Tag: ERP

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Software Deployment Strategy: How to Get It Right the First Time

Big or Small, Enterprise Architecture Is a Key Part of a Successful Software Deployment Strategy

A good software deployment strategy could be the difference between multiple and costly false starts and a smooth implementation. Considering the rate at which emerging technologies are introduced, it’s becoming more important than ever for organizations to have a software deployment strategy in place.

But what does it involve?

Not all software deployments and investments are equal. Large-scale, big-money investments like ERP require a lot of resources and planning. Small-scale investments, like website technology, on the other hand, can be purchased, expensed and deployed with few people knowing. And of course, there are thousands of software decisions made that fall somewhere in between.

Software purchase decisions and deployments represent an opportunity to leverage the experience and knowledge of your enterprise architecture (EA) team so you can make smarter, better investments. The key here is the EA team’s complete view of your IT landscape, which can help eliminate redundant purchases, identify issues of integration and more.

 

Software Deployment Strategy: How to Get It Right the First Time

Small Projects Can Create Big Headaches

Here’s an example of how a small-scale software investment can wreak havoc on an organization.

There is an intense focus today on customer experience (CX). Ensuring that your website visitors have access to the information they want, and they can find it quickly and easily, is just part of your overall CX. This makes your customer-facing technologies – the ones that power your website or mobile app – critical investments, even though they may not carry the price tag of an ERP system.

Even the smallest investments need to be vetted to make sure they work with existing infrastructure and processes. One small piece of website tech that ends up degrading your online CX can cost your organization millions in a very short amount of time. There’s simply too many choices just a click away today if something isn’t working properly. Differentiating technologies are also more likely to be customized than an application like ERP, which can often use a number of out-of-the-box processes.

These are areas where a software deployment strategy involving your EA team can help guide the software purchase and deployment process. But even in a world where software deployments increasingly mean logging into a cloud-based SaaS application, a software deployment strategy is still beneficial.

Don’t Be Resigned to Failure

Many SaaS vendors like to talk about how easy it is to get up and running with their products, especially when the infrastructure elements are in the cloud. But the reality is that the network that connects to the SaaS application, the security, the integrations with existing (often on-premise) applications, the SLAs and licensing, can all benefit from a review by the EA team.

Failed software deployments are, in fact, a significant problem for many organizations. Such failures can often be attributed to a lack of planning and foresight.

Considering the costs associated with some software – including its purchase, implementation and consultancy fees/training required to get started – a good software deployment strategy could save millions … literally.

A Gartner study found that nearly half (46 percent) of respondents said their most expensive, time-intensive software deployments were not delivering. When Gartner broke the software purchases in question into deal sizes of over and under $1 million, the firm got similar results.

When your EA team has the visibility to see across your IT landscape and understand the business processes built on your technology, it can help provide a better idea of the real costs behind your software deployments and you can better estimate your time to value. When it comes to software investments, you don’t be resigned to failure.

erwin EA gives organizations a full-featured, versatile platform for enterprise architecture in its broadest sense to ensure the success of projects – regardless of their size or scope.

Start your free trial of erwin EA now.

Enterprise Architecture Business Process Trial

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The Intersection of Innovation, Enterprise Architecture and Project Delivery

The only thing that’s constant for most organizations is change. Today there’s an unprecedented, rapid rate of change across all industry sectors, even those that have been historically slow to innovate like healthcare and financial services.

In the past, managing ideation to the delivery of innovation was either not done or was relegated within organizational silos, creating a disconnect across the business. This, in turn, resulted in change not being implemented properly or a focus on the wrong type of change.

For an organization to successfully embrace change, innovation, enterprise architecture and project delivery need to be intertwined and traceable.

Enterprise Architecture Helps Bring Ideas to Life

Peter Drucker famously declared “innovate or die.” But where do you start?

Many companies start with campaigns and ideation. They run challenges and solicit ideas both from inside and outside their walls. Ideas are then prioritized and evaluated. Sometimes prototypes are built and tested, but what happens next?

Organizations often turn to the blueprints or roadmaps generated by their enterprise architectures, IT architectures and or business process architectures for answers. They evaluate how a new idea and its supporting technology, such as service-oriented architecture (SOA) or enterprise-resource planning (ERP), fits into the broader architecture. They manage their technology portfolio by looking at their IT infrastructure needs.

A lot of organizations form program management boards to evaluate ideas, initiatives and their costs. In reality, these evaluations are based on lightweight business cases without broader context. They don’t have a comprehensive understanding of what systems, processes and resources they have, what they are being used for, how much they cost, and the effects of regulations.

Projects are delivered and viewed on an individual basis without regard for the bigger picture. Enterprise-, technology- and process-related decisions are made within the flux of change and without access to the real knowledge contained within the organization or in the marketplace. All too often, IT is ultimately in the hot seat of this type of decision-making.

5 Questions to Ask of Enterprise Architecture

The Five EA Questions IT Needs to Ask

While IT planning should be part of a broader enterprise architecture or market analysis, IT involvement in technology investments is often done close to the end of the strategic planning process and without proper access to enterprise or market data.

The following five questions illustrate the competing demands found within the typical IT environment:

  1. How can we manage the prioritization of business-, architectural-and project-driven initiatives?

Stakeholders place a large number of tactical and strategic requirements on IT. IT is required to offer different technology investment options but is often constrained by a competition for resources.

  1. How do we balance enterprise architecture’s role with IT portfolio management?

An enterprise architect provides a high-level view of the risks and benefits of a project and the alignment to future goals. It can illustrate the project complexities and the impact of change. Future-state architectures and transition plans can be used to define investment portfolio content. At the same time, portfolio management provides a detailed perspective of development and implementation. Balancing these often-competing viewpoints can be tricky.

  1. How well are application lifecycles being managed?

Application management requires a product/service/asset view over time. Well-managed application lifecycles demand a process of continuous releases, especially when time to market is key. The higher-level view required by portfolio management provides a broader perspective of how all assets work together. Balancing application lifecycle demands against a broader portfolio framework can present an inherent conflict about priorities and a struggle for resources.

  1. How do we manage the numerous and often conflicting governance requirements across the delivery process?

As many organizations move to small-team agile development, coordinating the various application development projects becomes more difficult. Managing the development process using waterfall methods can shorten schedules but also can increase the chance of errors and a disconnect with broader portfolio and enterprise goals.

  1. How do we address different lifecycles and tribes in the organization?

Lifecycles such as innovation management, enterprise architecture, business process management and solution delivery are all necessary but are not harmonized across the enterprise. The connection among these lifecycles is important to the effective delivery of initiatives and understanding the impact of change.

The Business Value of Enterprise Architecture

Enterprise architects are crucial to delivering innovation. However, all too often, enterprise architecture has been executed by IT groups for IT groups and has involved the idea that everything in the current state has to be drawn and modeled before you can start to derive value. This approach has wasted effort, taken too long to show results, and provided insufficient added value to the organization.

Enterprise and data architects who relate what they are doing back to what the C-suite really wants find it easier to get budget and stay relevant. It’s important to remember that enterprise architecture is about smarter decision-making, enabling management to make decisions more quickly because they have access to the right information in the right format at the right time. Of course, focusing on future state (desired business outcome) first, helps to reduce the scope of current-state analysis and speed up the delivery of value.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

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Five Benefits of an Automation Framework for Data Governance

Organizations are responsible for governing more data than ever before, making a strong automation framework a necessity. But what exactly is an automation framework and why does it matter?

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.

Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use.

IT teams need the ability to smoothly generate hundreds of mappings and ETL jobs. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage.

With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way.

In data governance terms, an automation framework refers to a metadata-driven universal code generator that works hand in hand with enterprise data mapping for:

  • Pre-ETL enterprise data mapping
  • Governing metadata
  • Governing and versioning source-to-target mappings throughout the lifecycle
  • Data lineage, impact analysis and business rules repositories
  • Automated code generation

Such automation enables organizations to bypass bottlenecks, including human error and the time required to complete these tasks manually.

In fact, being able to rely on automated and repeatable processes can result in up to 50 percent in design savings, up to 70 percent conversion savings and up to 70 percent acceleration in total project delivery.

So without further ado, here are the five key benefits of an automation framework for data governance.

Automation Framework

Benefits of an Automation Framework for Data Governance

  1. Creates simplicity, reliability, consistency and customization for the integrated development environment.

Code automation templates (CATs) can be created – for virtually any process and any tech platform – using the SDK scripting language or the solution’s published libraries to completely automate common, manual data integration tasks.

CATs are designed and developed by senior automation experts to ensure they are compliant with industry or corporate standards as well as with an organization’s best practice and design standards.

The 100-percent metadata-driven approach is critical to creating reliable and consistent CATs.

It is possible to scan, pull in and configure metadata sources and targets using standard or custom adapters and connectors for databases, ERP, cloud environments, files, data modeling, BI reports and Big Data to document data catalogs, data mappings, ETL (XML code) and even SQL procedures of any type.

  1. Provides blueprints anyone in the organization can use.

Stage DDL from source metadata for the target DBMS; profile and test SQL for test automation of data integration projects; generate source-to-target mappings and ETL jobs for leading ETL tools, among other capabilities.

It also can populate and maintain Big Data sets by generating PIG, Scoop, MapReduce, Spark, Python scripts and more.

  1. Incorporates data governance into the system development process.

An organization can achieve a more comprehensive and sustainable data governance initiative than it ever could with a homegrown solution.

An automation framework’s ability to automatically create, version, manage and document source-to-target mappings greatly matters both to data governance maturity and a shorter-time-to-value.

This eliminates duplication that occurs when project teams are siloed, as well as prevents the loss of knowledge capital due to employee attrition.

Another value capability is coordination between data governance and SDLC, including automated metadata harvesting and cataloging from a wide array of sources for real-time metadata synchronization with core data governance capabilities and artifacts.

  1. Proves the value of data lineage and impact analysis for governance and risk assessment.

Automated reverse-engineering of ETL code into natural language enables a more intuitive lineage view for data governance.

With end-to-end lineage, it is possible to view data movement from source to stage, stage to EDW, and on to a federation of marts and reporting structures, providing a comprehensive and detailed view of data in motion.

The process includes leveraging existing mapping documentation and auto-documented mappings to quickly render graphical source-to-target lineage views including transformation logic that can be shared across the enterprise.

Similarly, impact analysis – which involves data mapping and lineage across tables, columns, systems, business rules, projects, mappings and ETL processes – provides insight into potential data risks and enables fast and thorough remediation when needed.

Impact analysis across the organization while meeting regulatory compliance with industry regulators requires detailed data mapping and lineage.

THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE

  1. Supports a wide spectrum of business needs.

Intelligent automation delivers enhanced capability, increased efficiency and effective collaboration to every stakeholder in the data value chain: data stewards, architects, scientists, analysts; business intelligence developers, IT professionals and business consumers.

It makes it easier for them to handle jobs such as data warehousing by leveraging source-to-target mapping and ETL code generation and job standardization.

It’s easier to map, move and test data for regular maintenance of existing structures, movement from legacy systems to new systems during a merger or acquisition, or a modernization effort.

erwin’s Approach to Automation for Data Governance: The erwin Automation Framework

Mature and sustainable data governance requires collaboration from both IT and the business, backed by a technology platform that accelerates the time to data intelligence.

Part of the erwin EDGE portfolio for an “enterprise data governance experience,” the erwin Automation Framework transforms enterprise data into accurate and actionable insights by connecting all the pieces of the data management and data governance lifecycle.

 As with all erwin solutions, it embraces any data from anywhere (Any2) with automation for relational, unstructured, on-premise and cloud-based data assets and data movement specifications harvested and coupled with CATs.

If your organization would like to realize all the benefits explained above – and gain an “edge” in how it approaches data governance, you can start by joining one of our weekly demos for erwin Mapping Manager.

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