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erwin Expert Blog Business Process

In Times of Rapid Change, Business Process Modeling Becomes a Critical Tool

With the help of business process modeling (BPM) organizations can visualize processes and all the associated information identifying the areas ripe for innovation, improvement or reorganization.

In the blink of an eye, COVID-19 has disrupted all industries and quickly accelerated their plans for digital transformation. As part of their transformations, businesses are moving quickly from on premise to the cloud and therefore need to create business process models available to everyone within the organization so they understand what data is tied to what applications and what processes are in place.

There’s a clear connection between business process modeling and digital transformation initiatives. With it, an organization can explore models to understand information assets within a business context, from internal operations to full customer experiences.

This practice identifies and drives digital transformation opportunities to increase revenue while limiting risks and avoiding regulatory and compliance gaffes.

Business Process Data Governance

Bringing IT and Business Together to Make More Informed Decisions

Developing a shared repository is key to aligning IT systems to accomplish business strategies, reducing the time it takes to make decisions and accelerating solution delivery.

It also serves to operationalize and govern mission-critical information by making it available to the wider enterprise at the right levels to identify synergies and ensure the appropriate collaboration.

One customer says his company realized early on that there’s a difference between business expertise and process expertise, and when you partner the two you really start to see the opportunities for success.

By bringing your business and IT together via BPM, you create a single point of truth within your organization — delivered to stakeholders within the context of their roles.

You then can understand where your data is, how you can find it, how you can monetize it, how you can report on it, and how you can visualize it. You are able to do it in an easy format that you can catalog, do mappings, lineage and focus on tying business and IT together to make more informed decisions.

BPM for Regulatory Compliance

Business process modeling is also critical for risk management and regulatory compliance. When thousands of employees need to know what compliance processes to follow, such as those associated with the European Union’s General Data Protection Regulation (GDPR), ensuring not only access to proper documentation but current, updated information is critical.

Industry and government regulations affect businesses that work in or do business with any number of industries or in specific geographies. Industry-specific regulations in areas like healthcare, pharmaceuticals and financial services have been in place for some time.

Now, broader mandates like GDPR and the California Consumer Privacy Act (CCPA) require businesses across industries to think about their compliance efforts. Business process modeling helps organizations prove what they are doing to meet compliance requirements and understand how changes to their processes impact compliance efforts (and vice versa).

This same customer says, “The biggest bang for the buck is having a single platform, a one-stop shop, for when you’re working with auditors.” You go to one place that is your source of truth: Here are processes; here’s how we have implemented these controls; here are the list of our controls and where they’re implemented in our business.”

He also notes that a single BPM platform “helps cut through a lot of questions and get right to the heart of the matter.” As a result, the company has had positive audit findings and results because they have a structure, a plan, and it’s easy to see the connection between how they’re ensuring their controls are adhered to and where those results are in their business processes.

Change Is Constant

Heraclitus, the Greek philosopher said, “The only constant in life is change.” This applies to business, as well. Today things are changing quite quickly. And with our current landscape, executives are not going to wait around for months as impact analyses are being formulated. They want actionable intelligence – fast.

For business process architects, being able to manage change and address key issues is what keeps the job function highly relevant to stakeholders. The key point is that useful change comes from routinely looking at process models and spotting a sub-optimality. Business process modeling supports many beneficial use cases and transformation projects used to empower employees and therefore better serve customers.

Organizational success depends on agility and adaptability in responding to change across the enterprise, both planned and unplanned. To be agile and responsive to changes in markets and consumer demands, you need a visual representation of what your business does and how it does it.

Companies that maintain accurate business process models also are well-positioned to analyze and optimize end-to-end process threads—lead-to-cash, problem-to-resolution or hire-to-retire, for example—that contribute to strategic business objectives, such as improving customer journeys or maximizing employee retention.

They also can slice and dice their models in multiple other ways, such as by functional hierarchies to understand what business groups organize or participate in processes as a step in driving better collaboration or greater efficiencies.

erwin Evolve enables communication and collaboration across the enterprise with reliable tools that make it possible to quickly and accurately gather information, make decisions, and then ensure consistent standards, policies and processes are established and available for consumption internally and externally as required.

Try erwin Evolve for yourself in a no-cost, risk-free trial.

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erwin Expert Blog Data Intelligence Data Modeling Business Process Enterprise Architecture

Benefits of Enterprise Modeling and Data Intelligence Solutions

Users discuss how they are putting erwin’s data modeling, enterprise architecture, business process modeling, and data intelligences solutions to work

IT Central Station members using erwin solutions are realizing the benefits of enterprise modeling and data intelligence. This article highlights some specific use cases and the results they’re experiencing within the organizations.

Enterprise Architecture & Business Process Modeling with erwin Evolve

An enterprise architect uses erwin Evolve at an aerospace/defense firm with more than 10,000 employees. His team is “doing business process modeling and high-level strategic modeling with its capabilities.” Others in his company are using it for IT infrastructure, such as aligning requirements to system solutions.

For Matthieu G., a senior business process management architect at a pharma/biotech company with more than 5,000 employees, erwin Evolve was useful for enterprise architecture reference. As he put it, “We are describing our business process and we are trying to describe our data catalog. We are describing our complete applications assets, and we are interfacing to the CMDB of our providers.”

His team also is using the software to manage roadmaps in their main transformation programs. He added, “We have also linked it to our documentation repository, so we have a description of our data documents.” They have documented 200 business processes in this way. In particular, the tool helped them to design their qualification review, which is necessary in a pharmaceutical business.

erwin Evolve users are experiencing numerous benefits. According to the aerospace enterprise architect, “It’s helped us advance in our capabilities to perform model-based systems engineering, and also model-based enterprise architecture.”

This matters because, as he said, “By placing the data and the metadata into a model, which is what the tool does, you gain the abilities for linkages between different objects in the model, linkages that you cannot get on paper or with Visio or PowerPoint.” That is a huge differentiator for this user.

This user also noted, “I use the automatic diagramming features a lot. When one of erwin’s company reps showed that to me a couple of years ago, I was stunned. That saves hours of work in diagramming. That capability is something I have not seen in other suppliers’ tools.”

He further explained “that really helps too with when your data is up to date. The tool will then automatically generate the updated diagram based on the data, so you know it’s always the most current version. You can’t do that in things like Visio and PowerPoint. They’re static snapshots of a diagram at some point in time. This is live and dynamic.”

erwin DM

Data Modeling with erwin Data Modeler

George H., a technology manager, uses erwin Data Modeler (erwin DM) at a pharma/biotech company with more than 10,000 employees for their enterprise data warehouse.

He elaborated by saying, “We have an enterprise model being maintained and we have about 11 business-capability models being maintained. Examples of business capabilities would be finance, human resources, supply-chain, sales and marketing, and procurement. We maintain business domain models in addition to the enterprise model.”

Roshan H., an EDW architect/data modeler who uses erwin DM at Royal Bank of Canada, works on diverse platforms, including Microsoft SQL Server, Oracle, DB2, Teradata and NoSQL. After gathering requirements and mapping data on Excel, they start building the conceptual model and then the logical model with erwin DM.

He said, “When we have these data models built in the erwin DM, we generate the PDF data model diagrams and take it to the team (DBA, BSAs, QA and others) to explain the model diagram. Once everything is reviewed, then we go on to discuss the physical data model.”

“We use erwin DM to do all of the levels of analysis that a data architect does,” said Sharon A., a senior manager, data governance at an insurance company with over 500 employees. She added, “erwin DM does conceptual, logical and physical database or data structure capture and design, and creates a library of such things.

We do conceptual data modeling, which is very high-level and doesn’t have columns and tables. It’s more concepts that the business described to us in words. We can then use the graphic interface to create boxes that contain descriptions of things and connect things together. It helps us to do a scope statement at the beginning of a project to corral what the area is that the data is going to be using.”

Data Governance with erwin Data Intelligence

IT Central Station members are seeing benefits from using erwin Data Intelligence (erwin DI) for data governance use cases. For Rick D., a data architect at NAMM, a small healthcare company, erwin DI “enabled us to centralize a tremendous amount of data into a common standard, and uniform reporting has decreased report requests.”

As a medical company, they receive data from 17 different health plans. Before adopting erwin DI, they didn’t have a centralized data dictionary of their data. The benefit of data governance, as he saw it, was that “everybody in our organization knows what we are talking about. Whether it is an institutional claim, a professional claim, Blue Cross or Blue Shield, health plan payer, group titles, names, etc.”

A solution architect at a pharma/biotech company with more than 10,000 employees used erwin DI for metadata management, versioning of metadata and metadata mappings and automation. In his experience, applying governance to metadata and creating mappings has helped different stakeholders gain a good understanding of the data they use to do their work.

Sharon A. had a comparable use case. She said, “You can map the business understanding in your glossary back to your physical so you can see it both ways. With erwin DI, I can have a full library of physical data there or logical data sets, publish it out through the portal, and then the business can do self-service. The DBAs can use it for all different types of value-add from their side of the house. They have the ability to see particular aspects, such as RPII, and there are some neat reports which show that. They are able manage who can look at these different pieces of information.”

For more real erwin user experiences, visit IT Central Station.

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Data Intelligence Data Governance erwin Expert Blog

Do I Need a Data Catalog?

If you’re serious about a data-driven strategy, you’re going to need a data catalog.

Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner.

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer.

It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., legacy systems, data warehouses, flat files stored on individual desktops and laptops, and modern, cloud-based repositories.)

These fragmented data environments make data governance a challenge since business stakeholders, data analysts and other users are unable to discover data or run queries across an entire data set. This also diminishes the value of data as an asset.

Data catalogs combine physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals in certain circumstances.

You also can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

The data catalog is a searchable asset that enables all data – including even formerly siloed tribal knowledge – to be cataloged and more quickly exposed to users for analysis.

Organizations with particularly deep data stores might need a data catalog with advanced capabilities, such as automated metadata harvesting to speed up the data preparation process.

For example, before users can effectively and meaningfully engage with robust business intelligence (BI) platforms, they must have a way to ensure that the most relevant, important and valuable data set are included in analysis.

The most optimal and streamlined way to achieve this is by using a data catalog, which can provide a first stop for users ahead of working in BI platforms.

As a collective intelligent asset, a data catalog should include capabilities for collecting and continually enriching or curating the metadata associated with each data asset to make them easier to identify, evaluate and use properly.

Data Catalog Benefits

Three Types of Metadata in a Data Catalog

A data catalog uses metadata, data that describes or summarizes data, to create an informative and searchable inventory of all data assets in an organization.

These assets can include but are not limited to structured data, unstructured data (including documents, web pages, email, social media content, mobile data, images, audio, video and reports) and query results, etc. The metadata provides information about the asset that makes it easier to locate, understand and evaluate.

For example, Amazon handles millions of different products, and yet we, as consumers, can find almost anything about everything very quickly.

Beyond Amazon’s advanced search capabilities, the company also provides detailed information about each product, the seller’s information, shipping times, reviews, and a list of companion products. Sales are measured down to a zip code territory level across product categories.

Another classic example is the online or card catalog at a library. Each card or listing contains information about a book or publication (e.g., title, author, subject, publication date, edition, location) that makes the publication easier for a reader to find and to evaluate.

There are many types of metadata, but a data catalog deals primarily with three: technical metadata, operational or “process” metadata, and business metadata.

Technical Metadata

Technical metadata describes how the data is organized, stored, its transformation and lineage. It is structural and describes data objects such as tables, columns, rows, indexes and connections.

This aspect of the metadata guides data experts on how to work with the data (e.g. for analysis and integration purposes).

Operational Metadata

Operational metadata describes systems that process data, the applications in those systems, and the rules in those applications. This is also called “process” metadata that describes the data asset’s creation, when, how and by whom it has been accessed, used, updated or changed.

Operational metadata provides information about the asset’s history and lineage, which can help an analyst decide if the asset is recent enough for the task at hand, if it comes from a reliable source, if it has been updated by trustworthy individuals, and so on.

As illustrated above, a data catalog is essential to business users because it synthesizes all the details about an organization’s data assets across multiple data sources. It organizes them into a simple, easy- to-digest format and then publishes them to data communities for knowledge-sharing and collaboration.

Business Metadata

Business metadata is sometimes referred to as external metadata attributed to the business aspects of a data asset. It defines the functionality of the data captured, definition of the data, definition of the elements, and definition of how the data is used within the business.

This is the area which binds all users together in terms of consistency and usage of catalogued data asset.

Tools should be provided that enable data experts to explore the data catalogs, curate and enrich the metadata with tags, associations, ratings, annotations, and any other information and context that helps users find data faster and use it with confidence.

Why You Need a Data Catalog – Three Business Benefits of Data Catalogs

When data professionals can help themselves to the data they need—without IT intervention and having to rely on finding experts or colleagues for advice, limiting themselves to only the assets they know about, and having to worry about governance and compliance—the entire organization benefits.

Catalog critical systems and data elements plus enable the calculation and evaluation of key performance measures. It is also important to understand data linage and be able to analyze the impacts to critical systems and essential business processes if a change occurs.

  1. Makes data accessible and usable, reducing operational costs while increasing time to value

Open your organization’s data door, making it easier to access, search and understand information assets. A data catalog is the core of data analysis for decision-making, so automating its curation and access with the associated business context will enable stakeholders to spend more time analyzing it for meaningful insights they can put into action.

Data asset need to be properly scanned, documented, tagged and annotated with their definitions, ownership, lineage and usage. Automating the cataloging of data assets saves initial development time and streamlines its ongoing maintenance and governance.

Automating the curation of data assets also accelerates the time to value for analytics/insights reporting and significantly reduces operational costs.

  1. Ensures regulatory compliance

Regulations like the California Consumer Privacy Act (CCPA ) and the European Union’s General Data Protection Regulation (GDPR) require organizations to know where all their customer, prospect and employee data resides to ensure its security and privacy.

A fine for noncompliance or reputational damage are the last things you need to worry about, so using a data catalog centralizes data management and the associated usage policies and guardrails.

See a Data Catalog in Action

The erwin Data Intelligence Suite (erwin DI) provides data catalog and data literacy capabilities with built-in automation so you can accomplish all the above and much more.

Request your own demo of erwin DI.

Data Intelligence for Data Automation

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erwin Expert Blog Enterprise Architecture

Enterprise Architecture vs. Technical Architecture

The most straightforward way to convey the difference between technical architecture and enterprise architecture (EA) is by looking at the scope and focus of each.

As the name suggests, technical architects are more concerned with the technicalities and the specifics of a particular technology than with technology’s place in the enterprise.

That’s not to say that they operate without the enterprise’s overall strategy in mind. They do – or should ­– but the direction for said strategy typically comes from elsewhere. Most significantly – from an enterprise architect.

In this post:

Enterprise architecture review

What Is a Technical Architect?

A technical architect works closely with development teams in a supervisory capacity – providing leadership and guidance during the project lifecycle.

They often work under more specific titles, reflecting the technology they specialize in – e.g., “Java architect” or “Python architect.”

Typically, their operations revolve around technical services and in the context of the project lifecycle – enabling technology.

It’s a role that requires good communication and relationship management skills, as well as the ability to anticipate problems, manage their time, and operate under pressure.

The Difference Between Technical Architecture and Enterprise Architecture

We previously have discussed the difference between data architecture and EA plus the difference between solutions architecture and EA.

In the latter, we described enterprise architects as having a “holistic view” of the organization, mostly focusing on things from a high-level, strategic point of view.

Although technology is something that enterprise architects are concerned with, they aren’t expected to have a deep, ground-level understanding of the tech itself.

Their broad scope and holistic view of the enterprise does not allow for this.

Technical architecture can be seen as the antithesis to this. Instead of seeing the organization from a high-level, strategic point of view, technical architects operate amongst the weeds.

They have a high focus on technology, and a low focus on how that technology fits in with the enterprise’s overall strategy.

Using an orchestra as an analogy, enterprise architects would assume the role of a conductor who is less concerned with how any individual instrument operates but requires they work together cohesively to complete the performance.

While enterprise architects have a high strategy focus, they are less detail orientated., Technical architects operate in the reverse, with solution architects somewhere in the middle.

Enterprise Architecture vs. Technical Architecture – Which One Do I Need?

Well? … Both.

It’s a straightforward and somewhat vague answer. But it’s one that needs to be given, because we’re asking the wrong question.

In a world where organizations are increasingly data-driven, any ambition to scale will inevitably scale with the complexities of the systems involved too.

With this considered, we should abandon the binary question, in favor of asking “when”  instead of “which.”

When do I need enterprise architecture? When do I need technical architecture?

The answer to both is “sooner, rather than later.”

With regard to enterprise architecture, many organizations  already are doing enterprise architecture before they have a formally recognized enterprise architecture initiative or enterprise architecture tool.

We consider these efforts “low-maturity” enterprise architecture.

But doing enterprise architecture this way can cause bottlenecks as an organization’s enterprise architecture scales. Additionally, this sort of enterprise architecture is vulnerable in its dependency on individuals.

Without a formalized approach to enterprise architecture, the whole enterprise architecture could collapse if the person in charge of the EA were to leave without contingency plans in place.

You may already be at and the point of experiencing bottlenecks. If so, you should consider formalizing your enterprise architecture approach and investing in an enterprise architecture management suite (EAMS) now.

This 2020 Forrester Report on EAMS is a great place to start your research.

The need for technical architecture implies an organization already has a complex enterprise architecture and some degree of formalization for managing it.

This, in turn, implies an organization is large and therefore has more complicated product lifecycles and higher stakes implementation procedures for new tech.

In either case, managing an organization’s enterprise architecture through manual processes and repurposed systems – some organizations attempt to make do with managing their EA via PowerPoint slides and sticky notes – is not the recommended approach.

For a scalable, manageable and efficient approach to enterprise architecture, organizations should adopt a dedicated enterprise architecture tool.

erwin Evolve is one such tool that also supports business process modeling use cases.

erwin Evolve has collaborative features at its core, meaning organizational silos that had once kept EA in an ivory tower are broken down, and the holistic view of enterprise architects is enhanced.

With erwin Evolve, users enjoy the following benefits:

  • Creation and visualization of complex models
  • Powerful analytic tools to better understand an organization’s architecture – such as impact analysis
  • Documentation and knowledge retention for better business continuity
  • Democratization of the decision-making process
  • Greater agility and efficiency in implementations and dealing with disruption
  • Lower risks and costs driven by an enhanced ability to identify redundant systems and processes

Organizations can try erwin Evolve for free and keep any content you produce should you decide to buy.

For more information on enterprise architecture, click here to get the erwin experts’ definitive guide to enterprise architecture – 100% free of charge.

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erwin Expert Blog Data Intelligence Data Governance

Overcoming the 80/20 Rule – Finding More Time with Data Intelligence

The 80/20 rule is well known. It describes an unfortunate reality for many data stewards, who spend 80 percent of their time finding, cleaning and reorganizing huge amounts of data, and only 20 percent of their time on actual data analysis.

That’s a lot wasted of time.

Earlier this year, erwin released its 2020 State of Data Governance and Automation (DGA) report. About 70 percent of the DGA report respondents – a combination of roles from data architects to executive managers – say they spend an average of 10 or more hours per week on data-related activities.

COVID-19 has changed the way we work – essentially overnight – and may change how companies work moving forward. Companies like Twitter, Shopify and Box have announced that they are moving to a permanent work-from-home status as their new normal.

For much of our time as data stewards, collecting, revising and building consensus around our metadata has meant that we need to balance find time on multiple calendars against multiple competing priorities so that we can pull the appropriate data stakeholders into a room to discuss term definitions, the rules for measuring “clean” data, and identifying processes and applications that use the data.

Overcoming the 80/20 Rule - Analyzing Data

This style of data governance most often presents us with eight one-hour opportunities per day (40 one-hour opportunities per week) to meet.

As the 80/20 rule suggests, getting through hundreds, or perhaps thousands of individual business terms using this one-hour meeting model can take … a … long … time.

Now that pulling stakeholders into a room has been disrupted …  what if we could use this as 40 opportunities to update the metadata PER DAY?

What if we could buck the trend, and overcome the 80/20 rule?

Overcoming the 80/20 Rule with Micro Governance for Metadata

Micro governance is a strategy that leverages the native functionality around workflows.

erwin Data Intelligence (DI) offers Workflow Manager that creates a persistent, reusable role-based workflow such that edits to the metadata for any term can move from, for example, draft to under review to approved to published.

Using a defined workflow, it can eliminate the need for hour-long meetings with multiple stakeholders in a room. Now users can suggest edits, review changes, and approve changes on their own schedule! Using micro governance these steps should take less than 10 minutes per term:

  • Log on the DI Suite
  • Open your work queue to see items requiring your attention
  • Review and/or approve changes
  • Log out

That’s it!

And as a bonus, where stakeholders may need to discuss the edits to achieve consensus, the Collaboration Center within the Business Glossary Manager facilitates conversations between stakeholders that persistent and attached directly to the business term. No more searching through months of email conversations or forgetting to cc a key stakeholder.

Using the DI Suite Workflow Manager and the Collaboration Center, and assuming an 8-hour workday, we should each have 48 opportunities for 10 minutes of micro-governance stewardship each day.

A Culture of Micro Governance

In these days when we are all working at home, and face-to-face meetings are all but impossible, we should see this time as an opportunity to develop a culture of micro governance around our metadata.

This new way of thinking and acting will help us continuously improve our transparency and semantic understanding of our data while staying connected and collaborating with each other.

When we finally get back into the office, the micro governance ethos we’ve built while at home will help make our data governance programs more flexible, responsive and agile. And ultimately, we’ll take up less of our colleagues’ precious time.

Request a free demo of erwin DI.

Data Intelligence for Data Automation

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Enterprise Architecture erwin Expert Blog

The Difference Between Enterprise Architecture and Solutions Architecture

Despite the similarities in name, there are a number of key differences between enterprise architecture and solutions architecture.

Much like the differences between enterprise architecture (EA) and data architecture, EA’s holistic view of the enterprise will often see enterprise and solution architects collaborate.

And as with data architecture, a solution architect’s focus is narrower.

In this post:

What Is a Solutions Architect?

Solutions architecture is about solving problems. It describes the process of orchestrating software engineering to address an organization’s needs.

Typically, a solution architect’s responsibilities cover:

  • Assessing and understanding an organization’s technological assets
  • Finding a solution to address a problem
  • Defining and denoting the critical technologies for the organization’s operations
  • Understanding the impact of time on the current technologies in place, including establishing what can be scaled and what must be replaced/upgraded
  • Designing prototype solutions
  • Assessing and selecting new technologies

Their technical skills typically include software engineering and design, DevOps, business analysis and increasingly, cloud architecture.

What Is an Enterprise Architect?

Broadly speaking, enterprise architecture is a strategic planning initiative.

Enterprise architects are concerned with how they can reduce costs, eliminate redundancies in technology and processes, and prepare for, mitigate and manage the impact of change.

To operate effectively, enterprise architects must have a solid understanding of the organizations they work with/for.

Such an understanding has its advantages, but it also means that there isn’t the scope to be concerned with the more “technical” side of an organization’s architecture.

Typically, an enterprise architect’s responsibilities cover:

  • Understanding and developing the whole enterprise architecture
  • Evaluating risks and the impacts of change and creating a roadmap with such evaluations considered
  • Ensuring alignment between the business and IT through the organization’s enterprise architecture
  • Advising decision-makers and business leaders from an organization-wide, holistic perspective of the enterprise

From this perspective, solution architecture’s value to enterprise architecture becomes even more clear. Where an enterprise architect is concerned with the EA’s current state, and the strategy to reach the desired future-state, solutions architects act on that strategic direction.

Enterprise Architecture Tools

Enterprise Architects vs. Solutions Architects

Perhaps it’s misleading to use “versus” to describe the difference between enterprise architecture and solutions architecture. They are very much collaborators in the organization and should not be looked at as competitive in terms of which provides more value.

A better way of highlighting the difference between the two is through their focus on strategy vs. technology.

A focus on strategy implies a broad understanding of the mechanics of any given technology. This is because there is a lot more to strategy than just the technology needed to implement it. A skewed focus on technology would mean that the processes, people and other variables required to inform strategy are ignored.

Conversely, a focus on technology is necessary to ensure implementations and operations can run smoothly. By its nature, it is more “in the weeds” and so the necessary holistic perspective of the organization can be harder to understand and/or account for.

With their holistic view of the organization, enterprise architects take on the strategy. They then use their strategic planning perspective to inform and delegate to solutions architects.

In the same vain, a technical architect has a low strategic focus and a high technological focus.

Bottom line,  an enterprise architect’s strategic focus is high and their technology focus is low; technology architects operate in the reverse; and solution architects bridge the two.

The Imperative for Enterprise Architecture and Solutions Architecture

With the quickening pace of digital transformation and the increased acceleration owed to the Covid-19 crisis, enterprise architects and solution architects are becoming increasingly relevant.

“Enterprise architect” was named the top tech job in the UK for 2020 and as this article implies, solution architects should stand to benefit, as well.

However, simply hiring enterprise architects and solution architects isn’t enough. Enterprise architecture in particular has been blighted by its perception as a role operating in an ivory tower, disconnected from the wider business.

Considering IT and business alignment is a core tenant of an enterprise architect’s responsibilities, this is obviously counter-productive.

For many organizations, shaking this perception will require a change in how enterprise architecture is done. Organizations need a definable, measurable and collaborative approach to enterprise architecture to make the most out of its vast potential.

This means moving away from low maturity examples of enterprise architecture that are managed through a hodgepodge of repurposed tools, and decentralized notes.

erwin is helping organizations mature their EA with erwin Evolve. With Evolve, organizations can collaborate within a purpose-built enterprise architecture tool for both greater consistency and involvement from the wider business.

As part of the wider erwin Enterprise Data Governance Experience (EDGE), erwin Evolve lets organizations synergize their enterprise architecture with their data governance and management strategies.

This means that efforts to manage the enterprise architecture include a data inclusive perspective. And considering data’s value as an asset, this perspective is vital.

It means an organization can get a clear and full picture of the whole data lifecycle in relation to the systems and broader context it exists in, so that the intersections between data and the organization’s assets is clearer.

Organizations can try erwin Evolve for free and keep any content you produce should you decide to buy.

For more information on enterprise architecture, click here to get the erwin Experts’ definitive guide to enterprise architecture – 100% free of charge.

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erwin Expert Blog Data Intelligence

The Top Five Data Intelligence Benefits

Data intelligence benefits data-driven organizations immensely. Primarily, it’s about helping organizations make more intelligent decisions based on their data.

It does this by affording organizations greater visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it’s integrated with and used by key applications.

For more context, see: What is Data Intelligence?

The Top 5 Data Intelligence Benefits

Through a better understanding of what data an organization has available – including its lineage, associated metadata and access permissions – organization’s data-driven decisions are afforded more context and ultimately, a greater likelihood of successful implementation.

Considering this, the benefits of data intelligence are huge, and include:

1. Improved consumer profiling and segmentation

Customer profiling and segmentation enables businesses and marketers to better understand their target consumer and group them together according to common characteristics and behavior.

Businesses will be able to cluster and classify consumers according to demographics, purchasing behavior, experience with product and services, and so much more. Having a holistic view of the customers’ preferences, transactions, and purchasing behavior enables businesses to make better decisions regarding the products and services they provide. Great examples are BMW Mini, Comfort Keepers, and Teleflora.

2. A greater understanding of company investments

Data intelligence is able to provide business data with a greater context in regard to the progress and effectiveness of their investments. Businesses that partner with IT companies can develop data intelligence that is tailored to monitoring and evaluating their current investments, as well as forecast potential future investments.

If the current investments that a business has is not as effective, then data intelligence tools can provide guidance on the best avenues to invest in. Big IT companies even have off-the-shelf data analytics software ready to be configured by a company to their needs.

3. The ability to apply real-time data in marketing strategies

With real-time analytics, businesses are able to utilize information such as regional or local sales patterns, inventory level summaries, local event trends, sales history, or seasonal factors in reviving marketing models and strategies and directing them to better serve their customers.

Real-time data analytics can be used by businesses to better meet customer needs as it arises and improve customer satisfaction. Dickey’s BBQ Pit was able to utilize data analytics across all its stores and, using the resulting information, adjust their promotions strategy from weekly to around every 12 to 24 hours.

4. A greater opportunity to enhance logistical and operational planning

Data intelligence can also enable businesses to enhance their operational and logistical planning. Insights on things such as delivery times, optimal event dates, potential external factors, potential route roadblocks, and optimal warehousing locations can help optimize operations and logistics.

Data intelligence can take raw, untimely, and incomprehensible data and present it in an aggregated, condensed, digestible, and usable information. UPS employed the Orion route optimization system and was able to cut down 364 million miles from its routes globally.

5. An enhanced capacity to improve customer experience

To keep pace with technology, businesses have been employing more tools and methods that incorporate modern technology like, Machine Learning, and the Internet of Things(IoT) to enhance the consumer experience.

Information derived from tools like customer profiling analyses is able to provide insight into consumer purchasing behavior, which the business then uses to tailor their products and services to match the needs of their target consumers. Businesses are also able to use such information to provide customers with user-centric customer experience.

digital transformation data intelligence

Transforming Industries with Data Intelligence

With big data, and tools such as Artificial Intelligence, Machine Learning, and Data Mining, organizations collect and analyze large amounts of data reliably and more efficiently. From Amazon to Airbnb, over the last decade, we’ve seen orgnaizations that take advantage of the aforementioned data intelligence benefits to manage large data volumes, rise to the pole position in their industry.

Now, in 2020, the benefits of data intelligence are enjoyed by organizations from a plethora of different markets and industries.

Data intelligence transforms the way industries operate by enabling businesses to hasten the process of analyzing and understanding the derived information with its more understandable models and aggregated trends.

Here’s how data intelligence is benefiting some of the most common industries:

Travel

The travel industry has found enhanced quality and range of products and services to provide travelers, as well as optimization of travel pricing strategies for future travel offerings.

Businesses in the travel industry can analyze historical trends on travel peak travel seasons and customer Key Performance Indicators (KPI) and can adjust services, amenities, and packages to match customer needs.

Education

Educators can provide a more valuable learning experience and environment for students. With the use of data intelligence tools, educational institutes can provide teachers with a more holistic view of a student’s academic performance.

Teachers can spot avenues for academic improvement, provide their students with support in aspects that need their help.

Healthcare

Several hospitals have also employed data intelligence tools in their services and operational processes. These hospitals are making use of dashboards that provide summary information on hospital patient trends, treatment costs, and waiting times.

Aside from these, these data intelligence tools also provide healthcare institutions with an encompassing view of the hospital and care critical data that hospitals can use to improve the quality and level of service and increase their economic efficiency.

Retail

The retail industry has also employed data intelligence in developing tools to better forecast and plan according to supply and demand trends and consumer Key Performance Indicators (KPI).

Businesses, both small and large, have made use of dashboards to monitor and illustrate transaction trends and product consumption rates. Tools such as these dashboards provide insight into customer purchasing patterns and transaction value that businesses such as Teleflora are leveraging to provide better products and services.

Data Intelligence Trends

With its rate of success evident among many of the most successful organizations in history, data intelligence is clearly no fad. Therefore, it’s important to keep an eye on both the current and upcoming data intelligence trends:

Real-time enterprise is the market.

Businesses, small and big, will be employing real-time data analytics and data-driven products and services as it will be what consumers will demand from businesses going forward.

Expanding big data.

Not moving from big data but instead expanding big data and incorporating more multifaceted data and data analytic methods and tools for more well-rounded insights and information.

Graph analytics and associative technology for better results.

This is where businesses and IT companies move forward with using natural associations within the data and use associative technology to derive better data for decision making.

DataOps and self-service.

DataOps will make business data processes more efficient and agile. This will make the business’s customer engagement and communication able to provide self-service interactions in their transactions and services.

Data literacy as a service.

Even more, businesses will be integrating data intelligence, hence the increasing demand for the skills and experienced dedicated development teams. Data literacy and data intelligence will further become an in-demand service.

Expanding search to multiform interaction.

Simple searches will be expanded to incorporate multifaceted search technology, from analyzing human expressions to transaction pattern analysis, and provide more robust search capabilities.

Ethical computing becomes crucial.

As technology becomes more ingrained in our day-to-day activities and consumes even more personal data, ethics and responsible computing will become essential in safeguarding consumer privacy and rights.

Incorporating blockchain technology into more industries.

Blockchain enables more secure and complex transaction record-keeping for businesses. More businesses employing data intelligence will be incorporating blockchain to support its processes.

Data quality management.

As exponential amounts of data will be consumed and processed, quality data governance and management will be essential. Overseeing the data collection and processing and implementing governance of these is important.

Enhanced data discovery and visualization.

With improved tools to process large volumes of data, numerous tools geared towards transforming this data into understandable and digestible information will be highly coveted.

As a Data-Driven Global Society, We Must Adapt

Data is what drives all of our actions, from individually trying to decide what to eat in the morning to entire global enterprises deciding what the next big global product will be. How we collect, process, and use the data for is what differs. Businesses will eventually move towards data-driven strategies and business models and with it the increased partnership with IT companies or hiring in-house dedicated development teams.

With a global market at hand, businesses can also employ a remote team and be assured that the same quality work will be provided. How businesses go about it may be diverse, but the direction is towards data-driven enterprises providing consumer-centric products and services.

This is a guest post from IT companies in Ukraine, a Ukraine-based software development company that provides top-level outsourcing services. 

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erwin Expert Blog

erwin and Snowflake Partnership: Helping Our Customers Manage and Govern the Entire Data Lifecycle

In my role as chief sales officer, I am fortunate to spend my time with the industry’s most passionate and committed customers. In these highly competitive enterprises and in the new post-COVID era of business, erwin’s customers are laser-focused on helping their businesses enhance their operations through the application of data truths: insights that help them improve everything — from how they serve their customers to increasing their competitive edge to delivering new products and services to meet the demand of new digital paradigms.

That’s why I’m so excited about our announcement about our new partnership with Snowflake. Our customers are in search of creative and sustainable ways to increase their speed to insights for digital transformation, infrastructure modernization and cloud migration and many of them are looking to implement the Snowflake Cloud Data Platform.

It’s designed with a patented new architecture to be the centerpiece for data pipelines, data warehousing, data lakes, data application development, and for building data exchanges to easily and securely share governed data.

With our new partnership, we can now help our customers manage and govern the entire Snowflake data lifecycle, speed transformation of legacy systems to Snowflake and automatically ingest, catalog and govern the data in these scalable, high-performance cloud data stores.

The native erwin DM integration lets customers automate the creation of Snowflake-specific data models; forward-engineer or generate code for Snowflake database schema; reverse-engineer existing Snowflake schema into erwin models; and compare, analyze and synchronize Snowflake models with the databases they represent.

The erwin Data Connector for Snowflake automatically scans and ingests metadata from Snowflake platforms into erwin DI, enabling data mapping to and from Snowflake databases to generate data movement code, lineage and impact analysis. And because erwin DM and erwin DI are integrated, there’s a complete picture of physical, semantic and business metadata in every Snowflake instance, and the creation and association of terms within the business glossary can be accelerated.

Sounds like a match made in heaven? Well, we think so. Let me know your thoughts on the new erwin/Snowflake partnership. Drop me a line.

erwin Rapid Response Resource Center (ERRRC)

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Enterprise Architecture erwin Expert Blog

Using Enterprise Architecture for Integration After Mergers and Acquisitions

Because of its holistic view of an organization, enterprise architecture and mergers & acquisitions (M&A) go hand-in-hand.

M&A activity, despite or in light of COVID-19, are on an upswing. The Financial Times reported Google, Amazon, Apple, Facebook and Microsoft have made 19 deals so far this year, according to Refinitiv, the London-based global provider of financial market data. This represents the fastest pace of acquisitions and strategic investments since 2015.

Let’s face it, company mergers, even once approved, can be daunting affairs. Depending on the size of the businesses involved, hundreds of systems and processes need to be accounted for, which can be difficult and often impossible to do in advance.

Following these transactions, businesses typically find themselves with a plethora of duplicate applications and business capabilities that eat into overhead and complicate inter-departmental alignment.

These drawbacks mean businesses have to ensure their systems are fully documented and rationalized. This way the organization can comb through its inventory and make more informed decisions on which systems can and should be cut or phased out, so it can operate closer to peak efficiency and deliver the roadmap to enable the necessary change.

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Enterprise Architecture Needs a Seat at the Table

IT professionals have the inside track about the connection that already exists across applications and data – and they’ll be the ones tasked with carrying out whatever technical requirements are in order post-acquisition.

But despite this, they’re rarely part of M&A tech strategy discussions and the synergy between enterprise architecture and mergers & acquisitions is overlooked. That should change.

With IT leaders involved from the start, they can work with the CFO and COO teams on assessing systems and providing advice on costs that might not otherwise be fully accounted for, such as systems and data integration.

Additionally, by leveraging mergers and acquisitions tools in the beginning, IT can provide a collaborative platform for business and technical stakeholders to get a complete view of their data and quickly visualize and assess what’s in place across companies, as well as what integrations, overlaps or other complexities exist.

This is why enterprise architecture for mergers and acquisitions is essential.

EA helps organizational alignment, providing a business-outcome perspective for IT and guiding transformation. It also helps a business define strategy and models, improving interdepartmental cohesion and communication.

Enterprise Architecture roadmaps can also be leveraged to provide a common focus throughout the company, and if existing roadmaps are in place, they can be modified to fit the new landscape.

EA aids in rooting out duplications in processes and operations, making the business more cost efficient on-the-whole.

Two Approaches to Enterprise Architecture

The Makeshift Approach

The first approach is more common in businesses with either no or a low-maturity enterprise architecture initiative. Smaller businesses often start out with this approach, as their limited operations and systems aren’t enough to justify real EA investment. Instead, businesses opt to repurpose tools they already have, such as the Microsoft Office Suite.

This comes with its advantages that mainly play out on a short-term basis, with the disadvantages only becoming apparent as the EA develops. For a start, the learning curve is typically smaller, as many people are already familiar with software, and the cost per license is relatively low when compared with built-for-purpose EA tools.

These short-term advantages will be eclipsed overtime as the organization’s EA grows. The adhoc Office tools approach to EA requires juggling a number of applications and formats that can stifle effectiveness.

Not only do the operations and systems become too numbered to manage this way, the disparity between formats prevents deep analysis. It also creates more work for the enterprise architect, as the disparate parts of the Office tools must be maintained separately when changes are made, to make sure everything is up to date.

This method also increases the likelihood that data is overlooked as key information is siloed, and it isn’t always clear which data set is behind any given door, disrupting efficiency and time to market.

It isn’t just data that siloed, though. The Office tools approach can isolate the EA department itself, from the wider business as the aforementioned disparities owed to the mis-matching formats can make collaborating with the wider business more difficult.

The Dedicated Approach

As an organization’s enterprise architecture grows, investing in dedicated EA tools becomes a necessity, making the transition just a matter of timing.

With a dedicated enterprise architecture tool, EA management is much easier. The data is all stored in one place, allowing for faster, deeper and more comprehensive analysis and comparison.

See also: Getting Started with Enterprise Architecture Tools

Collaboration also benefits from this approach, as having everything housed under one roof makes it far easier to share with stakeholders, decision-makers, C-level executives and other relevant parties.

Benefits of Enterprise Architecture for Mergers & Acquisitions

While organizational mergers can be fraught with many challenges. they don’t have to be so hard.

Enterprise architecture is essential to successful M&A. EA helps document and manage this complexity, turning all this data into meaningful insights.

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.

Roadmaps can be used to provide a common focus throughout the new company, and if existing roadmaps are in place, they can be modified to fit the new landscape.

erwin Evolve is a full-featured, configurable set of enterprise architecture and business process modeling and analysis tools. Use erwin Evolve to effectively tame complexity, manage change, and increase operational efficiency.

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erwin Expert Blog

Introducing erwin Insights 2020: Call for Proposals & Engagement

We know these have been unprecedented and challenging times.

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

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

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

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

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

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

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

Click here to submit your ideas.