successful digital transformation adopter and innovator. The company realized that different generations want different information and have distinct communication preferences.

For instance, millennials are adept at using digital channels, and they are the fastest-growing customer base for financial services companies. Fidelity knew it needed to understand customer needs and adapt its processes around key customer touch points and build centers of excellence to support them.

Business architecture and process modeling

Business Architecture and Process Modeling

Planning and working toward a flexible, responsive and adaptable future is no longer enough – the modern organization must be able to visualize not only the end state (the infamous and so-elusive “to-be”) but also perform detailed and comprehensive impact analysis on each scenario, often in real time. This analysis also needs to span multiple departments, extending beyond business and process architecture to IT, compliance and even HR and legal.

The ability of process owners to provide this information to management is central to ensuring the success of any transformation initiative. And new requirements and initiatives need to be managed in new ways. Digital and business transformation is about being able to do three things at the same time, all working toward the same goals:

  • Collect, document and analyze requirements
  • Establish all information layers impacted by the requirements
  • Develop and test the impact of multiple alternative scenarios

Comprehensive business process modeling underpins all of the above, providing the central information axis around which initiatives are scoped, evaluated, planned, implemented and ultimately managed.

Because of its central role, business process modeling must expand to modeling information from other layers within the organization, including:

  • System and application usage information
  • Supporting and reference documentation
  • Compliance, project and initiative information
  • Data usage

All these information layers must be captured and modeled at the appropriate levels, then connected to form a comprehensive information ecosystem that enables parts of the organization running transformation and other initiatives to instantly access and leverage it for decision-making, simulation and scenario evaluation, and planning, management and maintenance.

No Longer a Necessary Evil

Traditionally, digital and business transformation initiatives relied almost exclusively on human knowledge and experience regarding processes, procedures, how things worked, and how they fit together to provide a comprehensive and accurate framework. Today, technology can aggregate and manage all this information – and more – in a structured, organized and easily accessible way.

Business architecture extends beyond simple modeling; it also incorporates automation to reduce manual effort, remove potential for error, and guarantee effective data governance – with visibility from strategy all the way down to data entry and the ability to trace and manage data lineage. It requires robotics to cross-reference mass amounts of information, never before integrated to support effective decision-making.

The above are not options that are “nice to have,” but rather necessary gateways to taking business process management into the future. And the only way to leverage them is through systemic, organized and comprehensive business architecture modeling and analysis.

Therefore, business architecture and process modeling are no longer a necessary evil. They are critical success factors to any digital or business transformation journey.

A Competitive Weapon

Experts confirm the need to rethink and revise business processes to incorporate more digital automation. Forrester notes in its report, The Growing Importance of Process to Digital Transformation, that the changes in how business is conducted are driving the push “to reframe organizational operational processes around digital transformation efforts.” In a dramatic illustration of the need to move in this direction, the research firm writes that “business leaders are looking to use process as a competitive weapon.”

If a company hasn’t done a good job of documenting its processes, it can’t realize a future in which digital transformation is part of everyday operations. It’s never too late to start, though. In a fast-moving and pressure cooker business environment, companies need to implement business process models that make it possible to visually and analytically represent the steps that will add value to the company – either around internal operations or external ones, such as product or service delivery.

erwin BP, part of the erwin EDGE Platform, enables effective business architecture and process modeling. With it, any transformation initiative becomes a simple, streamlined exercise to support distributed information capture and management, object-oriented modeling, simulation and collaboration.

To find out about how erwin can help in empowering your transformation initiatives, please click here.

data-driven business transformation

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erwin Expert Blog

Managing Emerging Technology Disruption with Enterprise Architecture

Emerging technology has always played an important role in business transformation. In the race to collect and analyze data, provide superior customer experiences, and manage resources, new technologies always interest IT and business leaders.

KPMG’s The Changing Landscape of Disruptive Technologies found that today’s businesses are showing the most interest in emerging technology like the Internet of Things (IoT), artificial intelligence (AI) and robotics. Other emerging technologies that are making headlines include natural language processing (NLP) and blockchain.

In many cases, emerging technologies such as these are not fully embedded into business environments. Before they enter production, organizations need to test and pilot their projects to help answer some important questions:

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

Enterprise Architecture’s Role in Managing Emerging Technology

Pilot projects that take a small number of incremental steps, with small funding increases along the way, help provide answers to these questions. If the pilot proves successful, it’s then up to the enterprise architecture team to explore what it takes to integrate these technologies into the IT environment.

This is the point where new technologies go from “emerging technologies” to becoming another solution in the stack the organization relies on to create the business outcomes it’s seeking.

One of the easiest, quickest ways to try to pilot and put new technologies into production is to use cloud-based services. All of the major public cloud platform providers have AI and machine learning capabilities.

Integrating new technologies based in the cloud will change the way the enterprise architecture team models the IT environment, but that’s actually a good thing.

Modeling can help organizations understand the complex integrations that bring cloud services into the organization, and help them better understand the service level agreements (SLAs), security requirements and contracts with cloud partners.

When done right, enterprise architecture modeling also will help the organization better understand the value of emerging technology and even cloud migrations that increasingly accompany them. Once again, modeling helps answer important questions, such as:

  • Does the model demonstrate the benefits that the business expects from the cloud?
  • Do the benefits remain even if some legacy apps and infrastructure need to remain on premise?
  • What type of savings do you see if you can’t consolidate enough close an entire data center?
  • How does the risk change?

Many of the emerging technologies garnering attention today are on their way to becoming a standard part of the technology stack. But just as the web came before mobility, and mobility came before AI,  other technologies will soon follow in their footsteps.

To most efficiently evaluate these technologies and decide if they are right for the business, organizations need to provide visibility to both their enterprise architecture and business process teams so everyone understands how their environment and outcomes will change.

When the enterprise architecture and business process teams use a common platform and model the same data, their results will be more accurate and their collaboration seamless. This will cut significant time off the process of piloting, deploying and seeing results.

Outcomes like more profitable products and better customer experiences are the ultimate business goals. Getting there first is important, but only if everything runs smoothly on the customer side. The disruption of new technologies should take place behind the scenes, after all.

And that’s where investing in pilot programs and enterprise architecture modeling demonstrate value as you put emerging technology to work.

Emerging technology - Data-driven business transformation

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erwin Expert Blog

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

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

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

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

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

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

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

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

Data Quality Obstacles

The sad truth is that high-paid knowledge workers like data scientists spend up to 80 percent of their time finding and understanding source data and resolving errors or inconsistencies, rather than analyzing it for real value.

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

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

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

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

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

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

Creating a High-Quality Data Pipeline

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

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

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

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

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

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

Automate Data Mapping

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erwin Expert Blog Data Governance Data Intelligence

Demystifying Data Lineage: Tracking Your Data’s DNA

Getting the most out of your data requires getting a handle on data lineage. That’s knowing what data you have, where it is, and where it came from – plus understanding its quality and value to the organization.

But you can’t understand your data in a business context much less track data lineage, its physical existence and maximize its security, quality and value if it’s scattered across different silos in numerous applications.

Data lineage provides a way of tracking data from its origin to destination across its lifespan and all the processes it’s involved in. It also plays a vital role in data governance. Beyond the simple ability to know where the data came from and whether or not it can be trusted, there’s an element of statutory reporting and compliance that often requires a knowledge of how that same data (known or unknown, governed or not) has changed over time.

A platform that provides insights like data lineage, impact analysis, full-history capture, and other data management features serves as a central hub from which everything can be learned and discovered about the data – whether a data lake, a data vault or a traditional data warehouse.

In a traditional data management organization, Excel spreadsheets are used to manage the incoming data design, what’s known as the “pre-ETL” mapping documentation, but this does not provide any sort of visibility or auditability. In fact, each unit of work represented in these ‘mapping documents’ becomes an independent variable in the overall system development lifecycle, and therefore nearly impossible to learn from much less standardize.

The key to accuracy and integrity in any exercise is to eliminate the opportunity for human error – which does not mean eliminating humans from the process but incorporating the right tools to reduce the likelihood of error as the human beings apply their thought processes to the work.

Data Lineage

Data Lineage: A Crucial First Step for Data Governance

Knowing what data you have and where it lives and where it came from is complicated. The lack of visibility and control around “data at rest” combined with “data in motion,” as well as difficulties with legacy architectures, means organizations spend more time finding the data they need rather than using it to produce meaningful business outcomes.

Organizations need to create and sustain an enterprise-wide view of and easy access to underlying metadata, but 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, resulting in faulty analyses.

These issues can be addressed with a strong data management strategy underpinned by technology that enables the data quality the business requires, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

An automated, metadata-driven framework for cataloging data assets and their flows across the business provides an efficient, agile and dynamic way to generate data lineage from operational source systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture; construct business glossaries; assess what data aligns with specific business rules and policies; and inform how that data is transformed, integrated and federated throughout business processes – complete with full documentation.

Centralized design, immediate lineage and impact analysis, and change-activity logging means you will always have answers readily available, or just a few clicks away. Subsets of data can be identified and generated via predefined templates, generic designs generated from standard mapping documents, and pushed via ETL process for faster processing via automation templates.

With automation, data quality is systemically assured and the data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders. Without such automation, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by a manual approach. And outsourcing these data management efforts to professional services firms only increases costs and schedule delays.

With erwin Mapping Manager, organizations can automate enterprise data mapping and code generation for faster time-to-value and greater accuracy when it comes to data movement projects, as well as synchronize “data in motion” with data management and governance efforts.

Map data elements to their sources within a single repository to determine data lineage, deploy data warehouses and other Big Data solutions, and harmonize data integration across platforms. The web-based solution reduces the need for specialized, technical resources with knowledge of ETL and database procedural code, while making it easy for business analysts, data architects, ETL developers, testers and project managers to collaborate for faster decision-making.

Data Lineage

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erwin Expert Blog

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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erwin Expert Blog

Benefits of Process: Why Modern Organizations Need Process-Based Engines

In the current data-driven business climate, the benefits of process and process-based strategy are more desirable to organizations than ever.

Industry regulations and competition traditionally have driven organizational change, but such “transformation” has rarely been comprehensive or truly transformative. Rather, organizational transformation has come in waves, forcing companies and their IT ecosystems to ride them as best as they can – sometimes their fortunes have risen, and sometimes they have waned.

The advent of Brexit and GDPR have again forced today’s organizations to confront external stimuli’s impact on their operations. The difference is that the modern, process-based enterprises can better anticipate these sorts of mandates, incorporate them into their strategic plans, and even leapfrog ahead of their requirements by initiating true internal transformation initiatives – ones based on effectively managed and well-documented business processes.

Shifting Attitudes

Traditional organizations focus almost exclusively on rigid structures, centralized management and accountability; concentrated knowledge; service mainly to external customers; and reactive, short-term strategy alignment driven mainly by massive-scale projects. This traditional approach results in large, unwieldy and primarily reactive organizations that rely either on legacy strengths or inertia for survival.

But as technology evolves and proliferates, more and more organizations are realizing they need to adjust their traditional thinking and subsequent actions, even if just slightly, to gain strategic advantage, reduce costs and retain market dominance. For example:

  • Structures are becoming more adaptable, allowing for greater flexibility and cost management. How is this possible and why now? Organizations are grasping that effective, well-managed and documented business processes should form their operational backbones.
  • Business units and the departments within them are becoming accountable not only for their own budgets but also on how well they achieve their goals. This is possible because their responsibilities and processes can be clearly defined, documented and then monitored to ensure their work is executed in a repeatable, predictable and measurable way.
  • Knowledge is now both centralized and distributed thanks to modern knowledge management systems. Central repositories and collaborative portals give everyone within the organization equal access to the data they need to do their jobs more effectively and efficiently.
  • And thanks to all the above, organizations can expand their focus from external customers to internal ones as well. By clearly identifying individual processes (and their cross-business handover points) and customer touchpoints, organizations can interact with any customer at the right point with the most appropriate resources.

If business drivers are connected to processes with appropriate accountability, they become measurable in dimensions never before possible. Such elements as customer-journey quality and cost, process-delivery efficiency and even bottom-up cost aggregation can be captured. Strategic decision-making then becomes infinitely practical and forward-looking.

With this interconnected process – and information – based ecosystem, management can perform accurate and far-reaching impact analyses, test alternate scenarios, and evaluate their costs and implementation possibilities (and difficulties) to make decisions with full knowledge of their implications. Organizational departments can provide real-time feedback on designs and projects, turning theoretical designs into practical plans with buy-in at the right levels.

Benefits of Process

As stated above, one of the key benefits of process and a process-based organizational engine is that organizations should be able to better handle outside pressures, such as new regulations, if they are – or are becoming – truly process-based. Because once processes (and their encompassing business architecture) become central to the organization, a wide array of things become simpler, faster and cheaper.

The benefits of process don’t stop there either. Application design – the holy grail or black hole of budgetary spending and project management, depending on your point of view – is streamlined, with requirements clearly gathered and managed in perfect correspondence to the processes they serve and with the data they manage clearly documented and communicated to the developers. Testing occurs against real-life scenarios by the responsible parties as documented by the process owners – a drastic departure from the more traditional approaches in which the responsibility fell to designated, usually technical application owners.

Finally – and most important – data governance is no longer the isolated domain of data architects but central to the everyday processes that make an organization tick. As processes have stakeholders who use information – data – the roles of technical owners and data stewards become integral to ensuring processes operate efficiently, effectively and – above all – without interruptions. On the other side of this coin, data owners and data stewards no longer operate in their own worlds, distant from the processes their data supports.

Seizing a Process-Based Future

Process is a key axis along which the modern organization must operate. Data governance is another, with cost management becoming a third driver for the enterprise machine. But as we all know, it takes more than stable connecting rods to make an engine work – it needs cogs and wheels, belts and multiple power sources, all working together.

In the traditional organization, people are the internal mechanics. But one can’t escape visions of Charlie Chaplin’s Modern Times worker hopelessly entangled in the machine on which he was working. That’s why, these days, powerful and flexible workflow engines provide much-needed automation for greater visibility plus more power, stability and quality – all the things a machine needs to operate as required/designed.

Advanced process management systems are becoming essential, not optional. And while not as sexy or attention-grabbing as other technologies, they provide the power to drive an organization toward its goals quickly, cost-effectively and efficiently.

To learn how erwin can empower a modern, process-based organization, please click here.

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Business Process Modeling and Its Role Within the Enterprise

To achieve its objectives, an organization must have a complete understanding of its processes. Therefore, business process design and analysis are key to defining how a business operates and ensures employees understand and are accountable for carrying out their responsibilities.

Understanding system interactions, business processes and organizational hierarchies creates alignment, with everyone pulling in the same direction, and supports informed decision-making for optimal results and continuous improvement.

Those organizations operating in industries in which quality, health, safety and environmental issues are constant concerns must be even more in tune with their complexities. After all, revenue and risk are inextricably linked.

What Is Business Process Modeling and Why Does It Matter?

A business process is “an activity or set of activities that will accomplish a specific organizational goal,” as defined by TechTarget. Business process modeling “links business strategy to IT systems development to ensure business value,” according to Gartner.

The research firm goes on to explain that it “combines process/workflow, functional, organizational and data/resource views with underlying metrics, such as costs, cycle times and responsibilities, you establish a foundation for analyzing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies.”

To clearly document, define, map and analyze workflows and build models to drive process improvement and therefore business transformation, you’ll need to invest in a business process (BP) modeling solution.

Only then will you be able to determine where cross-departmental and intra-system process chains break down, as well as identify business practices susceptible to the greatest security, compliance, standards or other risks and where controls and audits are most needed to mitigate exposures.

Companies that maintain accurate BP models also are well-positioned to analyze and optimize end-to-end process threads that help accomplish such strategic business objectives as improving customer journeys and maximizing employee retention. You also can slice and dice models in multiple other ways, including to improve collaboration and efficiency.

Useful change only comes from evaluating process models, spotting sub-optimalities, and taking corrective actions. Business process modeling is also critical to data governance, helping organizations understand their data assets in the context of where their data is and how it’s used in various processes. Then you can drive data opportunities, like increasing revenue, and limit data risks, such as avoiding regulatory and compliance gaffes.

How to Do Business Process Modeling

Business process modeling software creates the documentation and graphical roadmap of how a business works today, detailing the tasks, responsible parties and data elements involved in processes and the interactions that occur across systems, procedures and organizational hierarchies. That knowledge, in turn, prepares the organization for tomorrow’s changes.

Effective BP technology will assist your business in documenting, managing and communicating your business processes in a structured manner that drives value and reduces risks.

It should enable you to:

  • Develop and capture multiple artefacts in a repository to support business-centric objectives
  • Support process improvement methodologies that boost critical capabilities
  • Identify gaps in process documentation to retain internal mastery over core activities
  • Reduce maintenance costs and increase employee access to critical knowledge
  • Incorporate any data from any location into business process models

In addition, a business process modeling solution should work in conjunction with the other data management domains (i.e., enterprise architecture, data modeling and data governance) to provide data clarity across all organizational roles and goals.

Data Governance, Data Modeling, Enterprise Architecture, Business Process - erwin EDGE

Business Process Modeling and Enterprise Data Management

Data isn’t just for “the data people.” To survive and thrive in the digital age, among the likes of Amazon, Airbnb, Netflix and Uber that have transformed their respective industries, organizations must extend the use, understanding and trust of their data everyday across every business function – from the C-level to the front line.

A common source of data leveraged by business process personnel, enterprise architects, data stewards and others encourages a greater understanding of how different line-of-business operations work together as a single unit. Links to data terms and categories contained within a centralized business glossary let enterprises eliminate ambiguity in process and policy procedure documents.

Integrated business models based on a sole source of truth also offer different views for different stakeholders based on their needs, while tight interconnection with enterprise architecture joins Process, Organization, Location, Data, Applications, and Technology (POLDAT) assets to explanatory models that support informed plans for change.

Seamless integration of business process models with enterprise architecture, data modeling and data governance reveals the interdependence between the workforce, the processes they perform, the actively governed assets they interact with and their importance to the business.

Then everyone is invested in and accountable for data, the fuel for the modern enterprise.

To learn more about business process modeling and its role within data-driven business transformation, click here.

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Five Steps to Digital Transformation

Digital Transformation

Digital transformation is ramping up in all industries. Facing regular market disruptions, and landscape-changing technological breakthroughs, modern businesses must be both malleable and willing to change.

To stay competitive, you must be agile.

Digital Transformation is Inevitable

Increasing numbers of organizations are undergoing a digital transformation. The tried-and-tested yet rigid methods of doing business are being replaced by newer, data-orientated approaches that require thorough but fast analysis.

Some businesses – like Amazon, Netflix and Uber – are leading this evolution. They all provide very different services, but at their core, they are technology focused.

And they’re reaping rewards for it too. Amazon is one of the most valuable businesses in the world, perhaps one of the first companies to reach a $1-trillion valuation.

It’s not too late to adopt digital transformation, but it is  too late to keep fighting against it. The tide of change has quickened, and stubborn businesses could be washed away.

But what’s the best way to get started?

Step One: Determine Your End Goal

Any form of change must start with the end in mind, as it’s impossible to make a transformation without understanding why and how.

Before you make a change, big or small, you need to ask yourself why are we doing this? What are the positives and negatives? And if there are negatives, what can we do to mitigate them?

To ensure a successful digital transformation, it’s important to plot your journey from the beginning through your end goal, understanding how one change or a whole series of changes will alter your business.

Business process modeling tools can help map your digital transformation journey.

Step Two: Get Some Strategic Support

For businesses of any size, transformational change can disrupt day-to-day operations. In most organizations, the expertise to manage a sizeable transformation program doesn’t exist, and from the outset, it can appear quite daunting.

If your goal is to increase profits, it can seem contradictory to pay for support to drive your business forward. However, a slow or incorrect transformational process can be costly in many ways. Therefore, investing in support can be one of the best decisions you make.

Effective strategic planning, rooted in enterprise architecture, can help identify gaps and potential oversights in your strategy. It can indicate where investment is needed and ensure transformative endeavors aren’t undermined by false-starts and U-turns.

Many businesses would benefit further by employing strategic consultants. As experts in their fields, strategic consultants know the right questions to ask to uncover the information you need to influence change.

Their experience can support your efforts by identifying and cataloging underlying components, providing input to the project plan and building the right systems to capture important data needed to meet the business’s transformation goals.

Step Three: Understand What You Have

Once you know where you want to go, it’s important to understand what you currently do. That might seem clear, but even the smallest organizations are underpinned by thousands of business processes.

Before you decide to change something, you need to understand everything about what you currently do, or else a change could have an unanticipated and negative impact.

Enterprise architecture will also benefit a business here, uncovering strategic improvement opportunities – valuable changes you might not have seen.

As third-parties, consultants can provide an impartial view, rather than letting historic or legacy decisions cloud future judgment.

Businesses will also benefit from data modeling. This is due to the exponential increase in the volume of data businesses have to manage – as well as the variety of disparate sources.

Data modeling will ensure data is accessible, understood and better prepared for analysis and the decision-making process.

Step Four: Collect Knowledge from Within

Your employees are a wealth of knowledge and ideas, so it’s important to involve them in the enterprise architecture process.

Consultants can facilitate a series of staff workshops to enable employee insights to be shared and then developed into real, actionable changes.

Step Five: Get Buy-in Across the Business

Once you’ve engaged with your staff to collect the knowledge they hold, make sure you don’t cut them off there. Business change is only successful if everyone understands what is happening and why, with continuous updates.

Ensure that you take your employees through the change process, making them  part of the digital transformation journey.

Evidence suggests that 70 percent of all organizational change efforts fail, with a primary reason being that executives don’t get enough buy-in for new initiatives and ideas.

By involving relevant stakeholders in the strategic planning process, you can mitigate this risk. Strategic planning tools that enable collaboration can achieve this. Thanks to technological advancements in the cloud, collaboration can even be effectively facilitated online.

Take your employees through your digital transformation journey, and you’ll find them celebrating with you when you arrive at your goal.

If you think now is the right time for your business to change, get in touch with us today.

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