The decades-long history of the mainframe has centered on the value the platform can provide for mission-critical data and transaction processing tasks.

Today, the mainframe remains the most essential platform for mission-critical operations across the enterprise landscape, despite the ongoing evolution of cloud computing and the rest of the distributed systems world.

Nevertheless, the broader context of business and technology transformation drives change for the platform, as modernizing and optimizing the mainframe in place become critical priorities.

In fact, the business drivers for mainframe transformation are so urgent that mainframe leaders can no longer sit back while the rest of the organization transforms.

Such change is always difficult. Change that rises to the level of transformation can be daunting. Understanding an organization’s motivations for such transformation, therefore, is a critical first step to long-term success.

Within the banking, financial services, and insurance (BFSI) sector, seven interrelated mainframe transformation drivers provide the motivation for this change.

The Seven Core Drivers of Mainframe Transformation

Given the mainframe’s long-standing mission-critical role, business priorities must be sufficiently urgent to drive transformation on the platform.

BFSI mainframe leaders will likely recognize several of these drivers.

Mainframe transformation driver #1: reducing complexity (cybersecurity driver)

The mainframe is but one system among many, as it is a full participant in today’s complex cloud native world. With this complexity comes costs as well as risks: the risk of failure as well as the ever-present cybersecurity risk.

BFSI enterprises require modern tools that deal with constantly evolving cyber threats, including real-time monitoring, encryption, and threat detection powered by artificial intelligence (AI).

Furthermore, technical debt challenges, including outdated code, tools, and processes, add to this complexity.

Transforming the mainframe in place reduces this debt. For example, moving to cloud storage for mainframe backup addresses the cost and complexity of traditional tape systems while eliminating their technical debt.

Mainframe transformation driver #2: dealing with disruption (cybersecurity driver)

Cyber threats and technical debt aren’t the only sources of potential disruption. In addition, organizations must deal with digital transformation pressures, shifting regulations, and the evolving customer expectations that come with a dynamic, competitive landscape.

Innovation also leads to disruption, as BFSI enterprises leverage new technologies and approaches like generative AI, AIOps, DevOps, hybrid cloud integration, and a plethora of other drivers of change.

Success in the face of such disruption requires a multi-pronged approach that includes continued investments in cybersecurity as well as a greater focus on resilience.

Mainframe transformation driver #3: cost efficiency (modernization driver)

Mainframe organizations seek to streamline operations and limit inefficiencies across the technology and process landscape while simultaneously reducing costs. This streamlining must maintain the resilience for which these companies have always relied upon the mainframe.

Cost efficiency is especially important for BFSI enterprises, as the mainframe is both mission-critical and always on. Even a savings of a few percent can add up to millions of dollars per year for a 24 x 7 system like the mainframe.

Modernizing the mainframe in place is an important tool for maintaining cost efficiency without sacrificing resilience, as such a strategy extends the life of the platform while leveraging it to meet changing business needs.

Mainframe transformation driver #4: operational efficiency (AIOps driver)

Cost efficiency is not the only mainframe efficiency driver. Operational efficiency focuses on maintaining the mainframe’s “never fail” role in the IT estate.

BFSI enterprises have always been able to trust the platform to have near-perfect uptime – and this promise must continue as such organizations leverage the platform for new applications and services.

The operational efficiency driver is responsible for increased attention to IT operations management and is thus responsible for increased investments in AIOps and other modern management technologies.

Mainframe transformation driver #5: improving time-to-value (DevOps driver)

Today, the need for speed drives rapid change across the mainframe landscape, just as it does for the distributed computing world.

On the mainframe, increasing time-to-value requires faster delivery of applications, which in turn speeds up the deployment of new financial services across BFSI product lines.

To achieve this acceleration, BFSI mainframe development teams have been adopting DevOps and platform engineering best practices, extending state-of-the-art app development approaches from the cloud world to the mainframe.

Mainframe transformation driver #6: leveraging a shifting workforce (modernization driver)

As experienced mainframe professionals retire, BFSI mainframe organizations must bring on new generations of mainframe talent.

This new talent grew up on the Internet, and entered the workforce leveraging modern, cloud-centric technologies and approaches like DevOps.

Hiring such younger professionals onto mainframe teams should not be an exercise in stifling their expectations of technology. Rather, it’s an opportunity to bring new ways of thinking and solving problems to the venerable mainframe shop.

Mainframe transformation driver #7: regulatory compliance (business driver)

BFSI has always consisted of regulated industries, so compliance has been a long-standing concern for every mainframe organization in the space.

Nevertheless, today’s regulatory climate is remarkably dynamic, considering global trends in security, privacy, and resilience.

The Digital Operational Resilience Act (DORA) in Europe, as well as similar regulations in other jurisdictions, are important examples. DORA requires financial institutions to strengthen their operational resilience and cybersecurity postures – priorities that align with other drivers of mainframe transformation in their organizations.

The Intellyx take

It’s important to note that these drivers are interrelated. Different organizations may not even see them as distinct.

Depending on the specific business circumstances, one transformation driver may be the top priority for an organization, while a different driver is more of a concern for a different company.

Furthermore, organizations must weigh every transformative change on the mainframe against the risks inherent in migrating off the platform, including the loss of institutional knowledge, potential downtime, and security risks.

As a result, it’s important to actively assess and prioritize the transformation drivers within the organization. It’s useful to have a checklist like this one to ensure that you’re weighing the various drivers of transformation against each other as you plan the continuing role for the mainframe into the future.

Copyright ^© Intellyx BV. BMC is an Intellyx customer. Intellyx retains final editorial control of this article. No AI was used to write this article.

As enterprises accelerate their digital transformation journeys, many are turning to SAP RISE with SAP S/4HANA to simplify their path to the cloud while preserving business continuity. SAP RISE is SAP’s strategic offering that bundles cloud infrastructure, managed services, and SAP S/4HANA into a single subscription model.

But as SAP landscapes grow more complex—with a mix of on-premises, cloud, and hybrid environments—the need for seamless orchestration and intelligent automation has never been greater. Control-M  is a proven, SAP-certified application and data workflow orchestration platform that is now fully compatible with SAP RISE and integration-ready with SAP S/4HANA.

Control-M: Purpose-Built for Modern SAP Workflows

Control-M empowers enterprises to orchestrate and monitor business-critical workflows across SAP and non-SAP systems with a single, unified platform. As organizations transition from SAP ECC to SAP S/4HANA—either on-premises or through SAP RISE—Control-M ensures that scheduling, automation, and monitoring capabilities remain robust, flexible, and aligned with modern best practices.

Whether it’s traditional ABAP-based jobs, cloud-native extensions, or third-party integrations, Control-M manages them all—without relying on custom scripts or siloed tools.

SAP RISE Compatibility: Simplifying the Move to Cloud ERP

While SAP RISE streamlines procurement and lowers TCO, it also introduces a shared responsibility model, making automation and visibility into background jobs even more essential.

Control-M is designed to integrate directly with SAP S/4HANA under the SAP RISE model, ensuring that organizations retain full control over their scheduled jobs, dependencies, and business workflow, even as SAP infrastructure and services are managed by SAP or Hyperscalers.

Seamless Integration with SAP S/4HANA Features

Control-M supports the full range of SAP S/4HANA features and architecture elements, including:

• ABAP jobs
• SAP Business Warehouse (BW) processes
• Data archiving
• SAP HANA and Fiori-based applications
• SAP BTP extensions and API-based workflows
• Hybrid and multi-cloud environments (including OCI, AWS, Azure)

With Control-M, users can define and manage dependencies between SAP S/4HANA jobs and external workflows—whether they’re running in a data lake, cloud integration platform, or third-party ERP module.

Future-Ready: Clean Core and SAP BTP Integration

As enterprises adopt clean core strategies—keeping custom logic outside the core S/4HANA system, Control-M’s support for SAP BTP and API-based orchestration becomes critical. Businesses can now automate workflows across SAP BTP extensions and custom applications, maintain upgrade readiness, and drive agility across their IT operations.

This makes Control-M an ideal partner for organizations embracing side-by-side innovation with SAP BTP, as well as cloud-native integrations.

Key Benefits for SAP-Centric Enterprises

• Unified orchestration of SAP and non-SAP workflows across cloud, hybrid, and on-prem environments
• Out-of-the-box support for SAP ECC, SAP S/4HANA, and SAP RISE
• End-to-end visibility and SLA management from a single control plane
• Faster troubleshooting and reduced downtime through proactive monitoring and alerts
• Support for clean core principles via SAP BTP and API integrations

The move to SAP S/4HANA and SAP RISE is a strategic imperative for many organizations—but the transition requires careful orchestration, especially as business processes become more distributed and data-driven.

With Control-M, enterprises can confidently modernize their SAP environments, maintain full control over their critical workloads, and unlock the full value of SAP’s intelligent ERP—now and in the future.

To learn more about how Control-M for SAP can help your business, visit our website.

Today, the tech required for an always-on world seems to be on an almost daily evolution, and AI is at the heart of it. A lot of our customers have already made or are making the move to an AIOps-based infrastructure that lives in a cloud or hybrid cloud environment. And now comes the promise of agentic AI, with all sources heralding it as a tidal wave, poised to compete with smartphones in its far-reaching impact. But can it really change the world? With the right planning and governance, I think yes. And I believe it may be one of the smartest IT investments organizations will ever make.

The quiet part out loud

So, let’s address the elephant in the room: agentic AI also has the power to reduce workforce costs—and yes, that makes a lot of IT leaders uncomfortable. Not because the tech isn’t compelling, but because teams are made up of people. People with institutional knowledge, technical expertise, and a lot of pride in their work. So, what happens when AI starts doing some of that work faster and cheaper?

Here’s the short answer: you don’t lose value when you reduce repetitive work—you redirect it. Agentic AI thrives in taking on structured, time-consuming tasks like incident resolution, change management, and proactive monitoring. That’s where cost savings begin: fewer manual hours, fewer escalations, and faster resolutions.

Change the mix

The current economics are that we spend 90 percent of our time (and budget) on “keep the lights on” work and 10 percent on work that can change the business because there simply isn’t time to do both. One real world example? For years, enterprise project management offices have captured demands (sometimes called ideas) that have yet to see the light of day because they’ve been either too expensive or too unrealistic to work on.

Agentic AI gives us the opportunity to change the mix. Enterprises can use the extra capacity freed up by agentic AI to focus on the captured demands that have gone unaddressed. Again, Ai isn’t about gutting teams—it’s about giving your people space to do more of what only humans can do: apply judgment, respond with empathy, and make decisions in messy, unstructured situations where AI still struggles.

Keep humans in the loop

Case in point: earlier this year, a major airline suffered customer backlash when its automated system rebooked passengers on inconvenient, impractical routes after weather disruptions—with no human check before the messages went out. The company had the right tech but missed the human layer of oversight. That’s the risk when automation becomes a blunt instrument. Agentic AI, used well, doesn’t replace people—it reroutes the work so humans can provide guidance, make calls when things aren’t clear-cut, and prevent problems AI can’t predict.

That’s why it’s not just about cost savings—it’s about strategic efficiency. With AI handling repeatable tasks, IT teams can focus on security, governance, long-term planning, and experience optimization. These are areas where human strengths matter most—and where companies differentiate. And let’s be honest: IT budgets aren’t getting any looser. If you’re not exploring how AI can streamline your operations, someone else in your industry already is. Staying competitive means making room for tools that amplify your team’s impact, not shrink it.

To be continued

This is the first of a three-part conversation. Next, we’ll dig into how you can approach getting agentic AI up and running with compelling use cases that are more likely to get buy-in across the business. And in the final post, we’ll show how to take that momentum to the C-suite, framing the savings and innovation in a way that unlocks continued investment. Because this isn’t just a tech shift. It’s a business imperative.

SAP Business Technology Platform (SAP BTP) is a comprehensive, multi-cloud platform that enables organizations to develop, extend, and integrate business applications. It offers a broad suite of services across data and analytics, artificial intelligence, application development, process automation, and enterprise integration—empowering digital innovation and agile business transformation. By leveraging SAP BTP, organizations can achieve centralized visibility, enhanced operational reliability, and real-time coordination of background processes, data flows, and application services. This seamless integration via Integration Suite leverages API’s which streamlines operations, minimizes manual intervention, and ensures uninterrupted business execution—particularly critical during digital transformation initiatives such as migrating to SAP RISE /SAP S/4HANA.

Designed to support clean core principles, SAP BTP enables customers to decouple custom logic from the digital core (e.g., SAP S/4HANA), using APIs and side-by-side extensions that promote agility, upgradeability, and innovation.

Control-M integrates with SAP BTP through robust API-based connectivity, (Application Integrator) enabling enterprises to seamlessly orchestrate, schedule, and monitor workflows that span both SAP and non-SAP systems. By leveraging SAP BTP’s extensibility and integration capabilities, Control-M can automate and manage end-to-end business processes involving applications built or extended on BTP, such as SAP S/4HANA extensions, custom applications, or third-party service integrations.

This integration allows for real-time execution and monitoring of background jobs, data pipelines, and event-driven processes across hybrid environments. Control-M simplifies job scheduling and workflow orchestration on SAP BTP by offering a centralized platform to define dependencies, manage workloads, and ensure SLA compliance across diverse systems.

Control-M’s capabilities are further enhanced by the introduction of a new SAP BTP job type, designed specifically to streamline scheduling, orchestration, and monitoring of workflows running on SAP BTP. This new job type enables users to natively connect with SAP BTP’s API-driven environment, allowing seamless automation of jobs across SAP extensions, custom applications, and integrations built on the platform.

With this innovation, Control-M users can define, schedule, and monitor SAP BTP-based tasks alongside traditional SAP jobs and non-SAP workflows—all within a unified interface. The integration provides end-to-end visibility and control over complex, hybrid workflows, reducing manual effort and accelerating response times to job failures or exceptions.

With its cloud integration services and API-first architecture, SAP BTP allows seamless connectivity across hybrid environments and supports integration with non-ABAP systems. These capabilities align perfectly with Control-M’s application and data workflow orchestration, delivering powerful automation across complex enterprise landscapes.

This capability is particularly valuable for organizations migrating to SAP S/4HANA or adopting SAP RISE, as it supports automation and governance across modern SAP landscapes. By leveraging Control-M’s new SAP BTP job type, businesses can enhance operational efficiency, improve SLA adherence, and drive smoother digital transformation journeys.

To learn more about Control-M for SAP, visit our website.

If there is one thing that businesses around the world have learned this year, it is this: nothing is certain. When we wished each other Happy New Year, most of us expected life to go on as usual. But as Dr. Spencer Johnson said in his best-selling book Who Moved My Cheese,

“Life is no straight and easy corridor along which we travel free and unhampered, but a maze of passages, through which we must seek our way, lost and confused, now and again checked in a blind alley”.

Ensure Continuity by Planning for Change

All businesses want to flourish regardless of the season, but this calls for forward planning and risk management to make one prepared for the unforeseen. And this brings us to two terms—business continuity and business resiliency—that are used interchangeably but are different in some ways.

Let’s take a look.

What is Business Continuity?

The ISO 22300:2018 standard defines business continuity as:

“The capability of an organization to continue the delivery of products or services at acceptable predefined levels following a disruption”.

A disruption could be anything from your superstar employee moving to your competitor, new legislation forcing you to make drastic changes to your products, or an unforeseen event in the local or global economy that destroys what you have taken years to build. Business continuity means anticipating such disruptions and preparing a plan to ensure that you can continue business operations if the disruptions materialize.

We can use the Plan Do Check Act (PDCA) cycle to describe the activities involved in business continuity management:

Plan

Planning for business continuity mainly involves:

• Understanding the environment in which your organization operates.
• Identifying potential risks which, if they materialize, can disrupt day-to-day operations. As you identify risks, you’ll classify, prioritize, and determine mitigation actions.

In addition, business impact analysis exercises are used to identify critical business processes, the underlying assets that support them, and the potential impact the organization faces should the assets or processes be disrupted. Here, key metrics such as RTO, RPO, and MAO are used to determine the acceptable disruption and required speed of continuity.

Do

This involves implementing the control measures that would ensure continuity in case disruption occurs in line with the business continuity plan. These would include:

• Appropriate IT systems
• People
• Suppliers
• Procedures
• Budget
• Defined target metrics

As people are expected to implement the business continuity plan, you must provide training for key players and create awareness for everyone involved to ensure alignment and preparation for the unexpected.

Check

The organization must continue to regularly check whether the control measures are working and remain relevant to meeting the organization’s needs, especially as the environment changes. Testing will identify whether the continuity metrics can be met using existing measures or more is required.

Act

Based on the results of the tests and actual disruptions, the leadership will need to take both corrective and preventive action to ensure the business continuity plan remains effective for the ever-evolving context that the business faces.

(Learn more about how the PDCA cycle can support continuous improvement.)

What is Business Resiliency?

The ISO 22316:2017 standard defines organizational resilience as:

“The ability of an organization to absorb and adapt in a changing environment to enable it to deliver its objectives and to survive and prosper.”

ITIL 4 defines resilience as the ability of an organization to anticipate, prepare for, respond to, and adapt to both incremental changes and sudden disruptions from an external perspective.

In simple terms, it means taking a blow and recovering from it. For a business, that means that when disruption occurs, you have mechanisms in place to absorb the hit without significant impairment to your business operations.

(Head to our learn page to learn more about Operational Resilience.)

In order to have a framework for effective organizational resilience, there are certain principles that need to be adhered to. Resilience requires:

• Behaviour that is aligned with a shared vision and purpose
• An up-to-date understanding of an organization’s context
• Ability to absorb, adapt, and effectively respond to change
• Good governance and management
• Diversity of skills, leadership, knowledge, and experience
• Coordination across management disciplines and contributions from technical and scientific areas of expertise
• Effective risk management

With these principles in place, you can deploy a coordinated approach that provides:

1. A mandate to ensure the organization’s leadership is committed to enhance organizational resilience
2. Adequate resources needed to enhance the organization’s resilience
3. Appropriate governance structures to achieve the effective coordination of organizational resilience activities
4. Mechanisms to ensure investments in resilience activities are appropriate to the organization’s internal and external context
5. Systems that support the effective implementation of organizational resilience activities
6. Arrangements to evaluate and enhance resilience in support of organizational requirements
7. Effective communications to improve understanding and decision making

Business Continuity vs Business Resilience: next steps

According to PWC, business resilience builds on the principles of business continuity but extends much further to help enhance an organization’s immune system to be able to tackle challenges, fend off illness and bounce back more quickly.

How to increase Business Resiliency

As there is no single approach to enhance an organization’s resilience, it is more realistic to consider it the result of:

• The relationships and interactions of attributes and activities.
• Contributions from other management disciplines such as disaster recovery, crisis management, and business continuity, which by themselves are insufficient to lead to resilience.

Similar to business continuity, there is a lot of emphasis in organizational resilience on understanding the environment, identifying and assessing potential risks that could disrupt the business operations, and planning to deal with the disruption if it occurs. However, while business continuity is process centric, resilience is more strategic in nature, being a holistic approach that is influenced by a unique interaction and combination of strategic and operational factors.

Benefits of business continuity and resilience

Lasting business success requires that your organization has the resilience to survive, even thrive, through disruptions, maintain operations through tough times, and recover quickly. To ensure the continuity of your business through cyber attacks, natural disasters, geopolitical events, and supply chain or economic disruption takes planning and preparation. Here are some reasons why it is worth making those efforts.

• Minimized downtime: Effective business continuity planning ensures that everyone knows what needs to be done and who will do what. While nothing goes exactly as planned, you can recover faster with reduced downtime and the ability to maintain operational flow.
• Safeguarding reputation: As with people, companies that show they can perform under pressure earn the trust and admiration of others. You give employees, customers, and shareholders confidence when you respond swiftly and surely with minimal service interruption.
• Risk mitigation: Taking a proactive stance will help identify and mitigate potential risks before damage is done. When you prepare your business for the unexpected, you lessen the negative impacts of crises.
• Financial stability: Continuity planning reduces the financial fallout of a crisis. With resilient operations, you can maintain cash flow and strengthen stakeholder confidence.
• Regulatory compliance: Government regulations impose stiff penalties and fines on compliance failures, making a bad situation worse. Implementing business continuity measures with strong documentation reduces potential legal complications.
• Improved employee confidence: A well-structured continuity plan eases fears, removes indecision, and raises morale, instilling a sense of security from clarity and preparedness.

BMC team could, additionally, come up with a similar visual aid as shown below:

Source: https://drata.com/blog/business-continuity-and-resilience

Additional resources

For more on business practices and culture, explore the BMC Business of IT Blog and these articles:

• What Is Threat Remediation? Threat Remediation Explained
• Impact, Urgency, and Priority: Understanding the Matrix
• The State of ITSM in 2020

In 1982, Alan Kay famously stated:

“People who are really serious about software should make their own hardware”.

And in this age of cloud and mobile, where we rarely interact with servers and network equipment, it might be easy to think they are no longer as useful as before.

But the hard truth is that good hardware remains the bedrock for all technology services. That’s why having an approach that effectively manages the lifecycle of hardware assets is critical, whether it’s for your own organization or for others, in a hosted arrangement.

Let’s explore hardware asset management and how it works for your organization.

(This article is part of our Sustainable IT Guide. Use the right-hand menu to explore topics related to sustainable technology efforts.)

Understanding & managing hardware assets?

An asset is any item, thing, or entity that has potential or actual value to an organization. That’s according to ISO.

Value here is about benefits for the organization:

• Value is usually financial in nature, as seen in the balance sheet, when an asset is used to generate revenue, reduce costs, or mitigate risk.
• Value can also be non-financial when it’s used well, such as customer satisfaction.

Hardware assets in IT service management refers to assets that are tangible in nature—those you can touch and feel. Hardware assets include those that are in use as well as those in storage.

Some examples of hardware assets, as listed in ITIL^® 4, include:

• End-user devices: personal computers, laptops, tablets, smartphones, and SIM cards
• Network and telecom equipment: routers, switches, load balancers, and video- conferencing and voice over Internet protocol (VoIP) systems
• Data center hardware: servers, storage and backup systems, utilities, and security equipment
• Significant peripherals: personal printers, monitors, scanners, and multifunction printing systems

(Explore the related practices of IT asset management and enterprise asset management.)

Benefits of IT hardware asset management

Hardware assets can be expensive to procure, configure, maintain, and secure. They also require significant management effort and they depreciate rapidly.

Organizations that successfully execute Hardware Asset Management as a discipline often experience enterprise-wide improvements in their operations, and ultimately their bottom lines. These benefits include:

• Improved business agility
• Increased asset lifespan
• Reduced overheads
• Increased operational efficiency

Using automated systems for managing hardware assets, including IoT, can go a long way in raising productivity by:

• Reducing the management effort required
• Enhancing security and providing visibility in asset use, which can support decisions around improving operational efficiency and effectiveness.

Hardware asset management lifecycle

The same standard defines asset management as the coordinated activity of an organization to realize value from assets.

This coordination covers the acquisition, use, and disposal of assets, as described in the hardware asset management lifecycle from ITAM:

Let’s look at each phase.

1. Specify

Hardware planning is usually driven by two perspectives. On the one hand, there is data from business or customer side that is indicative of strategy and demand that influence capacity and type, while on the other hand there are technical aspects driven by evolution, incidents, problems, and continuity factors.

A hardware asset plan will capture these two perspectives, and consolidate the information into a concise approach that meets the priorities of the organization.

Once you’ve identified the priorities, budgeting is the next logical step. Of course, the business plays the bigger role in the decision-making process based on how much funding is available for hardware assets.

Budget will also be determined by preferred acquisition role as upgrades and leasing are considerably cheaper compared to outright purchase.

(See how capital and operating expenses play out over time.)

2. Acquire

Following the budget, the procurement process kicks in.

In this phase, you’ll have to write specifications for hardware to a sufficient detail in order to:

• Ensure the selected vendors understand what you need
• Guarantee that your organization receives bids that meet its needs.

Key determinants of which vendors you’ll select usually include:

• Compatibility with other assets, existing or planned
• Warranty
• Technical support

Procurement will ensure the contract captures elements of support under the service level agreement (SLA).

One alternative to acquisition is to bring your own device (BYOD). In this model, users provide their own computing devices, which will be:

• Logged by the corporate body
• Provided access to internal systems subject to the relevant information security policies

Upon receipt of the hardware assets, your organization must log them in a fixed asset register for financial reasons. This ensures that the financial value from the asset is:

• Captured for financial reporting
• Depreciated year on year

You’ll also tag the asset with the appropriate tagging mechanism, then store the asset in preparation for dispatch and assignment.

3. Deploy

The next step, before dispatching and assigning the asset, is to capture the hardware asset as a configuration item

in the relevant IT service management system or register. This supports and device servicing, when necessary, by logging the information needed to support monitoring and maintenance activities by designated IT and vendor support teams.

For end user hardware assets, deployment means either:

• Configuring, dispatching, and assigning assets to the user at their designated work area, including home for purposes of remote work.
• Having the users collect their devices from IT.

For security reasons, you might:

• Issue asset passes to facilitate movement in and out of buildings
• Require users to sign an acceptable use policy before handing over the asset

For corporate hardware assets, deployment means:

1. Moving the assets from storage.
2. Using the change management process to configure, install, and integrate assets into the live environment.

IT specialists or vendors would conduct these deployment activities. Internal teams, including security specialists and systems audits, then handle the validation process.

4. Service

At this step, you carry out maintenance of the hardware asset. This usually happens either as:

• Scheduled maintenance carried out by IT specialists or vendors in line with contract SLAs.
• A response to events, incidents, and problems that require remedial actions.

To support maintenance, an essential activity is spares management, ensuring that faulty parts can be restored quickly and effectively.

Servicing will also include necessary upgrades and patches, which are subject to the existing change management process.

Financial management will track the usage of the asset and compute depreciation as part of annual statutory financial reporting.

5. Retire

Once the hardware asset reaches its useful end of life or is unserviceable, it will then be decommissioned, and then considered for disposal as a logical final step. Decommissioning can also be triggered by:

• Employee exit (considering BYOD)
• Security or audit advisory regarding vulnerabilities or compromise

Decommissioning is a sensitive process when it comes to corporate hardware assets. As such, manage decommissioning using the existing change management processes. Status of the asset should be updated in the IT service management system or hardware asset register.

Before disposal, a security check is required to ensure that the asset is wiped of any corporate information. Specialized techniques might be required to ensure that any data contained in drives is irrecoverable.

Disposal could involve:

• Returning, as in the case of leased assets
• Selling, in the case where the asset still has some financial value

Some organizations consider donations to other institutions, e.g. for education or charity.

Finally, update the asset records to reflect the exit of the hardware asset from the organization.

Challenges of hardware asset management

While effective hardware asset management is essential for operational efficiency, cost control, and service quality, there are considerable obstacles. Below are some of the most common challenges companies face.

• Lack of organization: Keeping tabs on hardware across your organization is difficult if you don’t have a centralized and structured process for assigning ownership and tracking maintenance. Improvised systems or trying to use spreadsheets can lead to gaps, confusion, and potential compliance issues.
• Change and obsolescence: Hardware has a limited useful lifespan. Maintenance and upgrades are part of the lifecycle, but devices eventually must be replaced. Without an automated process, keeping track of all device details can strain your IT operations.
• Poor budget planning: Without good data on hardware assets, it’s easy to overspend on new equipment or fail to repurpose existing hardware, even potentially donating it for a tax advantage. Good hardware tracking helps with planning and budgeting.
• Manual inventory: Tracking hardware by hand is time-consuming, error-prone, and not scalable. For companies with multiple locations and remote workers, the problem is magnified.
• Service and support: When asset details are wrong, outdated, or missing, resolving technical issues can be frustrating and time-consuming. A modern hardware asset management framework provides visibility for fast and effective troubleshooting.

Managing hardware assets

Hardware asset management isn’t a practice for later. With ramifications around employee productivity, financial health, and overall security, hardware asset management is a critical activity for every organization.

Related reading

• BMC Service Management Blog
• IT Asset Management: 10 Best Practices for Successful ITAM
• IT Asset Management vs Software Asset Management: What’s The Difference?
• Software Asset Management: Mistakes, Truths & Best Practices in SAM
• Mobile Device Management (MDM): An Introduction

Think of the Enterprise Network as the internet, except that it’s local to your organization.

An enterprise network helps employees and machines communicate, share files, access systems, and analyze the performance of an IT environment that drives business operations. Enterprise networks are configured to:

• Connect a limited number of authorized systems, apps, and individuals.
• Enable a secure and efficient communication channel to perform specific business operations.

In this article, we will discuss the enterprise network, how it helps the business, and industry-proven best practices to run secure, high performance, and highly dependable enterprise networking systems.

What is an enterprise network?

The term ‘enterprise network’ refers to the physical, virtual, or logical connectivity infrastructure that enables systems and apps to:

• Communicate
• Share information
• Run services and programs
• Analyze system performance

The enterprise network effectively comprises the infrastructure, hardware and software systems, and the communication protocols used to deliver end-to-end services. The network (or its subset) may be architected, designed, deployed, optimized, and configured to perform a unique set of business and technical objectives.

To establish an enterprise network at geographically disparate locations, use Virtual Private Networks (VPNs) to connect these regions.

(Understand IT infrastructure and cloud infrastructure.)

Types of enterprise networks

Some of the common types of enterprise networks include:

• Local Area Networks
• Wide Area Networks
• Cloud networks

Local Area Network (LAN)

A LAN is a computer network that interconnects systems within a small building or room. Typically used for personal, non-commercial use cases, LANs can also be used as small-scale prototyping or testbed networks.

You can also establish LANs logically and virtually within a larger network. For example, each department within the enterprise network can operate a small LAN where multiple computers are connected to the same switch but decoupled from other departmental LANs.

Wide Area Network (WAN)

Think of a LAN that spans across buildings and disparate geographic locations—even globally.

WAN connectivity differs from LANs in terms of the protocols and components across the layers of the OSI model used to transmit data. While LAN technologies are used to transmit data at higher rates within close proximity, WANs are set up for communication that is:

• Long-distance
• Energy efficient
• Secure
• Dependable

WANs can be deployed as a private or public network and are usually set up by the internet service providers (ISPs).

You can also have a software-defined WAN, or SD-WAN. This is a virtual WAN architecture controlled by software technologies that create an abstraction of the virtualized WAN from the underlying infrastructure components. This technology enables secure WAN operations while decoupling the performance from the underlying components.

An SD-WAN offers more flexible and dependable connectivity services that can be controlled at the application level, without sacrificing security and quality of service (QoS).

(Learn more about software-defined networking.)

Cloud networks

Most enterprise IT services are delivered from data centers and cloud networks. The IT environment may be a hybrid mix of on-premise servers and off-site cloud networks. The cloud stack may consist of multiple cloud computing models—private, public, and hybrid cloud.

Additionally, you likely employ multi-cloud services to deliver various application components and services as an optimal tradeoff between cost, performance, and security offered by different cloud models.

The infrastructure components and software technologies enable the connectivity between data center hardware, applications. and services running across these various IT environments. The cloud resources and the services running on the hardware are accessed and controlled over the internet, usually through private and secure network channels (unless used for public-facing applications).

Conceptually, cloud networks can be seen as a WAN (often an SD-WAN) that may comprise multiple subset of networks shared or distributed privately among customers of cloud computing services.

Benefits of enterprise networking architecture

The networking architecture of your enterprise is the digital foundation for your organization. It enables the agility you need and provides security that evolves to stay ahead of changing threats. Functionally, it provides the connectivity between people, devices, applications, and data to optimize operations and empower innovation.

The right networking architecture supports long-term growth and helps your organization thrive with these core benefits:

• Always-on connectivity: It provides continuous access to data, systems, and applications in distributed environments, both remote and on-site.
• Optimized user experience: In managing traffic intelligently, it gives users access to the tools they need quickly and without fail.
• Readiness for digital transformation: It enables you to be future-ready and able to integrate and use the latest technologies like IoT, AI, and edge computing.
• Easier network management: It simplifies network configuration, monitoring, and troubleshooting with automation and tools for managing complex environments.
• Enhanced security: In using the latest technology and complying with multiple security frameworks, it offers enhanced protection against threat actors and their exploits.
• Flexible software subscriptions: It removes the need to invest in new hardware to scale operations or to use the latest features and applications.
• Seamless cloud integration: It provides the dependable, secure, and uninterrupted connectivity essential to modern IT operations, whether in public, private, or hybrid cloud environments.

Enterprise networking services, trends, and concepts

Already embarked on your enterprise networking strategy? It can be interesting to follow some of the latest trends in the enterprise networking domain.

Today’s technology advancements and improvements are generally centered around service dependability, security, and readiness to integrate new technology standards and systems.

Some new innovations and trends include:

• Secure Access Service Edge (SASE). This network architecture introduces an additional security layer for edge network technologies.
• 5G connectivity. With significant investments and adoption recently, the new 5G networking standard is set to reach maturity in coming years. Organizations taking advantage of the technology are early adopters and disruptors, especially since 5G connectivity offers significantly better user experience with high data transmission rates.
• Wi-Fi 6 and 6E. These new connectivity standards are around 30% faster than Wi-Fi 5. They’re especially useful for simple in-house LAN implementations.
• Cloud-managed popularity. According to a recent IDC publication, cloud-managed WAN, SD-WAN, and Unified Communications adoption continues to rise.
• Managed service options. New service delivery models, like Networking as a Service (NaaS), enable organizations to leverage advanced enterprise networking capabilities on a subscription cost basis.
• AI and machine learning. AI- and ML-enabled enterprise networking will greatly enhance visibility and control into enterprise networks and the IT infrastructure that generates a vast deluge of information at every node and network endpoint.

(See how ML supports data center network security.)

Related reading

• BMC IT Operations Blog
• The Network Operations Center (NOC): How NOCs Work
• IT Infrastructure Management Explained
• Building an IT Network for a Remote Facility
• Network Engineer vs Network Administrator: Roles, Responsibilities & Job Descriptions
• The Empowered Edge: An Introduction

Database as a service (DBaaS) is one of the fastest growing cloud services—it’s projected to reach $320 billion by 2025. The service allows organizations to take advantage of database solutions without having to manage and maintain the underlying technologies.

DBaaS is a cost-efficient solution for organizations looking to set up and scale databases, especially when operating large-scale, complex, and distributed app components.

In this article, we will discuss Database as a Service, how it works, and its benefits to your organization from both technology and business perspectives.

What is Database as a Service?

Database as a Service is defined as:

“A paradigm for data management in which a third-party service provider hosts a database and provides the associated software and hardware support.”

Database as a Service is a cloud-based software service used to set up and manage databases. A database, remember, is a storage location that houses structured data. The administrative capabilities offered by the service includes scaling, securing, monitoring, tuning and upgrading of the database and the underlying technologies, which are managed by the cloud vendor.

These administrative tasks are automated, allowing users to focus on optimizing applications that use database resources. The hardware and IT environment operating the database software technologies is abstracted. Users don’t need to focus their efforts on the database implementation process itself. The service is suitable for:

• IT shops offering cloud-based services
• End users such as developers, testers, and DevOps personnel

How DBaaS works

Depending on the service, the DBaaS service can be a managed front-end SaaS service or a component of the comprehensive Infrastructure as a Service (IaaS) or Platform as a Service (PaaS) stack.

Here’s how a typical DBaaS, as part of the IaaS, works:

Initial setup

The first step involves provisioning a virtual machine (VM) as an environment abstracted from the underlying hardware. The database is installed and configured on the VM.

Depending on the service, a predefined database system is made available for end users. Users can access this database system using an on-demand querying interface or a software system. Alternatively, developers can use a self-service model to set up and configure databases according to a custom set of parameters.

Operation

The DBaaS platform handles the backend infrastructure and operations. Database administrators (DBAs) can use simple click-on functionality to configure the management process. These include, but aren’t limited to:

• Monitoring
• Upgrades and patches
• Disaster recovery
• Security

Scaling

The DBaaS platform scales the instances according to the configuration and policies associated with the managed database systems.

For example, for disaster recovery use cases, the system replicates the data across multiple instances. The building blocks of the underlying components, such as a server resource, are controlled by the platform and rapidly provisioned for self-service applications of database deployment.

Without a managed database service or a DBaaS, you’ll have to manage and scale hardware components and technology integrations separately. This limits your ability to scale a database system rapidly to meet the technology requirements of a fast-paced business.

Benefits of database as a service in cloud computing

DBaaS technology saves valuable resources on setting up and managing database systems and the IT environment. The technology reduces the time spent on the procedure from weeks and days to a matter of a few minutes. This is especially true for self-service use cases in DevOps environments that require rapid and cost-effective operations capabilities for their IT systems.

From a business perspective, the DBaaS technology offers these benefits:

• High quality of service. Cloud vendors manage database systems as part of a Service Level Agreement (SLA) guarantee to ensure that the systems are running to optimal performance. These guarantees also include compliance to stringent security regulations. The service availability is managed by the cloud vendor to high standards as per the SLA agreement.
• Faster deployment. Free your resources from administrative tasks and engage your employees on tasks that lead directly to innovation and business growth—instead of merely keeping the systems running.
• Resource elasticity. The technology resources dedicated for database systems can be changed in response to changing usage requirements. This is especially suitable in business use cases where the demand for database workloads is dynamic and not entirely predictable.
• Rapid provisioning. Self-service capabilities allow users to provision new database instances as required, often with a few simple clicks. This removes the governance hurdles and administrative responsibilities from IT.
• Business agility. Organizations can take advantage of rapid provisioning and deployment to address changing business requirements. In DevOps organizations, this is particularly useful as Devs and Ops both take on collective responsibilities of operations tasks.
• Security. The technologies support encryption and multiple layers of security to protect sensitive data at rest, in transit and during processing.

Drawbacks of database as a service architecture

While Database as a Service (DBaaS) offers solid advantages, you may need to make some tradeoffs and compromises. Outsourcing this critical part of your infrastructure has several potential negatives:

• Cost may be higher: While you don’t have to spend upfront on capital or hire staff, the costs of subscribing to the service and transferring data adds up over time.
• Lack of control: Depending on your provider, you may not be able to customize performance settings, dig into database functions, or fine-tune configurations.
• Dependence on high-speed internet: Your data operations in the cloud require a fast internet connection without latency, bandwidth issues, or the potential for outages.
• Security concerns: Data stored off-site may be exposed to greater risk. Most DBaaS vendors have strong cybersecurity, but your data may face data sovereignty or access control issues. You may not have full control in complying with security frameworks like HIPAA and GDPR.
• Vendor lock-in: Once you have set up databases and applications within a vendor environment, changing to another platform can be complex, costly, and time-consuming. You may have lower negotiating power, which sacrifices agility.

What to look for in a DBaaS

Choosing the right DBaaS platform and vendor is a matter of finding the best options for your data strategy, performance requirements, and expectations for the future. Consider what kinds of workloads need support and the analytics tools you will use. You will want flexibility in deployment and licensing. Below are some of the most important capabilities to consider.

DBaaS deployment options

Look for providers that offer multiple deployment models, from a fully managed cloud to compatibility with sensitive data kept on premise to a hybrid approach. You may want a multi-cloud environment or a private cloud, depending on regulatory requirements and your operations.

Licensing flexibility

Avoid budget and operational limitations with usage-based licensing that scales as your workloads change. You may want to consider pay-as-you-go or a subscription model. You should have the flexibility to switch tiers without heavy penalties.

Data lake capability

Given the growing use of data lake architectures, your vendor should be able to integrate with them for ingesting, storing, and analyzing diverse data types. Ideally, your DBaaS provider can handle large volumes of structured and unstructured data for traditional analytics and AI/ML workloads, all in a single environment.

Ability to optimize

You will need more than basic provisioning, with tools that empower you to optimize your data operations. Your provider should offer tools for performance tuning, workload balancing, and intelligent automation. Features like auto-scaling, indexing recommendations, and resource optimization help with uptime and performance.

Depth of analytics

Storage is just the beginning of what a DBaaS should offer — analytics is where the value truly lies. Look for a rich analytics layer that allows you to extract as much insight as possible. The best vendors offer native integration with BI tools, complex query support, and capabilities such as machine learning, predictive modeling, and in-database processing.

Database as a service

Database as a service is just one more “as a service” offering that can bring agility, flexibility, and scaling to any business, no matter your size or industry.

Related reading

• BMC Multi-Cloud Blog
• Data Storage Explained: Data Lake vs Warehouse vs Database
• DBMS: An Intro to Database Management Systems
• MongoDB Guide, a tutorials series
• Data Visualization Guide, a tutorials series
• State of the Cloud in 2021

With the growing complexity of application development, organizations are increasingly adopting methodologies that enable reliable, scalable software.

DevOps and site reliability engineering (SRE) are two approaches that enhance the product release cycle through enhanced collaboration, automation, and monitoring. Both approaches utilize automation and collaboration to help teams build resilient and reliable software—but there are fundamental differences in what these approaches offer and how they operate.

So, this article delves into the purpose of DevOps and SRE. We’ll look at both approaches, including benefits, differences, and key elements.

(This article is part of our DevOps Guide. Use the right-hand menu to navigate.)

• Basics of DevOps
• Site reliability engineering (SRE) basics
• Differences between SRE and DevOps
• SRE vs. DevOps similarities
• How SRE supports DevOps
• Summing up DevOps vs. SRE

DevOps basics

DevOps is an overarching concept and culture aimed at ensuring the rapid release of stable, secure software. DevOps exists at the intersection of Agile development and Enterprise Service Management (ESM) practices.

Early methodologies involved development and operations teams working in silos, which led to slower development and unstable deployment environments. To solve this, the DevOps methodology integrates all stakeholders in the application into one efficient workflow, which enables the quick delivery of high-quality software.

By allowing communication and collaboration between cross-functional teams, DevOps also enables reliable service delivery and improved customer satisfaction.

DevOps practices & methods

DevOps practices are based on continuous, incremental improvements bolstered by automation. While full-fledged automation is rarely possible, DevOps methodology focuses on the following elements:

• Continuous delivery and integration (CI/CD). Using CI/CD pipelines, you can seamlessly connect processes and practices while using automation for fast and frequent updates and code releases. You also have continuous monitoring and deployment for consistent code across software versions and deployment environments.
• Infrastructure as code (IaC). With the abstraction of IT infrastructure, you can manage software engineering and provisioning automatically so your team efficiently tracks changes, monitors infrastructure configurations, and can roll back changes if there are undesired or unintended effects.
• Automated testing. Code is automatically and continuously tested while it is being written or updated. By eliminating the bottlenecks associated with pre-release testing, the continuous mechanism speeds up deployment.
• Framework integration. DevOps works with frameworks that enable comprehensive automation, along with faster delivery, efficiency, and enhanced collaboration. DevOps is ideal with the Scrum framework that designates project roles and defines workflows, the Kanban framework for workflow management, and Agile for rapid, frequent, and iterative updates through flexible and shorter development cycles.

Site reliability engineering (SRE) basics

SRE provides a unique approach to application lifecycle and service management by incorporating various aspects of software development into IT operations.

SRE was first developed in 2003 to create IT infrastructure architecture that meets the needs of enterprise-scale systems. With SRE, IT infrastructure is broken down into basic, abstract components that can be provisioned with software development best practices. This enables teams to use automation to solve most problems associated with managing applications in production.

SRE uses three Service Level Commitments to measure how well a system performs:

• Service level agreements (SLAs) define the required reliability, performance, and latency of the system as desired by end users.
• Service level objectives (SLOs) target values and goals set by SRE teams that should be met to satisfy SLAs.
• Service level indicators (SLIs) measure specific metrics and aspects that show how much a system conforms to the SLOs. Typical SLIs include request latency, system throughput, lead time, development frequency, mean time to restore (MTTR), and availability error rate.

Key principles of SRE include:

What does a Site Reliability Engineer do?

SRE essentially creates a new role: the site reliability engineer. An SRE is tasked with ensuring seamless collaboration between IT operations and development teams through the enhancement and automation of routine processes. Some core responsibilities of an SRE include:

• Developing, configuring, and deploying software to be used by operations teams
• Handling support escalation issues
• Conducting and reporting on incident reviews
• Developing system documentation
• Change management
• Determining and validating new features and updates

Differences between SRE and DevOps

Both methodologies enforce minimal separation between development and operations teams. But DevOps focuses more on a cultural and philosophical shift, while SRE is more pragmatic and practical.

Essence

SRE is a technical, software-first set of scalable tools for reliably producing applications. It is focused on creating a set of practices and metrics that improve collaboration, system reliability, and service delivery. SRE measures reliability, using SLOs to sustain performance.

Rather than practical tools, DevOps is the collection of philosophies that create a mind-set and culture for breaking down silos to promote different teams working together seamlessly. It seeks specifically to connect development and operations to collaborate and automate, leading to higher quality, faster releases and reduced failures.

Goal

Both SRE and DevOps aim to bridge the gap between development and operations, but their goals are different.

SRE seeks to improve reliability, stability, and performance through frequent changes. It prescribes approaches that balance reliability against innovation, use automation, and offer tools for proactive problem-solving.

DevOps has a goal of speed, with rapid, continuous updates done through an efficient approach built on automation, collaboration, and built-in testing and monitoring.

 Focus

SRE mainly focuses on enhancing system availability and reliability. The framework enforces reliability standards, analyzes failures, and develops ways to prevent issues that recur.

DevOps focuses on speed of development and delivery while enforcing continuity. Developers and operations share the responsibility to monitor, test, and deliver software continuously.

Team structure

An SRE team is composed of site reliability engineers who have a background in both operations and development. They are, however, separate from development teams, working in a support function.

DevOps teams include a variety of roles, such as QA experts, developers, engineers, SREs, and many others. They work together through the entire software lifecycle, looking for ways to enhance teamwork and automate workflows.

Process flow

Because of its focus on system reliability, the process flow for SRE starts with defining reliability goals and objectives, then monitors and observes, reporting incidents as they happen and responding. SRE process flows include error budgeting, where if reliability slips, new features are delayed until stability can be reestablished. Automating tasks reduces manual work. The final steps in a SRE workflow are running postmortems and learning from failures, as well as planning for capacity needs to prevent future failures.

DevOps focuses on integration, collaboration, and continuous deployment and drives an inclusive process. It begins with planning features and the system design, and then moves quickly into developing test code and using CI/CD pipelines to automate builds and code integration. Testing for quality leads to release into production and deployment. Monitoring systems produce data and insights that feed continuous improvement.

Tools

In some cases, SRE and DevOps use the same tools. For version control, both use Git, GitHub, GitLab, and Bitbucket. For CI/CD, both use Jenkins, GitHub Actions, GitLab, and CI. DevOps also uses Azure DevOps Pipeline. For containerization, both use Kubernetes, with DevOps also using Docker.

DevOps uses Terraform, Ansible, Chef, and Puppet for configuration and IaC. Prometheus, Grafana, ELK, and Datadog are DevOps monitoring and observability tools. It uses PagerDuty and Opsgenie for incident management.

SRE also uses Terraform for configuration and Iac, adding Helm and Kustomize. It uses Kubernetes and Istio for containerization. SRE uses the same monitoring and observability tools as DevOps, adding also OpenTelemetry and Sentry. Incident management is done with PagerDuty, VictorOps, and Blameless. SLIs and SLOs are the key to error budgeting and reliability.

Similarities between SRE and DevOps

While the differences between SRE and DevOps are many, so are the similarities:

• Both seek to deliver reliable software more quickly and make use of automation.
• Automation and continuous improvement are supported.
• The frameworks both break the siloes of operations working separately from development.
• Both include monitoring as a key workflow step in improving software.
• Incident responses feed continuous improvement.
• IaC is a shared concept for managing and scaling environments.

How SRE supports DevOps principles & philosophies

SRE and DevOps are not competing methodologies. That’s because SRE provides a practical approach to solving most DevOps concerns.

In this section, let’s explore how teams use SRE to implement the principles and philosophies of DevOps:

Reducing organizational silos

DevOps works to ensure that different departments/software teams are not isolated from each other, ensuring they all work towards a common goal.

SRE enables this by enforcing the ownership of projects between teams. With SRE, every team uses the same tools, techniques, and codebase to support:

• Uniformity
• Seamless collaboration

Implementing gradual change

DevOps embraces slow, gradual change to enable constant improvements. SRE supports this by allowing teams to perform small, frequent updates that reduce the impact of changes on application availability and stability.

Additionally, SRE teams use CI/CD tools to perform change management and continuous testing to ensure the successful deployment of code alterations.

Accepting failure as normal

Both SRE and DevOps concepts treat errors and failure as an inevitable occurrence. While DevOps aims to handle runtime errors and allow teams to learn from them, SRE enforces error management through Service Level Commitments (SLx) to ensure all failures are handled.

SRE also allows for a risk budget that allows teams to test the limits of failure for reevaluation and innovation.

Leveraging tools & automation

Both DevOps and SRE use automation to improve workflows and service delivery. SRE enables teams to use the same tools and services through flexible application programming interfaces (APIs). While DevOps promotes the adoption of automation tools, SRE ensures every team member can access the updated automation tools and technologies.

Measuring everything

Since both DevOps and SRE support automation, you’ll need to continuously monitor the developed systems to ensure every process runs as planned.

DevOps gathers metrics through a feedback loop. On the other hand, SRE enforces measurement by providing SLIs, SLOs, and SLAs to perform measurements. Since Ops are software-defined, SRE monitors toil and reliability, ensuring consistent service delivery.

Summing up DevOps vs. SRE: Which is better?

In comparing SRE and DevOps, neither is better than the other. In fact, they can work together: 50% of companies using DevOps for speed and efficiency have also adopted SRE to improve reliability.

SRE offers tools and techniques that complement DevOps philosophies and practices. SRE applies software engineering principles to managing system reliability and scaling operations. DevOps brings a collaborative philosophy and structured approach for delivering software quickly and efficiently.

The goal of both methodologies is to enhance the IT ecosystem. DevOps improves the application lifecycle, and SRE improves the operations.

“Gain insight to the capabilities necessary to attract top SRE talent and make them successful in your organization with artificial intelligence for operations (AIOps) and artificial intelligence for service management (AISM) capabilities.”

Related reading

• BMC DevOps Blog
• The Complete DevOps Certifications Guide
• The State of DevOps: A Report Roundup
• SRE vs ITOps: Are SRE & IT Operations The Same Thing?
• The State of SRE Today
• Implementing GitOps To Deliver Cloud NativeApplications

Data architecture is a framework for how IT infrastructure supports your data strategy. The goal of any data architecture is to show the company’s infrastructure, including how data is acquired, transported, stored, queried, and secured.

Data architecture is the foundation of any data strategy.

AI technology is radically changing data infrastructures, specifically data architecture and strategies for handling data. Data architecture defines how your organization captures data, how it’s stored and managed, and how that data is used. AI applications demand better ways to handle massive volumes of data, as well as increases in computational capacity.

To handle sophisticated AI applications, your data infrastructure must support agility, both for rapidly changing business demands and to handle the fast pace of AI innovation. Your data architecture has to be highly efficient, resilient, and strong, and it must also offer scalability.

How can you achieve these requirements?

In this article, we’ll look at:

• Data architecture definition
• Architecture components
• Common data architecture frameworks
• Data standards
• The shift to new architecture

Let’s get started.

What is data architecture?

Data architecture is the structure and organization of how you acquire data, store it, and manage it, and ultimately how your systems access and use it. Data architecture components include data models, rules and policies, data access and security technologies, and analytical processes and outputs.

Data architecture resolves the “how” for implementing your data strategy.

Data architecture examples

Different data architecture examples include:

• Storing a file as a .csv on a local hard drive and reading the file into Tableau on a person’s computer for analysis.
• Streaming data from a set of point-of-sale registers to accounting.
• Accumulating data in a large-scale data lake and then using big data tools like Spark or Hadoop to process and analyze it.
• Capturing data and placing it where it can be managed by various business units on one platform.
• An enterprise data architecture combines everything from .csv files to data lakes and warehouses to streaming data, using data integration frameworks and business intelligence tools.

Why is data architecture important?

The data architecture is 100% responsible for increasing a company’s freedom to move around the world.

If agility is what is needed to avoid collapse during slow seasons or to capitalize on the spontaneous popularity of a new product, the more advanced the data architecture is, the more capable the company is to take action.

Explicitly, data architecture is important because it:

• Gives a fuller picture of what is happening in the company
• Creates a better understanding of the company’s data
• Offers protocols by which data moves from its source to being analyzed and consumed by its destinations
• Ensures a system is in place to secure the data
• Grants all teams the ability to make data-driven decisions

Key components of data architecture

The architectural components of today’s data architecture world are:

• Data pipelines: Refers to the methods used to bring raw data into a data store, typically with some transformation or processing.
• Cloud storage: This model for gathering and keeping data relies on remote devices that you can access via a network.
• Application programming interfaces (APIs): This set of rules provides existing functions for connecting to, communicating with, and sharing among software.
• AI & ML models: These sets of programs find patterns in data to make decisions or predictions to solve tasks.
• Data streaming: Refers to continuously transferring data from its source or sources for use in processing into outputs.
• Kubernetes: This open-source system automates deploying, scaling, and managing applications in containers for efficiency.
• Cloud computing: Involves providing computing services on remote devices that are accessed and managed over the internet.
• Real-time analytics: Uses data, software, and hardware to analyze data as soon as it is generated.

Common data architecture frameworks

A data architecture framework is a structured approach to defining your data strategy, including how to organize data, process it, analyze it, and document it.

• The Open Group Architectural Framework (TOGAF): A modular approach for creating a hierarchy and content framework that eliminates redundancy and inefficiency while boosting data usability.
• Data Management-Body of Knowledge (DAMA-DMBOK2): Applies best practices for data governance, quality, and security.
• The Zachman Framework: Provides a logical matrix structure to support both automated and manual systems for aligning the IT department with business goals.

What are data standards?

Data standards are the overarching standards of a data architecture, which you apply to areas such as data schemas and security.

Data schemas

A data schema defines how you organize data within a database, including specifying its format, relationships, and standards for storage and access. The data schema spells out:

• Each entity that should be collected. The Schema for contact info, for example, might include name, phone number, email, and place of work.
• The type of data each piece should be. For example, name is text data, phone number is integer data, email is text data, and place of work is text data.
• The relationship of that entity to others in the database, such as where it comes from and where it’s going.

Most companies update their data schema around changing business needs, applications, and data models. As data becomes increasingly pervasive, companies are shifting away from on-premise databases to scalable cloud-native relational databases.

You can easily add data and combine data from a network of data sources into today’s relational (NoSQL) databases without being restricted to a fixed hierarchy. Plus, these relational databases can grow much larger and handle adding data dynamically through integrations with analytics tools that are not possible with traditional SQL databases.

Updating and modifying your data schema, or “versioning” it, is vital. Versioning the data schema helps standardize what to find, where, and the ability to ask when a data set was in a location.

(Explore data storage from database to warehouse to lake and from hot to cold.)

Data security

Data standards also help set the security rules for the architecture. These can be visualized in the architecture and schema by showing what data gets passed where, and, when it travels from point A to point B, how the data is secured.

Security protocols can include:

• Encrypting data during travel
• Restricting access to individuals
• Anonymizing data to decrease the value of the information upon receipt by receiving party
• Additional actions

Shifting to new architecture

AI is driving data architecture trends, reflecting the need for processing data in real time, handling massive volumes of data from diverse sources in a multiplicity of formats, and supporting highly sophisticated queries and analytics. Trends include:

• Decentralizing data management and moving away from centralized data warehouses or even data lakes to domain- or department-specific data collections, all managed on a single platform.
• Unifying data integrations, sometimes called data fabric, using AI and automation to connect data across platforms in hybrid or multi-cloud environments.
• Processing in real-time, or ongoing streaming, to support applications like fraud protection, the function of IoT, and running AI.
• Driving data management decisions with AI at the center to automate the basics of governance, quality checking, and optimization.
• Using distributed databases and multiple models to ensure global scalability with high failover resilience.
• Designing for cybersecurity and compliance with various frameworks and regulations in mind.

When thinking about anything related to data — which is arguably everything — you should always consider the data architecture.

Related reading

• BMC Business of IT Blog
• BMC Machine Learning & Big Data Blog
• BMC Guides, offering multi-part tutorials on a variety of data products
• Data Ethics for Companies
• 3 Keys to Building Resilient Data Pipelines
• 5 Questions to Ask About Data in Your SaaS Environment