Tag: digital transformation

Categories
erwin Expert Blog Data Modeling

Integrating SQL and NoSQL into Data Modeling for Greater Business Value: The Latest Release of erwin Data Modeler

SQL or NoSQL words written on white board, Big data concept

Due to the prevalence of internal and external market disruptors, many organizations are aligning their digital transformation and cloud migration efforts with other strategic requirements (e.g., compliance with the General Data Protection Regulation).

Accelerating the retrieval and analysis of data —so much of it unstructured—is vital to becoming a data-driven business that can effectively respond in real time to customers, partners, suppliers and other parties, and profit from these efforts. But even though speed is critical, businesses must take the time to model and document new applications for compliance and transparency.

For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. It facilitates communication between the business and system developers so stakeholders can understand the structure and meaning of enterprise data within a given context. Today, it provides even greater value because critical data exists in both structured and unstructured formats and lives both on premises and in the cloud.

Comparing SQL and NoSQL

While it may not be the most exciting match up, there’s much to be said when comparing SQL vs NoSQL databases. SQL databases use schemas and pre-defined tables, while NoSQL databases are the complete opposite. Instead of schemas and tables, NoSQL databases store data in ways that depend on what kind of NoSQL database is being used.

While the SQL and NoSQL worlds can complement each other in today’s data ecosystem, most enterprises need to focus on building expertise and processes for the latter format.

After all, they’ve already had decades of practice designing and managing SQL databases that emphasize storage efficiency and referential integrity rather than fast data access, which is so important to building cloud applications that deliver real-time value to staff, customers and other parties. Query-optimized modeling is the new watchword when it comes to supporting today’s fast delivery, iterative and real-time applications

DBMS products based on rigid schema requirements impede our ability to fully realize business opportunities that can expand the depth and breadth of relevant data streams for conversion into actionable information. New, business-transforming use cases often involve variable data feeds, real-time or near-time processing and analytics requirements, and the scale to process large volumes of data.

NoSQL databases, such as Couchbase and MongoDB, are purpose-built to handle the variety, velocity and volume of these new data use cases. Schema-less or dynamic schema capabilities, combined with increased processing speed and built-in scalability, make NoSQL the ideal platform.

Making the Move to NoSQL

Now the hard part. Once we’ve agreed to make the move to NoSQL, the next step is to identify the architectural and technological implications facing the folks tasked with building and maintaining these new mission-critical data sources and the applications they feed.

As the data modeling industry leader, erwin has identified a critical success factor for the majority of organizations adopting a NoSQL platform like Couchbase, Cassandra and MongoDB. Successfully leveraging this solution requires a significant paradigm shift in how we design NoSQL data structures and deploy the databases that manage them.

But as with most technology requirements, we need to shield the business from the complexity and risk associated with this new approach. The business cares little for the technical distinctions of the underlying data management “black box.”

Business data is business data, with the main concerns being its veracity and value. Accountability, transparency, quality and reusability are required, regardless. Data needs to be trusted, so decisions can be made with confidence, based on facts. We need to embrace this paradigm shift, while ensuring it fits seamlessly into our existing data management practices as well as interactions with our partners within the business. Therefore, the challenge of adopting NoSQL in an organization is two-fold: 1) mastering and managing this new technology and 2) integrating it into an expansive and complex infrastructure.

The Newest Release of erwin Data Modeler

There’s a reason erwin Data Modeler is the No.1 data modeling solution in the world.

And the newest release delivers all in one SQL and NoSQL data modeling, guided denormalization and model-driven engineering support for Couchbase, Cassandra, MongoDB, JSON and AVRO. NoSQL users get all of the great capabilities inherent in erwin Data Modeler. It also provides Data Vault modeling, enhanced productivity, and simplified administration of the data modeling repository.

Now you can rely on one solution for all your enterprise data modeling needs, working across DBMS platforms, using modern modeling techniques for faster data value, and centrally governing all data definition, data modeling and database design initiatives.

erwin data models reduce complexity, making it easier to design, deploy and understand data sources to meet business needs. erwin Data Modeler also automates and standardizes model design tasks, including complex queries, to improve business alignment, ensure data integrity and simplify integration.

In addition to the above, the newest release of erwin Data Modeler by Quest also provides:

  • Updated support and certifications for the latest versions of Oracle, MS SQL Server, MS Azure SQL and MS Azure SQL Synapse
  • JDBC-connectivity options for Oracle, MS SQL Server, MS Azure SQL, Snowflake, Couchbase, Cassandra and MongoDB
  • Enhanced administration capabilities to simplify and accelerate data model access, collaboration, governance and reuse
  • New automation, connectivity, UI and workflow optimization to enhance data modeler productivity by reducing onerous manual tasks

erwin Data Modeler is a proven technology for improving the quality and agility of an organization’s overall data capability – and that includes data governance and data intelligence.

Click here for your free trial of erwin Data Modeler.

Categories
erwin Expert Blog Enterprise Architecture

Post-Pandemic Enterprise Architecture Priorities

Enterprise architecture priorities

Before the COVID-19 pandemic, many enterprise architects were focused on standardization. Identifying and putting into practice standard approaches to deploying systems, from the IT infrastructure and network protocols to the integration with other components, decreases the time to market for businesses and increases efficiency. In a world where agility and innovation are highly valued, speed is a critical factor for success.COVID-19 forced many businesses to radically change their business models – or re-evaluate their business processes – shifting the focus of enterprise architects. The top priority became mobility through a cloud-first strategy. By evaluating and deploying the right combination of cloud-based platforms and security tools, enterprise architects played a key role in keeping businesses up and running in a remote-work world.

As the world moves forward, enterprise architecture (EA) is moving with it. The enterprise architect needs to develop an understanding of the organization’s business processes and business architecture. With this understanding, enterprise architects can play a key role in both customer and employee experiences, which are central to growing a business today.

Responding to a Crisis

According to Deloitte’s Enterprise Architecture’s Role in Recovering from a Crisis report,  organizations typically respond to a crisis over three phases: respond, recover and thrive.

EA provides a way to drive change through every phase of recovery by providing an understanding of technology assets with business needs. Enterprise architects have been a critical component to helping businesses navigate the pandemic to reimagine the business, ensure business continuity, and identify the tools to survive and ultimately thrive in a post-COVID world.

We saw in the first phases of the pandemic how organizations had to navigate business continuity to survive. For example, a COVID EA response plan could have been used to ask: Are employees working from home? What roles do they have? What work do they do? And when are they available?

New Priorities

According to a survey by McKinsey and Co., the pandemic acted as an accelerant for digital transformation efforts, speeding up the adoption of digital technologies by several years.

As the world moves forward, so must enterprise architecture. Instead of focusing on standardization, the enterprise architect must play a key role in both customer and employee experiences, aspects that are central to growing a business.

Three priorities have emerged for enterprise architects as we move into this next phase:

Priority 1: Business Process and Business Architecture

Enterprise architects are accustomed to thinking about technology architecture and processes. With IT now being seen as an enabler of the business, enterprise architects need to think in terms of the customer journey and how people interact with the business across the value chain.

Priority 2: The Application Portfolio

Oversight of the application portfolio is not a new responsibility for many enterprise architects. Understanding the applications you have, the applications in use, and the applications that are ripe for retirement is an important part of running an efficient IT operation.

Priority 3: Risk Management – Security and Compliance

Businesses are paying close attention to risk from internal and external sources. With more connections between systems and companies, more third-party partnerships and more advanced attacks from cybercriminals and nation-states alike, security is top of mind from the boardroom on down.

The New Normal

As we move into recovery mode, organizations are assessing the processes, systems and technologies that will help them assimilate to the new normal and thrive post-pandemic. However, the role and priorities of enterprise architecture likely will continue to evolve to include responsibility for products, deployments and customers, as businesses continue to transform.

Whether documenting systems and technology, designing processes and critical value streams, or managing innovation and change, you need the right tools to turn your enterprise architecture artifacts into insights for better decisions.

erwin Evolve by Quest is a full-featured, configurable enterprise architecture and business process (BP) modeling and analysis software suite that tames complexity, manages change and increase operational efficiency. Its automated visualization, documentation and enterprise collaboration capabilities turn EA and BP artifacts into insights both IT and business users can access in a central location for making strategic decisions.

To learn more about the new priorities for enterprise architects post-pandemic, read our latest white paper: Enterprise Architecture: Setting Transformation-Focused Priorities.

 

[blog-cta header=”erwin Evolve” body=”Click here to request a demo of erwin Evolve.” button=”Request Demo” button_link=”https://s38605.p1254.sites.pressdns.com/erwin-evolve-free-trial/” image=”http://erwin.com/wp-content/uploads/2020/02/evolve-pic.jpg” ]

Categories
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.

Categories
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. 

Categories
erwin Expert Blog

The Value of Enterprise Architecture to Innovation and Digital Transformation

The value of enterprise architecture to innovation management and digital transformation is clear.

Innovation management is about quickly and effectively implementing your organization’s goals through the adoption of innovative ideas, products, processes and business models.

Most organizations are beginning to realize that to drive business growth and maintain a competitive advantage, innovation needs to be uncovered, documented and socialized rapidly but with care to ensure maximum value.

The process of innovation needs to be managed and governed in the organization because it’s an important facet of a company’s overall function. And ultimately, it is a process in which the business and IT need to collaborate to drive the transformation.

Enterprise Architecture

How Enterprise Architecture Guides Innovation and Transformation

Once you develop a good idea, you need to understand how to implement it successfully, which is why enterprise architecture (EA) is a perennial innovation tool.

Investment in a particular idea requires a degree of confidence that a product, service, IT component or business process is going to make it to market or positively change the business.

Conversely, IT requires traceability back to the innovation that drove it. Without such traceability, it’s difficult to see the value of IT and how it drives the business. And to make it all work seamlessly, it needs to be the business of both those who innovate and those who manage EA.

  • Get the eBook: Enterprise Architecture and Innovation Management

Without EA and an enterprise architecture tool, decision-making expanding from the right ideas and requirements is much more of a lottery.

And while there are more and more projects in progress and a rise in agile development approaches, companies simply do not invest enough time in combining innovation and EA.

DevOps and continuous delivery are prime candidates for connection to innovation management. In the context of speed and time to market, where the frequency, capability and release cycles are key to competitive advantage, EA’s support of decision-making allows innovative ideas to be implemented without costly mistakes.

Strategic Enterprise Architecture Planning Creates Digital Leaders

Innovation management and digital transformation go hand in hand these days, and EA teams can play an integral role, according to a study from McKinsey and Henley Business School.

The study highlighted the need for enterprise architects to facilitate digital transformation by managing technological complexity and setting a course for the development of their companies’ IT landscapes.

One of the stunning results of the study was that 100 percent of respondents from companies that identified themselves as “digital leaders” said their architecture teams develop and update models of what the business’s IT architecture should look like in the future.

In contrast, just 58 percent of respondents from other companies said they adhere to this best practice.

There are three broad states of EA maturity within most enterprises. Where does yours land?

1. Under design

  • Does not exist (or is covered by IT)
  • Information barely managed (or managed on an ad-hoc basis)
  • Knowledge resides mainly in people and disparate other media

2. Existing but needs improvement

  • Efforts have been made to collate and manage information
  • In disparate media, but usually more organized
  • A potential attempt at solutioning has been made
  • Some (manual) reporting is possible

3. Mature and works great!

  • Distinct function within the organization
  • Initial data aggregation and collation is completed
  • There is an EA solution deployed and used
  • Dashboards and reports are available

See also:

Enterprise Architecture Turns Around Inefficiencies

Envision a scenario in which you’re part of the EA team at an energy company with 30,000 wind turbines. When engineers inspect the wind turbines, they record the results on paper forms.

An administrator then uses this paperwork to enter information into the database so repairs can be scheduled. This manual, low-tech approach that relies on good penmanship equates to losing 10 days per year due to manual paperwork that delays necessary repairs; and work-order entry makes up about 25 percent of an admin’s day.

How could technology be used to improve this process? Is there an opportunity for digital transformation? Yes.

By deploying tablets in the field, engineers would be able to review the specs and history of each wind turbine in real time, note the necessary repairs, and then specify the work orders onsite. By driving the innovation process with EA, it’s possible to:

  • Demonstrate how different types and groups of users collaborate within the tool from ideation through execution.
  • Graphically illustrate the ideas, people and support for categories of ideation and innovation
  • Leverage mode 2 activities, such as business scenario planning, persona profiles and strategic value assessments as part of the process.
  • Manage iterative solution or application development projects, leveraging methods such as Kanban, agile, scrum or lean, which help the IT organization pursue a DevOps approach.

Enterprise Architecture at the Heart of Innovation

erwin’s technology roadmap is defined largely by our customers, their needs and requirements, and the trends and initiatives that matter most to their businesses. They are constantly evolving, and so are we.

That’s why we’ve released erwin Evolve, a full-featured, configurable set of EA and business process modeling and analysis tools.

With erwin Evolve, you can map IT capabilities to the business functions they support and determine how people, processes, data, technologies and applications interact to ensure alignment in achieving enterprise objectives.

Such initiatives may include innovation management and digital transformation, as well as cloud migration, portfolio and infrastructure rationalization, and regulatory compliance among other use cases.

Click here to test drive erwin Evolve today.

enterprise architecture business process

Categories
erwin Expert Blog

Top Use Cases for Enterprise Architecture: Architect Everything

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

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

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

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

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

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

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

Enterprise Architecture Use Cases

Top 7 Use Cases for Enterprise Architecture

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

The Regulatory Rationale for Integrating Data Management & Data Governance

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

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

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

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

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

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

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

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

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

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

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

Enterprise Architecture Business Process Trial

Categories
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

Categories
erwin Expert Blog

Constructing a Digital Transformation Strategy: Putting the Data in Digital Transformation

Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a digital transformation strategy viable?

Part Two of the Digital Transformation Journey …

In our last blog on driving digital transformation, we explored how business architecture and process (BP) modeling are pivotal factors in a viable digital transformation strategy.

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It gives them the ability to identify what challenges and opportunities exist, and provides a low-cost, low-risk environment to model new options and collaborate with key stakeholders to figure out what needs to change, what shouldn’t change, and what’s the most important changes are.

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there.

Digital Transformation Examples

Constructing a Digital Transformation Strategy: Data Enablement

Many organizations prioritize data collection as part of their digital transformation strategy. However, few organizations truly understand their data or know how to consistently maximize its value.

If your business is like most, you collect and analyze some data from a subset of sources to make product improvements, enhance customer service, reduce expenses and inform other, mostly tactical decisions.

The real question is: are you reaping all the value you can from all your data? Probably not.

Most organizations don’t use all the data they’re flooded with to reach deeper conclusions or make other strategic decisions. They don’t know exactly what data they have or even where some of it is, and they struggle to integrate known data in various formats and from numerous systems—especially if they don’t have a way to automate those processes.

How does your business become more adept at wringing all the value it can from its data?

The reality is there’s not enough time, people and money for true data management using manual processes. Therefore, an automation framework for data management has to be part of the foundations of a digital transformation strategy.

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management.

You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.

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.

Without this framework and the ability to automate many of its processes, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by taking a manual approach. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs.

With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders.

Constructing a Digital Transformation Strategy: Smarter Data

Ultimately, data is the foundation of the new digital business model. Companies that have the ability to harness, secure and leverage information effectively may be better equipped than others to promote digital transformation and gain a competitive advantage.

While data collection and storage continues to happen at a dramatic clip, organizations typically analyze and use less than 0.5 percent of the information they take in – that’s a huge loss of potential. Companies have to know what data they have and understand what it means in common, standardized terms so they can act on it to the benefit of the organization.

Unfortunately, organizations spend a lot more time searching for data rather than actually putting it to work. In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC.

The solution is data intelligence. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement.

It helps solve the lack of visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications.

Organizations need a real-time, accurate picture of the metadata landscape to:

  • Discover data – Identify and interrogate metadata from various data management silos.
  • Harvest data – Automate metadata collection from various data management silos and consolidate it into a single source.
  • Structure and deploy data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.
  • Analyze metadata – 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, policies and best practices and associate them with physical assets.
  • Socialize data – Empower stakeholders to see data in one place and in the context of their roles.

The Right Tools

When it comes to digital transformation (like most things), organizations want to do it right. Do it faster. Do it cheaper. And do it without the risk of breaking everything. To accomplish all of this, you need the right tools.

The erwin Data Intelligence (DI) Suite is the heart of the erwin EDGE platform for creating an “enterprise data governance experience.” erwin DI combines data cataloging and data literacy capabilities to provide greater awareness of and access to available data assets, guidance on how to use them, and guardrails to ensure data policies and best practices are followed.

erwin Data Catalog automates enterprise metadata management, data mapping, reference data management, code generation, data lineage and impact analysis. It efficiently integrates and activates data in a single, unified catalog in accordance with business requirements. With it, you can:

  • Schedule ongoing scans of metadata from the widest array of data sources.
  • Keep metadata current with full versioning and change management.
  • Easily map data elements from source to target, including data in motion, and harmonize data integration across platforms.

erwin Data Literacy provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms, complete with built-in accountability and workflows. With it, you can:

  • Enable data consumers to define and discover data relevant to their roles.
  • Facilitate the understanding and use of data within a business context.
  • Ensure the organization is fluent in the language of data.

With data governance and intelligence, enterprises can discover, understand, govern and socialize mission-critical information. And because many of the associated processes can be automated, you reduce errors and reliance on technical resources while increasing the speed and quality of your data pipeline to accomplish whatever your strategic objectives are, including digital transformation.

Check out our latest whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.

Data Intelligence: Empowering the Citizen Analyst with Democratized Data

Categories
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

Categories
erwin Expert Blog

Digital Transformation Examples: Three Industries Dominating Digital Transformation

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

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

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

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

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

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

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

Digital Transformation in Retail

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

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

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

Digital transformation examples

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

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

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

Digital Transformation in Hospitality

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

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

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

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

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

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

Digital Transformation in Municipal Government

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

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

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

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

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

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

Mitigating Digital Transformation Risks

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

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

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

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

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

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

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

erwin Expert Blog Subscribe