Chrissy Kidd – BMC Software | Blogs

Securing Data Centers & Server Rooms: Goals & Best Practices

Chrissy Kidd — Fri, 11 Feb 2022 00:00:32 +0000

Information security is a critical topic in the enterprise IT industry, especially when mission-critical data is stored in cloud data centers off-premises. Indeed, cybercrime is the most pressing concern since 79% of the organizations using cloud computing services have experienced a cloud-related data breach incident according to a recent survey.

However, there’s more to security of the cloud data center than defending against the prevalent cybercrime attack vectors. Physical security of hardware assets is equally critical for secure and dependable operations of a cloud data center.

In this blog, we will discuss what the physical security of a cloud data center entails, the applicable industry standards, and industry-proven best practices to secure your cloud data center resources.

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

Security controls for data centers & server rooms

Cloud data center and server room security controls encompass four key aspects of the data center:

Physical security. The security of static systems that constitute a data center. The building structure, hardware resources, utility services infrastructure, as well as portable and mobile objects that constitute a data center facility are included. The characteristics of these systems determine the security and physical threats facing a data center including fire, unauthorized access, work conditions for the workforce, and dependability.
Geographic characteristics. The location of a data center determines the natural threats such as earthquakes, flooding, and volcanic eruptions. Additionally, human-responsible threats such as burglary, civil disorders, interruptions, damages, and interceptions are also highly dependent on the location of the data center facility.
Supporting facilities. Refers to the services necessary for smooth data center operations. These facilities include the infrastructure of utility services such as energy, water, cooling, communication, and air conditioning. Emergency services including firefighting, policing, and emergency healthcare also impact the risk mitigation capacity of a data center.
Future prospects. Economic, political, geographic, and demographic factors affect how well a location is suitable for data center operations over the long term. The services and facilities available to your server room may suffice for now but does the location offer sufficient capacity, services, and facilities to scale in the future?

Securing your server room

The first step to securing a server room is to design one that is fully compliant to the leading industry standards. Organizations such as the National Institute of Standards and Technology (NIST) as well as government regulatory authorities provide guidelines, standards and frameworks that encompass all aspects of server room security: physical, environmental and information security.

Some of the common server room security standards and framework guidelines include:

ISO 27001
ISO 20000-1
SSAE 18 SOC 1 Type II, SOC 2 Type II and SOC 3
NIST SPs (including SP 800-14, SP 800-23, and SP 800-53)
Department of Defense (DoD) Information Assurance Technical Framework

Server room best practices

Server room security is an ongoing process. The security frameworks provide guidelines to maintain server room security in context of changing external circumstances and the scale of IT operations. Once a data center room is designed in compliance with the applicable standards, the next steps involve a range of controls that can help mitigate threat vectors ranging from human risks to threats from natural disasters.

The following best practices and security controls can help you get started with data center security:

Restricted access & multi-layer authentication

Only the authorized personnel should be allowed to enter (and exit) the premises. Multiple layers of security—passwords, RFID tags, and biometrics—can be combined to enforce implementation.

Server systems should be isolated such that the principle of least privilege access can be adopted, ensuring that damage can be contained within isolated sections of the data center when compromised.

(Read about zero trust network access.)

Fire safety & HVAC

Fire incidents, explosions, and inadequate HVAC affect the dependability of a server room. These incidents can leave irreversible damages to a server room, especially when the stored data is not adequately duplicated. Consequently, it’s important to evaluate the safety capacity of the building against these risks.

Adopt fire detection and control systems, automate emergency service routing and limit building occupancy. Data center efficiency is highly dependent on the HVAC systems. An effective server room design considers all aspects of ventilation, including damage limitation in event of a fire incident.

Building structure & utility infrastructure capacity

The hardware racks and building structure should be highly capable of supporting heavy hardware devices. Access to these devices should be convenient and systematic: troubleshooting, repairs, and upgrades should take minimal time and effort. The utility infrastructure that powers HVAC systems should be designed for:

High capacity
Structural integrity
Long life

Information security

Physical security of a server room also impacts the ability to secure information stored within the server systems. If the data is encrypted, it will remain secure even when the storage devices in the server room are compromised.

Similarly, the server systems should be designed for redundancy. If one device is no longer operational or is compromised, the stored data should be accessible through alternative and redundant storage devices.

(Learn more about data center redundancies.)

Emergency services

In event of a security breach or emergency incident, access to emergency services—police, healthcare, and firefighting services—should be automated and highly available. Deploy automated technology systems to inform the appropriate emergency services in event of an incident and engage with private security services to enhance building security.

Securing server rooms is critical business

Securing server rooms is an absolute necessity. It is not a cheap endeavor (would you want cheap security?), so you’ll have to find some custom balance of security, accessibility, and cost. Leadership may be hesitant to invest in server security, but by knowing that something will go wrong, it’s just a matter of when, you can choose to be on the offense instead of on the defense.

A good tenet of server room security: the more you control, the more secure your servers will be.

IT Director Requirements, Skills & Salaries

Chrissy Kidd — Fri, 28 Jan 2022 00:00:56 +0000

Organizations of all shapes and sizes rely on IT for practically every part of their operations. As business becomes more connected and digitized, it is crucial that companies, no matter the industry, entrust the supervision of the IT department to an experienced and knowledgeable professional: the IT director.

Given the high-impact role, what types of skills might a qualified IT director need to possess in order to meet the expectations of the position? What types of requirements might an organization ask for when searching to fill the role? And what salary should a potential candidate look for when applying?

We have answered all of these questions and more to provide the best background possible about IT director requirements, skills, and salaries.

What is an IT Director?

Nearly every sizable organization has at least one IT director, with many companies even having more than one. Depending on the scale and purpose of a company, the role of the director of technology can vary greatly—the larger the company, the more IT directors there may be.

For the IT director, some responsibilities may include overseeing the infrastructure of technical operations, managing a team of IT employees, and tracking technology in order to:

Achieve business goals
Minimize security risks
Increase user satisfaction
Maintain operations and systems

There are a variety of names this role might be listed as, including Director of Technology, Senior IT Director, Director of Information Technology. Generally, all of these names describe the same general set of job requirements.

Roles & responsibilities

Directors of IT play a crucial role in their companies’ overarching IT management, and they are often responsible for the broad maintenance of the functionality, security, and accessibility of all computer resources within the organization. This includes, but is not limited to:

Networking equipment
Computer hardware and software
Data storage infrastructure
Web applications, including SaaS, PaaS, and IaaS

Although this doesn’t cover them entirely, and every role will be different depending on the specific industry and company the position oversees, some other general responsibilities that a director of technology might expect include:

Developing and overseeing SMART goals for hardware, software, and storage
Ensuring strategic capacity planning
Managing all or part of the IT department, including:
- Directly supervising some employees
- Hiring certain members
- Handling employees’ concerns and performances
- Communicating with the technology team and other departments as collaboration requires

Determining business requirements for IT systems
Identifying and eliminating security vulnerabilities with strategic solutions that increase data security
Directing and supporting the implementation of new software and hardware
Identifying and recommending new technology solutions
Managing the organization’s help desk (internal, external, or both)
Coordinating IT activities to ensure data availability and network services with as little downtime as necessary
Overseeing departmental finances, including budgeting and forecasting
Implementing executive policies
Reporting back to the C-suite

IT directors often work closely with the chief technology officer (CTO) and other executives to ensure that all business-critical systems are operating smoothly.

IT director skills needed

The IT director will need to have a balance of internal and external perspective:

Looking inwards towards their team and responsible technologies
Looking outwards to understand the unique business needs across various departments within the company

As such, the necessary professional skills require wide breadth, perhaps more than expertise in a one single area. The skills needed to be a successful IT director include:

Technical skills

Although the IT director might not be the person directly maintaining or fixing the systems, it is going to be important for them to have technical skills and knowledge in order to understand what is going on within the infrastructure.

Communication skills

Given the fact that the IT director serves as a go-between for the senior-level executives and the IT department, having strong communication skills will be crucial, both written and verbal. This role also requires the need to cooperate and collaborate with employees, with both technical and non-technical colleagues.

(Explore other soft skills useful in tech work.)

Leadership skills

As a leader of multiple teams and employees, it goes without saying that leadership skills are necessary in order to be a successful IT director. These skills are going to be needed to not only motivate teams, but to help move the department towards its goals.

(Explore leadership skills for tech roles.)

Analytical skills

Having an analytical mindset to develop and utilize reliable metrics will help the qualified director of IT to generate solutions to a wide array of technology-related problems. The director must then be able to:

Take their analysis
Research the best solutions for it
Come to a final decision on the best way to solve it

(Learn more about data analytics.)

Organizational skills

Staying organized and focused are more skills that are going to be necessary for any interested IT director. Being able to coordinate the work and schedules of many people across numerous departments requires someone who can multi-talk and juggle multiple responsibilities at a time. These tasks are to be completed while the director also manages their own workload and priorities.

Business skills

There are many parts of an IT director’s job that do not include technical aspects, but rather, non-technical components that relate to business in general, including financial skills like budgeting, forecasting, and justifying.

By being familiar with business skills and having a managerial role within the company, the director of IT can better help to develop and implement plans to achieve the organization’s tech goals.

(Explore our IT Cost Management Guide.)

IT director education & requirements

Common requirements for a director of technology position include:

A bachelor’s degree in programming, computer science, computer engineering, or another related field with advanced course experience in mathematics, computer programming, and software development
Several years’ experience managing employees within an IT environment
Several years’ experience working with particular systems that are relevant to the company (For instance, EMR/EHR systems in healthcare technology, or finance-specific databases for mutual funds and banking institutions)

Due to the complex nature of this senior position, many larger organizations may require their director of IT to hold a graduate degree, such as an MBA or an MS in information technology. Both of these will only enhance the director’s knowledge base, not to mention increase their abilities to manage and oversee large teams of people.

It is not typically required for IT directors to be an expert in multiple programming languages or certified in every network, but instead they must possess a broad understanding of tech theories and applications from a macro-level. It is also important to understand new trends and shifts in technology, considering what may benefit the IT department while balancing the organization’s business needs and budget.

Peers & reporting

Looking up the career ladder, a director of technology often reports directly to the Chief Technology Officer (CTO), providing updates and requesting resource support for the entire technology team that the director oversees.

Within the role, the director will likely oversee one or several IT teams and may work alongside several other IT directors, all with responsibilities around various technologies and team functions.

Just as important as who the director of technology reports to is who the director of technology oversees and leads. While a director’s role seems to cover a lot of systems, it’s really the people that the director oversees. A director of technology is likely responsible for answering these questions:

Are individual IT teams achieving their goals?
Are the teams having issues bringing their product or solution to the finish line?
Are other departments supporting the IT department in providing the necessary support, resources, infrastructure, etc.?

The ways these questions will be answered depend on a lot of factors including the size of the company, the technologies deployed, the overarching philosophy of tech within the enterprise, and the scope of the director of technology.

Of course, the organization’s industry will have an impact on the job itself, as well. Education, government, non-profit, and healthcare sectors combined comprise nearly one-third of the director of technology positions nationwide. Smaller percentages go to financial, business, and software services, respectively.

Professional development for IT directors

It’s smart for leaders to partake in professional development opportunities in order to stay abreast of the latest trends, emerging management theories, and how innovation is changing the field.

A few different options for continued education include certifications for IT directors. These certifications can help directors to not only be more effective in their role, but to be more competitive in the job market, as well.

CompTIA A+ Certification

Offered by the Computing Technology Industry Association (CompTIA), this certification is seen as an industry standard for IT professionals. It helps candidates troubleshoot and solve technical problems, as well as teaching how to understand a variety of issues, from operating systems and networking, to mobile devices and security.

CompTIA Network+

This certification from CompTIA also verifies that candidates have the necessary skills to design, configure, maintain, and troubleshoot wired and wireless devices.

CompTIA Security+

This certification covers best practices for both IT networks and operational security, verifying that the candidate has the required skills to keep an organization’s data secure.

Continuous learning

Remember, though, that professional development doesn’t have to be formal. Some of the best ways to continue developing as a director are to stay curious and to partner with a mentor.

Staying curious. This can be as simple as a reading up on trade magazines or business journals that illustrate innovations in IT and cross-sectional fields. Another option is to attend conferences within the field that can provide new networking opportunities as well as providing information on the latest trends and technologies.
Seeking a mentor. A mentor could be someone who shows other ways of doing things, based on his or her own experience. This person could be more senior directors within the IT department, non-IT directors within the company, and even directors or executives outside of the company altogether. Their experiences can display different approaches to IT and management thinking. Plus, their advice can provide support should professional roadblocks occur.

Salary & job outlook

Despite all of the necessary skills and required responsibilities, IT directors in the U.S. are highly compensated, with the median salary across private, government, and non-profit sectors clocking in at just above U.S. $142,500.

The U.S. Bureau of Labor Statistics (BLS) projects that the demand for directors of technology will grow about 12% by 2026, much faster than the national average of all occupations (average 8%). This increased demand for such candidates stems from the growth into digital platforms that nearly all businesses will adopt, especially the expedited digital transformations companies have been taking as a result of the pandemic. In addition, they’ll need directors and managers to implement these growth goals.

Summing up IT directors

IT directors must possess a wide variety of skills, from technical know-how and knowledge to communication and leadership. This role encompasses many responsibilities and requirements that will only continue to become more complex as technology evolves, especially given the latest push at strong digital initiatives.

With the right person, this role will prove to have a major impact on the IT department, and the organization as a whole.

Bi-Modal IT: An Introduction

Chrissy Kidd — Fri, 28 Jan 2022 00:00:42 +0000

Bimodal IT refers to the management practices that enable users to explore opportunities for improvement and exploit optimal strategies to deliver the best results within the applicable constraints. The practice of Mode 1: Exploration and Mode 2: Exploitation are both adopted independently and optimized for the challenges and opportunities facing IT-driven organizations.

Gartner, a leading IT research and advisory firm, introduced the concept in 2014.

In this article, we’re taking a look at the concept of Bimodal IT, its goals, and its benefits so you can consider whether it’s the right fit for your team.

Two modes of Bimodal IT

IT technologies and processes are, in general, classified as legacy and modern.

Legacy systems and techniques are well understood, they enable predictable operations and present little risk of change.
On the other hand, modern systems and methodologies offer new opportunities, promise efficiency improvements and operational excellence.

In an enterprise IT environment, organizations maintain both the legacy and modern IT environments. The underlying motivation of adopting separated bimodal management practices for the two contrasting mechanisms of enterprise IT is that there is no single optimal approach to managing both. Therefore, Gartner proposes the following two modes of bimodal IT:

Mode 1: Exploitation

The Mode 1 of exploitation focuses on and optimizes areas of IT that are generally predictable and understood and may require a high level of security and safety. This mode may include the traditional internal, back-office workings of IT such as:

Securing systems and servers
Storing sensitive data

These are operational use cases where organizations face limited flexibility to modernize applications, systems, and processes from their current legacy state. At the same time, they also fully understand the internal workings of the systems and risks involved in adopting specific changes.

Therefore, organizations can potentially optimize their approach to exploit and modernize specific components of their legacy environment. This management practice will involve calculated risks. Organizations will be well aware of the risks—such as cost, security, and performance—and can devise a risk mitigation strategy to eliminate possible uncertainties.

Mode 2: Exploration

Mode 2, in contrast, is focused on optimizing IT areas that are uncertain, experimental, or exploratory. This mode often comprises the development of external consumer technology and is often correlated with agile and DevOps cultures.

An important characteristic of practices suitable for Mode 2: Exploration management practice is the focus on innovation. Bimodal capabilities enhance the ability of organizations to focus product development and improvements in areas that present low risk in the event of change; or unprecedented business opportunities through (digital) transformation and change.

In the latter case, high risk is naturally attributed to change. Therefore, Mode 2 encourages risk mitigation strategies such as short and iterative change that creates and sustains value over the long term.

An example is the use of the Minimal Viable Product (MVP) for product development. Instead of investing significant capital into developing new products, this strategy recommends testing a hypothesis with a small working solution in its most basic and simplistic form. Based on user feedbackv, this model is upgraded iteratively until it has transformed into a market-ready product. As the stage is reached following a journey of iterative improvements based on end-user feedback, Mode 2 management practice of exploration also mitigates the risk while guiding innovation in the correct direction.

(Read more about application & software modernization.)

Bimodal IT stats

Since most organizations are already running IT workloads and apps in the cloud, together with legacy IT environments, the Bimodal IT framework is relevant for most business organizations.

Recent research finds that 81% of all organizations have already adopted a multi-cloud strategy. 84% of the organizations already operate a hybrid mix of legacy and modern cloud-based infrastructure systems. For these organizations, it makes sense to run parallel tracks of a Bimodal IT strategy where:

Mode 1 deals with systems of records
Mode 2 deals with Systems of Innovation
Both modes are optimized to run systems of differentiation.

(Source)

Benefits of Bimodal IT

Industry experts agree that this shift exists. It began several years ago, and it is amplified today.

Detangling the two areas of IT does make sense in a lot of ways. Legacy systems often are responsible for the most critical business needs, like internal networks, and securing sensitive data as needed for accounting, HR, finance, etc. These areas rely on the safe environments of legacy systems. Trying something simply to be “new” or “innovative”—that certainly may fail—is a risk that these business needs often can’t take.

Bimodal IT, then, is an attempt to manage IT in a way that promotes rapid change while maintaining safety and security. These contrasting environments require different tools, processes, and skills.

For many companies, the benefits of Bimodal IT make sense. And there are plenty of benefits to delineating two IT modes:

Speed. By defining and managing one IT area to focus wholly on delivering new solutions, they can produce rapidly, to meet business needs.
Innovation. Because Mode 2 isn’t focused on maintaining security and handling daily issues, they can stay focused on wider problems that require innovation to solve.
Agility. The goal for many enterprises is to disrupt a certain industry – and by defining which parts of IT focus on these disruptions, they can get there faster. Those in Mode 2 IT become adept at agile practices, so there’s less risk and overhead, and the effort is smoother, as time goes on.
Reduces Shadow IT. When users get the solutions they need quickly, they are much less likely to use unauthorized or unproved applications and software, known as shadow IT—they aren’t bypassing IT.

Drawbacks to Bimodal IT

As with any suggestion, Bimodal IT is not a one-size-fits-all solution. Industry experts and enterprises who have tried the approach point to a few drawbacks of Bimodal IT:

The separation can be discursive. By explicitly separating these groups, teams may battle for attention, resources, power, and influence. This can create a mentality of “us versus them” within the larger IT sphere.
The separation can be too neat. Defining two IT modes in this way can make it seem that the modes won’t, or shouldn’t, rely on each other. For many enterprises, the reality is that an innovative, well-functioning application or software solution, the goal of Mode 2, often relies on well-oiled systems that are inherent in Mode 1.
The separation can be confusing. Confusing teams simply for the sake of “innovation” often leads to confusion on roles and processes. This confusion can manifest as resistance to change, common when employees are told about changes that don’t make sense to them.
The separation doesn’t guarantee innovation. Simply defining one team as innovative doesn’t mean it will just happen – if it did, everyone would be innovators. In fact, some enterprises find that innovation comes from the blending of skills and tools, not from intentionally drawn lines.

Finally, Bimodal IT as a concept is not new. For many experienced professionals, Bimodal IT reflects the key characteristics of DevOps, Agile, and other disruptive ITSM frameworks. For starters, it’s important to understand that separating the tasks should only be seen as a first step toward disruptive innovation and digital transformation that also mitigate the risks involved. It will however take a more exhaustive risk and change management strategy that also aligns with the Agile and DevOps goals of a technology company.

Still, this tension will continue to grow and shift as enterprise IT adapts, more and more, to agile business needs – which means the requirements for resources, management, and governance will continue to change as well, regardless of whether they’re siloed, as in Bimodal IT, or not.

What Is a Canonical Data Model? CDMs Explained

Chrissy Kidd — Tue, 30 Nov 2021 00:00:45 +0000

The companies succeeding in the age of big data are often ones that have improved their data integration and are going beyond simply collecting and mining data. These enterprises are integrating data from isolated silos to implement a useful data model into business intelligence that can:

Drive vital decision making
Improve internal processes
Indicate service improvement areas and opportunities

Data integration isn’t easy though, especially the larger your enterprise and the more software systems on which you rely. The hotch-potch of legacy systems and new tools make enterprise architectures difficult to manage, especially due to the different data formats that all these tools receive.

More and more, companies need to share data across all these systems. The problem is how difficult sharing data is when each system has different languages, requirements, and protocols. One solution is the canonical data model (CDM), effectively implementing middleware to translate and manage the data.

Defining a Canonical Data Model (CDM)

CDMs are a type of data model that aims to present data entities and relationships in the simplest possible form to integrate processes across various systems and databases. A CDM is also known as a common data model because that’s what we’re aiming for—a common language to manage data!

More often than not, the data exchanged across various systems rely on different languages, syntax, and protocols. The purpose of a CDM is to enable an enterprise to create and distribute a common definition of its entire data unit. This allows for smoother integration between systems, which can help:

Improve processes and practices
Make data analytics easier

How canonical data models work

Importantly, a canonical data model is not a merge of all data models. Instead, it is a new way to model data that is different from the connected systems. This model must be able to contain and translate the other types of data.

When one system needs to send data to another system, it first translates its data into the standard syntax (a canonical format or a common format) that are not the same syntax or protocol of the other system.
When the second system receives data from the first system, it translates that canonical format into its own data format.

By implementing this kind of data model, data is translated and “untranslated” by every system that an organization includes in its CDM. A CDM approach can and should include any technology the enterprise uses, including:

Enterprise service management (ESM) and business performance/process management (BPM) platforms
Other service-oriented architecture (SOAs), and any range of more specific tools and applications

Benefits of employing a CDM

Enterprises that are able to successfully employ a CDM benefit from the following situations:

Perform fewer translations. Without a CDM, the more systems you have, the more data translations you must do. With a CDM in place, you cut down on the manual work that data integration requires, and you limit the chances of user error.
Improve translation maintenance. On an enterprise level, systems will inevitability be replaced by other systems, whether new versions or vendor SOAs that replace legacy systems. When just a single system changes, you only need to verify the translations to and from the CDM. If you’re not employing a CDM, you may spend significantly more time verifying translations to every other system.
Enhance logic maintenance. In a CDM, the logic is written within the canonical model, so there is no dependence on any other systems. Like translation maintenance, when you change out one system, you need only to verify the new system’s logic within the logic of the CDM, not with every other system that your new system may need to communicate with.

How to implement a canonical data model

In its most extreme form, a canon approach would mean having one person, customer, order, product, etc., with a set of IDs, attributes, and associations that the entire enterprise can agree upon.

By employing a CDM, you are taking a canonical approach in which every application translates its data into a single, common model that all other applications also understand. This standardization is good.

Everyone in the company, including non-technical staff, can see that the time it takes to translate data between systems in time better spent on other projects.

Building a CDM

You may be tempted to use an existing data model from a connecting system as the basis of your CDM. A single, central system such as your ERP may house all sorts of data—perhaps all of your data—so it seems like a decent starting point to the untrained eye.

Experts caution against this seeming shortcut. If the system that is the basis of your model ever changes, even to a newer version, you may be stuck using old data models and an outdated system, which negates the benefit of the flexibility that CDMs are designed for.

You will also face problems with licenses. Developers who try to handle various similar data models may also spend more time trying to decipher the differences, which can lead to more user errors.

If you’re opting for a canonical data model, create your model from scratch. Focus on flexibility so that you reap the purpose of the CDM: easy changes as your enterprise architecture necessarily changes. Otherwise, the convenience of a common data format will quickly become extremely inconvenient.

CDMs in reality

Getting a company to buy into the idea of a CDM can be difficult. Building a single data model that can accommodate multiple data protocols and languages requires an enterprise-wide approach that can take a lot of time and resources.

When to avoid a canonical data model

From an executive perspective, the time and money investment may be too significant to take on unless there is a real tangible change for the end-user–which may not be the case when building a CDM.Other critics of employing CDM argue that it’s a theoretical approach that doesn’t work when applied practically. A project as large as this is so time- and resource-consuming precisely because it is unwieldy.

The inflexibility of making every service fit within a specific data model means you may lose the best case uses for some systems. These systems may benefit from less strict specifications, not the one-size-fits-all goal of a canonical approach.

Why experts recommend CDMs

These experts recommend that an enterprise architect should instead approach the idea of a CDM differently: if you like the goal of data consistency, consider standardizing on formats and fragments of these data models, such as small XML or JSON pieces that help standardize small groupings of attributes.

Less centralization will allow for independent parts to determine what’s best: teams should decide to opt into a CDM approach, instead of a top-down decision where everyone is forced to create a canon data model.

Should my organization adopt a CDM?

CDMs may benefit your company depending on the size and needs of your data. If you can spend the time on such a project, the more systems and applications that need to share data, the more elusive a one-size canonical model can be.

Effectively implementing all your entities into one centralized model and creating a common data format that communicates across all systems will speed up your enterprise’s data handling capabilities. Then, taking data from disparate systems and managing them in a central location makes implementing data into business decisions is more efficient and more effective.

CIO vs CTO: What’s the Difference?

Chrissy Kidd — Fri, 05 Nov 2021 00:00:11 +0000

As technology has become imperative to businesses large and small, two executive-level roles have become standard:

Chief information officer (CIO)
Chief technology officer (CTO)

But the distinction between the two can be confusing, as “information” and “technology” typically go hand-in-hand. So, what is the difference between these two roles? How does one focus on technology while the other focuses on information?

A simple distinction is that the CIO typically looks inward, aiming to improve processes within the company, while the CTO looks outward, using technology to improve or innovate products that serve the customers.

Let’s take a look at the difference between CIO and CTO roles as well as whether your company should employ one or both.

What is a CIO?

Short for chief information officer, the overall role of the CIO is ensuring business processes run efficiently, with a goal of promoting the productivity of individual employees and business units as a whole.

The CIO is responsible for managing and ensuring ongoing operations, mission critical systems, and overall security, from help desks and enterprise systems to service delivery and program management. The explicit impact of a CIO can be determined with a variety of metrics, though improving the company’s bottom line is a must.

The CIO can be seen as the ultimate cheerleader for all in-house technology and digital processes. IT traditionally has a nebulous reputation with other business units – so it is the role of the CIO to improve the image and reputation of IT services within the company.

But the CIO isn’t only tech-focused: a good CIO integrates the entire IT department with other business units, so knowledge of the common as a whole is imperative. For example, if a business unit seeks technology to digitize, improve, or even automate processes, the CIO is responsible for managing these processes, even if a specific IT team performs the actual implementation.

Responsibilities of a CIO

Some responsibilities of a CIO include, but are certainly not limited to:

Managing all technology infrastructure
Overseeing IT operations and departments
Aligning and deploying technology to streamline business processes
Increasing the company’s bottom line
Focusing on the requirements of internal employees and internal business units
Collaborating with ISPs and vendors to drive productivity

For a CIO to be successful in the role, general knowledge of a wide variety of technology is essential, though a CIO can’t be expected to have expert knowledge of every system. Management and communication skills are also essential: a CIO can oversee dozens of IT employees and a variety of IT teams, and the CIO must also be able to communicate needs and strategies with other executives and department managers.

What is a CTO?

The chief technology officer focuses on creating and using technology in order to help the business grow – typically improving offerings that the company’s customers purchase with the help of new technologies.

The CTO focuses on external customers: those who are buying your company’s products, even if the product itself isn’t digital or technology-based. As customers become savvier and more knowledgeable about the products they use, the CTO must stay innovative and on the cutting edge of technology to ensure the company is offering the best products.

To that end, the CTO is often responsible for the engineer and developer teams who focus on research and development to improve and innovate company offerings.

Responsibilities of a CTO

A CTO may be responsible for the following:

Owning the company’s tech offerings and external products
Using and reviewing technology to enhance the company’s external products
Managing the engineering and developer teams
Understand and touch all technologies the company deploys
Increasing the company’s top line
Aligning product architecture with business priorities
Collaborating with vendors on supply solutions

Though a CTO is tech-focused, staying abreast of tech developments and relying on a background in computing or software engineering, a successful CTO must also embrace right-brain skills like creativity. Innovation might start with a simple question of “How can I use this technology differently from everyone else?”

Collaboration is also an essential skill, as a CTO will need to partner with in-house engineers and external vendors to achieve something that hasn’t been done before.

CIO & CTO salaries

CIOs and CTOs are skilled and have a lot of responsibilities, so they earn a larger income. The average salary for both are roughly equal, according to Payscale. As you will learn as you go throughout your career, the real differences in income won’t be found in salaries so much as they will be found in the benefits, equity, commissions, and overall working experiences you have.

As of October 2021, CIO and CTO Salaries in the United States were:

CIOs & CTOs work together

CIOs and CTOs have different strategies of success for their jobs.

CIOs want to increase bottom-line numbers, and CTOs increase top line numbers.
CIOs mediate between internal IT teams and other departments, and CTOs develop relationships outside the company.

But, at the end of the day, the two’s strategies are housed under the same corporate roof, and they will have to work together to see their strategies through to a successful finish.

The two don’t always get along, however. Their strategies come at a conflict of interests, but the tension between the two is what helps increase innovation in the organization.

CTOs want to keep developing technologies, bringing to the table new ones to use, are constantly experimenting with new tech stacks, possibly spending money on new projects that don’t always pan out. These are the appropriate behaviors of innovation. Unfortunately, when judged by the bottom-line monitoring CIO, these activities can appear reckless, costly, and as wastes of time.

The CIO will make rules to try and reign in the CTO, and make their behaviors more efficient and align with the business goals.

To the CTO, the CIO looks to be an inhibitor of innovation. They always look at what is instead of what could be, and their risk-averse tendencies are draining on the innovation process, and constantly keep the development of the organization lagging behind what its true potential could be.

The CTO/CIO feud is one of those battle-of-the-sexes conflicts that keeps an organization alive. To make it more durable, each party should focus on what their responsibilities are, and acknowledge the success of the whole organization when they occur. Creation does come with tension, and the day-to-day can be a grind, with lots of tedious bickering and negotiations.

But, by looking up once in a while and setting sights on the overall mission to see where you’ve come from, and where you are now, and where you are headed, suddenly it is possible to see value in the dance along the way.

CIO and CTO: Do you need both?

Though CIOs and CTOs may be confused by less tech-minded people, both roles are vital to your company’s success. It can be tempting to think of one role as superior or more of a priority – especially for smaller companies lacking the funding for both. As chief-level positions, one is generally not more senior or junior than the other. In fact, successful companies are often marked by strong presence from both the CIO and the CTO. Do what you can to make both positions a reality as soon as possible.

If you’re looking to hire or create the position of a CTO or CIO, consider these guiding questions to determine which role you’re actually in need of:

Are you aiming to improve or digitize a business process or a product?
Are you catering to your organization or to external customers?

Importantly, from the individual’s perspective, these are independent career paths: you don’t train for years towards a CIO role and switch to a CTO position on a whim. CIOs may come more from the IT operations side of the business, where CTOs may have more software engineering experience.

Companies need one person to support and promote productive employees and business processes just as much they require an innovator and creative solver who can leverage technology to improve business offerings.

How to become a CTO/CIO

Becoming a CTO or CIO requires the education and technical skills to know what kind of technology is needed to cater to the business’ products and technical infrastructure. So, if you really enjoy technology and engineering entire ecosystems of computer operations, then you will need to be ready to learn.

Take chances to extend the knowledge you have into wider domains of expertise:

When you don’t know something, take the time to figure it out. If your learning growth has stalled, or staled, reach out to learn something new

Knowledge is not the only thing. Plenty of people know things. You need to prove the capacity to execute plans, lead teams, and communicate well with others. Not only do you need to prove this to others—you might have already proven it to yourself—but you need to prove it over and over again. Getting to be a C-level executive takes time. Your reputation as a leader of technological know-how, is something for you to take care of, tend to, invest in, and watch grow over time.

“You’re only a success for the moment that you complete a successful act.”—Phil Jackson, Former NBA Coach

Doors will open here and there for you as your reputation is acknowledged—those are opportunities for you to reap what you sow. You must show up in those moments. So, at every stage of your career, when a door opens to accept greater responsibilities, take it.

The Cloud in 2022: Growth, Trends, Market Share & Outlook

Chrissy Kidd — Tue, 12 Oct 2021 00:00:04 +0000

If cloud growth in 2021 must be summed up in a single stat, it’s this: Gartner predicts that the worldwide public cloud revenue will grow by 23% in 2021 for a total revenue close to $332.2 billion U.S., up from $270 billion from last year.

Of course, there is much more nuance in cloud growth, especially since so many businesses and organizations around the world have adopted the work from home model, which relies heavily on web-based public cloud services.

Tracking cloud growth isn’t easy: does cloud mean the few companies that actually provide and drive the cloud—the cloud service providers? Or the thousands more companies that run services on those clouds—the IaaS, SaaS, PaaS providers?

Luckily, there are a few ways to estimate, measure, and predict cloud growth. In this article, we’ll round up the most important cloud growth storylines in 2021 by looking at:

As a service revenue forecasts
Cloud industry trends
Positioning and spend among the big cloud providers
Investment trends

As a service revenue in 2021-2022

“As a service” offerings are the easiest ways for organizations to get involved in the cloud. Software as a service (SaaS) is the most successful of these by far in the first decade of true cloud services as it’s forecasted to reach $122.6 billion by the end of this year. SaaS is all the services and software you run online, trading CapEx license spending for OpEx subscription costs.

(Deep dive into SaaS growth stats & trends.)

But SaaS is not the only player in cloud services. PaaS and IaaS were both big players, and now we’re seeing newer entries, like desktop as a service. In percentage terms, DaaS will see the highest growth at 67.7% followed by Infrastructure as a Service (IaaS) at 38.5%.

Gartner is a leading technology analyst firm who tracks U.S. and worldwide spending of a variety of technology. This recent chart indicates six leading as a service tracks and their forecast revenue. Their most recent research forecasts six key types of cloud services, which we’ll look at in depth below the table:

BPaaS: Business Process as a Service
PaaS: Platform as a Service (aka cloud application infrastructure services)
SaaS: Software as a Service
Cloud Management and Security Services
IaaS: Infrastructure as a Service
DaaS: Desktop as a Service

Worldwide public cloud services: end-user spending forecast (Millions of U.S. Dollars) (Source)

BPaaS: Business process as a service

Of the six cloud categories Gartner tracks, BPaaS is one of the smallest players, with its growth barely budging over the next few years, especially relative to the other categories. The market is still emerging and the growth trends are linear instead of the exponential rise seen in some of the ‘as a Service’ offerings.

This is because a significant proportion of BPaaS customers include SMB organizations with lower requirements on cloud business process services.

(Explore related BPMaaS offerings.)

PaaS: Platform as a service (aka cloud application infrastructure services)

The cloud application and infrastructure services will grow experience significant growth between 2020 and 2021, moving up to a solid third place among these six cloud categories. This growth is expected as more organizations migrate their IT workloads to the cloud. The growth rate is projected to increase at a lower rate between the years 2021-2022.

SaaS: Software as a service

Here is the big growth we expect of the cloud in terms of spending: the public cloud services market will continue to dominate the IT services industry owing to the proliferation of low-cost SaaS solutions that draw workers away from pricey on-prem software licenses.

Gartner predicts that the SaaS cloud application services market will consistently constitute at least one-third of the total public cloud revenue share for the next four years:

In 2021 alone, the SaaS market will likely reach over $123 billion in revenue.
By end of 2022, the growth rate increase even further, expecting to reach $145 billion in revenue.

Cloud management & security services

Cloud management and security services is the next category. Though it’s a smaller player, it’s growth is solid, nearly 30% growth from 2020 to 2022.

IaaS: Infrastructure as a service

IaaS was one of the original ‘as a Service’ opportunities, but one that didn’t live up to its early hype. Now, that’s changed. Industry experts see IaaS as the up-and-coming cloud solution that will eventually surpass SaaS in revenue. Gartner clearly agrees with this assessment, estimated IaaS revenue to almost double in between 2020-2022: up from around $60 billion to $110 billion in revenue.

Still, organizations have been slower to adapt IaaS, reportedly due to a skills gap on cloud migration strategies. The sheer quantity of migration strategies that take the past of least resistance—the lift and shift approach—indicate many organizations are ill-staffed to handle activities vital to true cloud optimization, such as:

DaaS: Desktop as a Service

In terms of raw growth, Gartner predicts DaaS will win out in the next years. This estimate is based on the surge in remote workers, as we noted in our State of ITSM report, which requires secure access to enterprise apps across devices and geographic regions.

However, the percentage growth doesn’t necessarily outweigh other as-a-Service offerings in terms of dollar increase: the project revenue change between 2020-2022 is $1.2 billion to $2.67 billion.

Overall Gartner predictions for the cloud

When it comes to security, Gartner’s forecast highlights the shifting focus toward cloud solutions to run mission-critical security and performance sensitive IT workloads. The cloud industry is proving that on-premises data center deployments don’t automatically translate into strong security. Today, in many situations, cloud computing is a secure alternative.

Similarly, foregoing some proportion of visibility and control into third-party cloud infrastructure doesn’t compromise the security posture considering the stringent compliance regulations and sophisticated security capabilities designed to protect customer data in the cloud.

For IaaS and PaaS use cases, where organizations are ultimately responsible for managing and securing their own IT workloads, the growth in cloud management and security services market suggests that the industry is responding with effective solutions that help organizations maximize the value potential of their public cloud investments.

This might be the biggest takeaway: The combined market share of IaaS and PaaS revenue will finally surpass the powerful SaaS market revenue.

Interesting cloud growth trends & stats

Surveys by a variety of research organizations point to a common trend: the combined spending of SaaS, PaaS, and IaaS models is growing exponentially.

Organizations are increasingly opting for multi-cloud environments that take advantage of all three cloud models, on-premise and off-premise. In fact, a majority of industry verticals average more than 10 cloud vendor subscriptions at every organization.

Let’s look at some of the interesting cloud growth trends expected in 2021 and beyond:

81% of all organizations have already adopted a multi-cloud strategy. 84% describe existing IT infrastructure as some form of a multi-cloud environment.
67% of the organizations operate cloud-based infrastructure environments
AWS maintains the largest market share at 32%
By the end of 2022, the US will have leapfrogged all other countries in terms of cloud adoption—by several years!
Manufacturing ($19.7 billion), professional services ($18.1 billion), and banking ($16.7 billion) are the leading industry verticals in terms of public cloud spending.
SaaS will remain the leading choice of cloud service model over the next few years, reaching $697 billion at a Compound Annual Growth Rate (CAGR) of 19%.

Trends in the Big 3 cloud providers

The next category to look at for cloud growth trends are the cloud providers themselves. The majority of as a service offerings run on someone else’s cloud, no matter if they’re a brand-new startup or a global enterprise. Here’s how the projected market share looks like:

Amazon AWS: 32%
Microsoft Azure: 18%
Google Cloud: 8%
IBM Cloud: 5%
Alibaba Cloud: 5%

In terms of consumer cloud consumption trends:

Google Drive: 94%
DropBox: 64%
OneDrive: 39%
iCloud: 38%

(Read our comparison of the Big 3 cloud providers.)

Investment trends in the cloud industry

Increased adoption in cloud computing is driving investments in cloud computing startups and high-profile tech IPOs.

Top cloud IPOs

In terms of the service delivery model, web-based consumer apps and enterprise IT solutions can be considered as a SaaS offering. The service is hosted in third-party datacenters and delivered over the Internet on a subscription-based pricing model. The technology and business model characterizing high scalability and innovation has driven investor interest, particularly in the cloud infrastructure services segment that is a necessary enabler to all SaaS solutions in the consumer and enterprise IT markets.

Let’s highlight some of the top cloud infrastructure and data solutions IPOs:

Asana, an enterprise productivity SaaS solution, IPO’d at $19 billion in September 2020.
Snowflake, a data warehousing company IPO’d at $33.2 billion and is recently valued at $96 billion.
SentinelOne, cybersecurity solutions provider IPO’d at $10 billion in June 2021—the highest ever cybersecurity IPO ever. As of September 2021, it is valued at over $16 billion.
Confluent, a data-streaming platform company IPO’d at $11.4 billion in June 2021 and is currently valued at around $17 billion.

Data center trends

Another important part of the cloud services industry is the growing data center infrastructure segment.

Data center assets are critical for the sustained growth of public cloud service organizations. Datacenter organizations are pivotal to the growth of the economy as a variety of industry verticals are directly involved and affected by investments into datacenter organizations—construction, manufacturing, transportation, energy, scientific research, and more.

Let’s highlight some of the interesting datacenter investment trends as noted in the latest Frost & Sullivan research report:

Datacenter industry is growing at a CAGR of 9.9%, from $244.74 billion in 2019 to $432 billion in 2025.
The APAC region is leading the spending charts, replacing the North America region now at second, followed by EMEA at third. Emerging markets such as Southeast Asia are still at a “nascent stage” as compared to the Nordic region and the US.

Consumption of datacenter resources can also be measured in terms of power consumption and environmental impact. According to research:

Datacenter facilities consume 3% of the global energy consumption and is soon expected to reach 8% mark.
In terms of energy units, the increase will be from 292 TWh in 2016 to 353 TWh in 2030.

All these stats and trends verify and validate the exponential growth of the cloud computing industry. Cloud vendors, service providers and startup firms are gaining exponentially rising interest and popularity among investors, businesses and consumers.

Cloud (sub-)optimization stats

However, the growth figures should be viewed with the following caveat: cloud computing resources are drastically oversubscribed, underutilized and therefore, wasted. Take a look at the following research trends:

On average, 30% of all organizations waste cloud resources and,
23% over budget for the cloud spending.
61% of the organizations consider cloud spending optimization as their top initiative for the fifth year in a row.

While all of these trends present cloud computing as essential to running a successful technology-driven business organization, the returns on cloud investments are still subject to a few critical challenges: successful cloud migration, cybersecurity, cloud resource optimization, people and change management, and long-term vision for cloud-enabled digital transformation.

Drivers of cloud growth

As we’ve shown in this article, cloud growth shows no signs of stopping. What are the key drivers of cloud growth? Certainly, the ease and startup works for many small businesses—especially brand new businesses that need affordable, scalable solutions.

We’d be remiss not to mention how the global pandemic has upended traditional economic growth and employee work models. The cloud has been essential to companies pivoting to work from home structures.

Other drivers, though, are awareness and openness to new ways of working. In the US, we’ve been following the ongoing saga of which cloud provider will service the Department of Defense cloud migrations.

The State of SaaS in 2022: Growth Trends & Statistics

Chrissy Kidd — Fri, 17 Sep 2021 00:00:10 +0000

SaaS solutions are among the fastest-growing segments in the IT industry. Working on a subscription basis and centrally located on a remote cloud network, software as a service (SaaS) models are becoming the go-to for many organizations for a variety of reasons, including flexibility and affordability.

Of course, with the pandemic necessitating more remote work than ever, the need for SaaS will only increase.

In this article, we’ve put together some of the top trends and growth statistics surrounding SaaS solutions for 2022. Let’s take a look.

SaaS spending vs overall IT spending

Although global businesses and economies suffered this past year due to the pandemic, cloud growth continued to boom.

“Organizations are advancing their timelines on digital business initiatives and moving rapidly to the cloud in an effort to modernize environments, improve system reliability, support hybrid work models and address other new realities compelled by the pandemic,” said Brandon Medford, senior principal analyst at Gartner

In fact, Gartner forecasts end-user spending on public cloud services to reach $396 billion in 2021—and grow 21.7% to reach $482 billion in 2022.

“The economic, organizational and societal impact of the pandemic will continue to serve as a catalyst for digital innovation and adoption of cloud services,” said Henrique Cecci, senior research director at Gartner. “This is especially true for use cases such as collaboration, remote work and new digital services to support a hybrid workforce.”

Cloud growth: SaaS vs other cloud services

Among cloud options, the outlook for SaaS is arguably the brightest. After all, the overall growth of the SaaS industry will remain consistent through these years as more companies adopt SaaS solutions for a variety of business functions, extending far beyond the initial SaaS territories of core engineering and sales applications.

As the first cloud service to truly take off, SaaS has a significant lead on other cloud services. Gartner estimates that SaaS will continue to maintain this dominance well into 2022:

The SaaS growth rate, however, is beginning to slow, especially compared to other cloud services like platform as a service (PaaS) and infrastructure as a service (IaaS), both of which are projected to double from just 2020.

(Understand SaaS fully in our SaaS vs PaaS vs IaaS explainer.)

Largest SaaS companies

We can also look at SaaS success in terms of the companies that make those products. In the first part of 2021, the 10 largest publicly owned SaaS companies per market cap were:

(Source)

Compared to similar numbers from 2020, the growth these companies have experienced is astronomical. For example:

Salesforce alone grew from $161 billion in January 2020 to $251 billion in September 2021.
Similarly, Shopify evaluation in early 2020 was $52.1 billion compared to more than $185 billion today—that’s 225% growth in 20 months!

Notable industry giants that are missing from this list include Microsoft and Oracle. It’s important to realize, however, that a significant portion of their revenue comes from selling on-premises software—so while they are huge tech companies, calling them SaaS providers is a misnomer.

This demand for subscription-based pricing models, however, is spurring legacy companies to rapidly migrate their software solutions to a SaaS consumption model.

This means strong potential for growth of SaaS products in the coming years as their Total Cost of Ownership (TCO) matches that of the on-premise software deployment models. Organizations dominating the enterprise software space—IBM, Oracle, Microsoft, and SAP—will likely maintain their market share for enterprise software products, as a growing number of customers can take advantage of the same product capabilities with the feasible subscription-based pricing model.

SaaS acquisitions & IPOs

Of course, SaaS growth doesn’t stop with revenue projections. SaaS acquisitions feel as if they’re happening daily, as bigger companies look for the next big SaaS thing. Stability of the economy and investor interest in scalable cloud solutions have encouraged entrepreneurs, innovators, and enterprises to develop new SaaS solutions.

SaaS acquisitions in 2021

Notable SaaS acquisitions from the first half of 2021 include:

Docsend, which allows customers to share and track documents using a secure link, was acquired by Dropbox for $165 million.
Networking hardware titan Cisco bought up Kenna Security, a market leader in risk-based vulnerability management.
Chorus.ai, a conversation intelligence leader, was acquired by ZoomInfo in July for $575 million.
Panasonic Corporation completed the acquisition of the leading end-to-end, digital fulfillment platform provider Blue Yonder.

Recent SaaS IPOs

With the recent shifts in the workplace landscape, industry leaders have continued to lean on SaaS solutions. This increase in business created a clear path for multiple SaaS companies to IPO in 2021. In fact, the number of businesses specializing in SaaS that have IPOed in 2021 has increased 125% compared to the same period in 2020.

Significant SaaS IPO news of the last year:

Couchbase

NoSQL database specialist Couchbase debuted on the market on July 22, with a price jump of 39% to $33.25 a share on its first day, valuing the company at more than $1 billion.

SentinelOne

Cybersecurity firm SentinelOne closed markets at $42.50 per share on its June 30 debut, valuing the company at $10 billion. The company specializes in endpoint security, using machine learning (ML) to combat cyber-attacks.

(See more ML use cases in the business.)

Confluent

Debuting on June 23, Confluent ended the day at $36 per share, putting its value at $9 billion. The company provides an enterprise version of the popular open-source Kafka streaming data platform.

Sprinklr

Software maker Sprinklr is best known for its media management, advertising, and content marketing tools, used by some of the biggest brands in the world. The company sat at $16 per share at its IPO in June, valuing it at $4 billion.

SaaS adoption & workforce size

We can also measure SaaS growth in terms of adoption: are customers using more, less, or the same number of SaaS products?

The number and type of users of SaaS products has increased rapidly in recent years. Though initially positioned as ideal for SMBs and startups, companies of all shapes and sizes are finding SaaS a palatable, affordable solution that empowers agility and digital transformation.

Recent research finds that:

The SaaS market is currently growing by 18% each year
By the end of 2021, 99% of organizations will be using one or more SaaS solutions
Nearly 78% of small businesses have already invested in SaaS options
SaaS adoption in the healthcare industry grows at a rate of 20% per year
70% of CIOs claim that agility and scalability are two of the top motivators for using SaaS applications

The proportionality of SaaS adoption to workforce size is attributed to several factors:

Small organizations tend to work on a limited set of projects that naturally require a limited set of products. As the organization grows and the number of teams increase, users working on different projects may have their own requirement for the SaaS tools.
To avoid the issues resulting from shadow IT—the adoption of unapproved software at the workplace—including cost and security, large organizations make it easier to provision many SaaS resources as necessary.
The complexity of large-scale projects at the enterprise level means that no single SaaS solution delivers all necessary functionality. Users who rely on multiple SaaS solutions to address their technology requirements may adopt several products designed for the same target audience and application use cases.

Why is SaaS so popular?

Customers are increasingly adopting the subscription-based pricing model to satisfy growing IT needs—despite limited IT budgets particularly for SMBs and startups. Established enterprises aren’t looking down at SaaS either, despite their size. Instead, they’re wholeheartedly embracing the as-a-service business model to satisfy diverse needs with agile, modern solutions.

The result is a suitable business environment that facilitates healthy competition among SaaS vendors while the market demand continues to increase exponentially.

Research suggests that the number of competitors for SaaS firms starting around 2012 were less than three on average. By the end of 2017, every SaaS startup faced competition from nine other firms competing in the same SaaS market segment. Considering the example of SaaS marketing solutions, the number of products increased from 500 to 8,500 between 2007 and 2017.

SaaS growth rates, IPOs, and acquisitions all indicate this trend is not ending anytime soon.

SaaS benefits for the customer

From a customer perspective, SaaS products offer a variety of benefits:

SaaS delivers higher strategic value versus on-premise software deployments. With a SaaS model, software deployment time has reduced from several weeks and days to a few minutes.
The wealth of enterprise SaaS solutions available gives users a diverse set of resources to address varied demands. As a result, organizations are experiencing higher levels of employee engagement with feature-rich SaaS solutions designed for improved customer experience.
SaaS vendors are also able to push feature improvements, bug fixes, and security updates on the fly. These capabilities with on-premise deployments were previously required to pass through several layers of organizational protocols and governance before eventually reaching end-users.

SaaS technologies have made it easier for enterprises and software vendors to effectively deliver the necessary features and functionality to end-users, ultimately contributing to the popularity of SaaS solutions over on-premise software products.

SaaS Trends in 2022

Overall, the rise of software as a service solutions isn’t going anywhere. Both small and large businesses are utilizing SaaS in some form, and as these options continue to expand, this percentage will only increase.

Serverless vs Function-as-a-Service (FaaS): What’s The Difference?

Chrissy Kidd — Fri, 06 Aug 2021 00:00:57 +0000

The ever-increasing popularity of cloud computing has garnered a new technological revolution with many cutting-edge technologies. Function as a Service (FaaS) and serverless are two such technologies that approached the forefront due to the popularity of cloud computing. Both these technologies aim to:

Provide cost-effective cloud platforms
Eliminate the need for infrastructure management

Sometimes the terms FaaS and serverless are used interchangeably. However, they are two different technologies with some significant differences.

In this article, we will have a look at these technologies and understand their similarities, differences, and how to choose the right technology based on your needs.

What is Serverless?

As the name suggests, serverless is a computing model where infrastructure orchestration is managed by service providers.

The emergence of cloud computing has enabled users to quickly create any service instance, scale up or down, and discard as required, saving CapEx and OpEx while eliminating the need to manage physical hardware.

However, even with these cloud servers, the management and configuration tasks of the cloud infrastructure were left to the users. This is where serverless comes into play.

Serverless aims to eliminate the management and configuration tasks enabling users to solely focus on the application. This is not limited solely to server instances but extends to other areas, too, such as:

Serverless databases
DevOps pipelines
Kubernetes
Etc.

Let’s consider an example where we need to provision a SQL database within the Azure cloud provider.

In a traditional scenario, we will have to first provision all the underlying resources from networks and security groups to compute instances. Then, we’ll need to install and configure the SQL database and continuously manage the infrastructure.

However, with a serverless solution like Azure SQL Database serverless, we can create a SQL server with a few clicks that will automatically scale according to the system load without the need for users to manage any infrastructure.

What is Function as a Service (FaaS)?

Function as a Service is a relatively newer concept that aims to offer developers the freedom to create software functions in a cloud environment easily. In this method, the developers will still create the application logic, yet the code is executed in stateless compute instances that are managed by the cloud provider. FaaS provides an event-driven computing architecture where functions are triggered by a specific event such as message queues, HTTP requests, etc. Some of the FaaS options available through leading cloud providers include:

Function as a Service model adheres to a pay-as-you-go model where you have to only pay for the function when it’s used.

FaaS allows developers to solely focus on developing the application functionality without having to consider backend infrastructure or server configurations. Instead, you’ll simply:

Pick the programming language of your choice.
Package the function with its software dependencies.
Finally, deploy the function.

FaaS vs Serverless: How are they different?

At a high level, both FaaS and serverless refer to a cloud computing platform that eliminates the need for managing infrastructure. However, these two services excel in different other functionalities as well.

Serverless example

For instance, let’s consider the scenario of a Kubernetes deployment. In a traditional sense, users will have to provision servers, manage networking, install, and configure Kubernetes cluster software, manage scaling and availability, and finally create the container and deploy the application in the cluster. After that, users need to take care of all of the day-to-day management tasks of the cluster, which is their sole responsibility.

That’s a lot of work!

Serverless came along to reduce this workload. Serverless services like AWS Fargate allow users to create an Amazon Elastic Kubernetes Service in a couple of clicks, configure it to suit their needs, and then deploy the application container on the AWS-managed Kubernetes cluster. Because AWS manages everything in this kind of serverless Kubernetes environment, users do not have to:

Manage any infrastructure
Worry about scaling and availability

FaaS example

Now, a function as a service platform further abstracts any infrastructure requirement.

Assume that you can simply deploy the web application without provisioning any kind of infrastructure or configurations—just upload the code with dependencies, and you are done! This is what the FaaS services provide: a platform to run functions without worrying about the underlying infrastructure.

These FaaS services are highly useful when creating microservices-based applications. There, we can break down our web application into separate services that run as FaaS functions. Microservices can highly benefit from FaaS as they are targeted towards event-driven architectures. This is not only limited to a single service for a single function, and there might be instances where a single microservice can be a combination of multiple functions, all running on cloud functions communicating through APIs.

The FaaS concept revolves around providing a development platform that can function independently without relying on a larger application or framework.

FaaS vs Serverless: pros & cons

Ok. We’ve got a good understanding of the differences between each type of service. Now, let’s have a look at their advantages and disadvantages.

Serverless advantages

No infrastructure configurations
Most options support auto-scaling, leading to unlimited expansion potential
Cost savings compared to traditional server-based configurations
Ease of long-term management since all updates, security, and optimizations are managed by the service provider

Serverless disadvantages

Loss of fine-grained control, as all the infrastructure is managed by the cloud provider and users have no access to backend infrastructure for custom configurations.
Loss of features. Some serverless applications will lack specialized configurations available to end-users. This is most prevalent when dealing with serverless database applications as some features that are available on normal database deployments will sometimes not be available in the serverless versions.
Potentially expensive, depending on the use case. However, if you are dealing with huge data sets or data requests, it might be cost-effective to have dedicated servers to handle the load.

FaaS advantages

Ease of use. There’s no need to write complete applications. You can simply write only the required functional component and deploy it.
Reduction in OpEx. As the FaaS service model is based on a usage-based pricing model, you only have to pay when the function is executed. This can vastly reduce operational expenses.
Scalability and efficiency of functions. Functions allow users to simply scale up or down and provision multiple functions to meet any user demand without making any changes to the application functionality.

FaaS disadvantages

Not suitable for complex functionalities in a single function. Functions are aimed at creating small functions that accomplish a single task.
Potential for more tooling. Managing a large number of functions will require third-party management tools.
Added data stores. The stateless nature of the functions requires separate data stores outside the functions to support stateful services.

How to choose between FaaS or Serverless

Choosing a FaaS or a Serverless solution depends on:

The user requirements
The supported functionality

FaaS options, however, can offer more narrow solutions, and can be used to create functions that complement existing applications that live as separate services outside the core application. This offers users more flexibility when it comes to developing and testing new features.

Additionally, FaaS can be an invaluable asset in microservices-based applications to extend and support application functionalities.

On the other hand, Serverless offers many more options and is not limited to creating functions. It is the best solution if your main goal is to reduce infrastructure management responsibilities while retaining control of the application configurations. However, this comes with additional complexities compared to just deploying a cloud function (FaaS option).

Serverless is also the best option when dealing with large-scale deployments and functional requirements that require multiple technologies rather than simple functions. Unlike FaaS options, serverless options allow users to have a database.

It is not a must to make a strict choice between serverless or FaaS. We always have the option to use both these services in our applications.

For example, FaaS function can power an application component that reads data from a serverless database that pushes data to a serverless message queue or another function via a restful API.

Both Function as a Service and Serverless platforms and services are highly relevant and useful in the current technology landscape. With all the advantages offered by each of these options, it’s the user’s responsibility to select the right service that can best tackle their requirements.

Agile vs DevOps: A Full Comparison

Chrissy Kidd — Fri, 16 Jul 2021 02:50:07 +0000

Agile and DevOps are the two most popular software development lifecycle (SDLC) methodologies currently in practice.

One survey indicates that 97% of organizations use Agile, whereas the most innovative startup firms as well as large enterprises take advantage of DevOps to deploy new code features really fast:

AWS deploys new code every 11.7 seconds.
Netflix deploys new code thousands of times every day!

As more organizations are eager to follow suit, it’s important to carefully understand the similarities and differences between Agile and DevOps—which is exactly what this article will help you do. We will:

Briefly review the history of both SDLC models
Understand the driving factors behind Agile and DevOps
Highlight the key differences in the two

Agile origins: From Waterfall to Agile

Let’s with a recap of software development history. As software development projects grew in scale and complexity, IT organizations needed a systematic approach to consistently deliver high quality software at speed, while minimizing risk and cost overruns.

In the 1970s, the IT industry and academia formally adopted the Waterfall SDLC model: a linear and sequential model that flows through various stages of a standard software development project in the following order:

Requirements Gathering and Analysis
System Design
Implementation
Integration and Testing
Deployment
Maintenance

Originating in the 1950s manufacturing industry, the Waterfall model worked well enough until most organizations identified a few critical flaws when they actually implemented it. Common flaws of the Waterfall model include:

Rigidity. Requirements cannot be changed once the development process starts.
Risk. Any flaw or inadequacy in the product is identified only at the end of the SDLC pipeline when the project takes its final shape.
Waste. The sequential approach is slow and channels the bottleneck across the SDLC.
Scope. In practice, the cost and time spent on waterfall projects frequently exceeds the expected limitations.

In 2001, a team of professional developers released the Agile Manifesto: a set of values and guiding principles that can be used as a philosophy or a mindset to develop high quality software components iteratively—small but frequent releases with small improvements.

Consider how the values of Agile (left) differentiate from the traditional SDLC practices and priorities (right):

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

Agile also destroys the idea of a “finished product”, which was the goal of the Waterfall approach. Instead, Agile believes that software development is iterative and incremental. With each new release of software, the customer is able to either:

Perform new functions
Improve upon existing functions

Agile methodologies encourage developers to break down software development into small pieces known as “user stories”. This highlights the value Agile places on the customer, which helps the developers by both:

Providing faster feedback loops
Ensuring product alignment with market need

Agile further advocates for adaptive planning, evolving development, early and continuous delivery, and continuous improvement—these all enable developers to rapidly and flexibly respond to change in client needs, software, or other external factors.

(Read our in-depth Waterfall vs Agile explainer.)

From Agile to DevOps

Agile sounds good in theory.

In fact, Agile is easy to plan. It’s easy to consider as a philosophy for organizational culture and communication among development teams. Frameworks such as Scrum make it easier to adopt Agile principles.

In practice, however, Agile lacks in execution and delivery. Organizations often agree to follow rapid release cycles and conduct regular Scrum meetings, but find it challenging to adopt Agile.

One of the reasons? Agile as a guiding manifesto brings little practical advice as an SDLC process framework in itself. Slow and tedious governance process, inadequate communication and collaboration, lack of automation and, most importantly, the expanding divide between Devs and Ops personnel keeps organizations from becoming truly Agile.

Instead, developers end up practicing sprints of fast Waterfall: siloed, sequential, discontinuous development sprints that fail to iteratively improve on customer feedback.

As IT became essential to businesses in the 21st century, two imperative areas emerged: IT Operations (ITOps) and Development Operations (DevOps):

ITOps responsibilities include ensuring security, compliance, and reliability.
DevOps is responsible for developing and deploying new products to the end user.

While ITOps ensures safety and security for all business needs using the network, DevOps walks a line between flexibility and the rigorous testing and communication that comes with deploying new software.

DevOps is a theory rooted in communication, both within itself—as the developers and operators have to coordinate—and also across other departments. DevOps frequently communicates with ITOps to ensure secure and stable environments for testing. Their crossover to other teams like marketing and customer service makes sense as they deploy new software.

(Explore our multi-part DevOps Guide.)

Using DevOps & Agile together

Proponents of using both theories in appropriate business needs believe that DevOps can be seen as an extension of Agile. Agile relies on cross-functional teams that typically include:

A designer
A tester
A developer

DevOps takes this one step further by adding an operations person who can ease the transition from software to deployment. Because of DevOps’ inherent communication with other teams, DevOps can help automate processes and improve transparency for all teams.

(Learn about various IT teams.)

Consider these similarities between Agile and DevOps:

Business focus. Aligning the software development process with user and market-centric products helps drive business value.
Collaboration. Teams at an individual and group level must communicate regularly, actively breaking silos.
Lean philosophy. Focus on removing waste processes, a Lean derivative, and driving value at every stage of the SDLC pipeline.
Continuous release cycles. Short, iterative sprints that lead to a continuous release process. Adopt the mindset and technology capabilities that can help achieve this flexibility.
Approach. Both Agile and DevOps are approaches—they are not hard-coded playbooks for IT organizations to follow.

Considering these similarities, it’s easy to see how many practices that results from the Agile manifesto can be considered a subset of DevOps: collaboration, continuous improvement, and culture.

Agile vs DevOps: Contrasting points

While we are proponents of using Agile and DevOps theories together, it is important to understand where they clearly differ. Let’s look at a few contrasting points.

Speed

Agile is all about rapid and frequent deployment, but this is rarely the goal—or even part of the goal—for DevOps.

Creating vs deploying software

Developing software is inherent to Agile, but DevOps is concerned with the appropriate deployment of said software.

For the record, DevOps can deploy software that was developed in any number of approaches, including Agile and non-Agile theories, like the Waterfall approach, which is still appropriate for certain projects.

(Explore the differences in deploying & releasing software.)

Specialization

Agile is an equal opportunity team: every member of the scrum can do every job within the team, which prevents slowdowns and bottlenecks.

DevOps, on the other hand, assumes separate teams for development and operations. People stay within their teams, but they all communicate frequently.

Communication

Daily, informal meetings are at the heart of Agile approaches, so each team member can share progress, daily goals, and indicate help when needed. These scrums are not meant to go over documentation or milestones and metrics; instead they look solely at progress and any blockers to progress.

DevOps meeting are not daily.

Documentation

Agile teams don’t codify their meeting minutes or other communications, often preferring lo-fi methods of simple pen and paper.

DevOps takes documentation seriously, requiring design documents and specs in order to fully understand a software release.

Team size

Staying small is the core of Agile: the smaller the team, the fewer people on it, the faster they can move, even if they are contributing to a larger effort. DevOps will have many teams that work together and each team can realistically practice different theories.

Scheduling

Agile teams work in short, predetermined amounts of time, known as sprints. Sprints rarely last longer than a month, and often can be as short as a week.

DevOps values maximum reliability, so they focus on a long-term schedule that minimizes business disruptions.

Automation

Automation is the heart of DevOps, as the overall goal is to minimize disruptions and maximize efficiency, especially when deploying software. Agile doesn’t require automation.

Infrastructure as Code is another example of how DevOps streamlines the collective efforts of Devs and Ops complying to organizational policies and governance without compromising efficiency in the SDLC pipeline performance.

These stark differences remind us that Agile and DevOps, at their roots, are not the same.

Culture of Agile and DevOps

While Agile does not necessarily lead to DevOps, both can have profound culture shifts within an organization.

An Agile approach encourages a change in how we think about development. Instead of thinking of development as cumbersome, Agile thinking promotes small, manageable changes quickly that, over time, lead to large changes. Companies of all sizes have experimented with how working in an Agile way can boost many departments, not only IT. Today some enterprises consider themselves fully Agile.

DevOps can also bring its own cultural shifts within an organization, including enhancing communication and balancing stability with change and flexibility.

Choosing to use both theories is an active decision that many industry experts believe can lead to more rational decision making, thus improving the company culture.

Hadoop vs Kubernetes: Will K8s & Cloud Native End Hadoop?

Chrissy Kidd — Fri, 18 Jun 2021 11:00:14 +0000

Apache Hadoop is one of the leading solutions for distributed data analytics and data storage. However, with the introduction of other distributed computing solutions directly aimed at data analytics and general computing needs, Hadoop’s usefulness has been called into question.

There are many debates on the internet: is Hadoop still relevant? Or, is it dead altogether?

In reality, Apache Hadoop is not dead, and many organizations are still using it as a robust data analytics solution. One key indicator is that all major cloud providers are actively supporting Apache Hadoop clusters in their respective platforms.

Google Trends shows how interest in Hadoop reached its peak popularity from 2014 to 2017. After that, we see a clear decline in searches for Hadoop. However, this alone is not a good measurement of Hadoop’s usage in the current landscape. After all, Hadoop can be integrated into other platforms to form a complete analytics solution.

In this article, we will learn more about Hadoop, its usability, and whether it will be replaced by rapidly evolving technologies like Kubernetes and Cloud-Native development.

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

What is Hadoop?

Hadoop is an open-source framework that is used to store and process massive datasets efficiently. It is a reliable and scalable distributed computing platform that can be used on commodity hardware.

Hadoop distributes its data storage and analytics workloads across multiple nodes (computers) to handle the work parallelly. This leads to faster, highly efficient, and low-cost data analytics capabilities.

Hadoop modules

Hadoop consists of four main modules that power its functionality:

HDFS. Hadoop Distributed File System is a file system that can run on low-end hardware while providing better throughput than traditional file systems. Additionally, it has built-in fault tolerance and the ability to handle large datasets.
YARN. “Yet Another Resource Negotiator” is used for task management, scheduling jobs, and resource management of the cluster.
MapReduce. MapReduce is a big data processing engine that supports the parallel computation of large data sets. It is the default processing engine available on Hadoop. Currently, however, Hadoop also provides support for other engines such as Apache Tez and Apache Spark.
Hadoop Common. Hadoop Common provides a common set of libraries that can be used across all the other Hadoop modules.

Hadoop benefits

Now, let’s look at some top reasons behind the popularity of Apache Hadoop.

Processing power. Hadoop’s distributed computing model allows it to handle limitless concurrent tasks.
Data safety. Hadoop automatically creates and manages data backups. So, you can simply recover your data from a backup in case of a failure.
Cost. Hadoop’s ability to run on commodity hardware enables organizations to easily deploy a data analytics platform using it. It also eliminates the need for expensive and specialized hardware.
Availability. Hadoop is designed to handle failures at the application layer—which means it provides high availability without relying on hardware.

With its flexibility and scalability, Hadoop quickly gained the favor of both individual data engineers/analysts and corporations. This flexibility extends to types of data Hadoop can collect:

Structured and unstructured
Through different inputs like social media, streamed data, internal collections, etc.

Then Hadoop checks all these data sets and determines the usefulness of each data set. All this is done without having to go through the process of converting data into a single format.

Another feature that elevates Hadoop is its storage capability.

Once a large data set is accumulated, and the required data is extracted, we can simply store the unprocessed data with Hadoop endlessly. This enables users to reference older data easily, and the storage costs are also minimal since Hadoop is running on commodity hardware.

Drawbacks of Hadoop

Apache Hadoop clusters gained prominence thanks to all the above features.

However, as technology advances, new options have emerged, challenging Hadoop and even surpassing it in certain aspects. This, along with the inherent limitations of Hadoop, means it has indeed lost its market lead.

So, what are some drawbacks of Hadoop?

Inefficient for small data sets

Hadoop is designed for processing big data composed of huge data sets. It is very inefficient when processing smaller data sets. Hadoop is not suited and cost-prohibitive when it comes to quick analytics of smaller data sets.

Another reason: Although Hadoop can combine, process, and transform data, it does not provide an easy way to output the necessary data. This limits the available options for business intelligence teams for visualizing and reporting on the processed data sets.

Security concerns

Hadoop includes lax security enforcement by default and does not implement encryption decryption at the storage or network levels. Only Kerberos authentication is officially supported by Hadoop, which is a technology that is difficult to maintain by itself.

In each Hadoop configuration, users need to manually enable security options or use third-party tools to configure secure clusters.

Lack of user friendliness

Hadoop is developed using Java, one of the leading programming languages with a large developer base. However, Java is not the best language for data analytics, and it can be complex for new users.

This can lead to complications in configurations and usage—the user must have thorough knowledge in both Java and Hadoop to properly use and debug the cluster.

Not suitable for real-time analytics

Hadoop is designed with excellent support for batch processing. However, with its limitations in processing smaller data sets and not providing native support for real-time analytics, Hadoop is ill-suited for quick real-time analytics.

Hadoop alternatives

So, what other options to Hadoop are available? While there is no single solution to replace Hadoop outright, there are newer technologies that can reduce or eliminate the need for Hadoop.

Apache Spark

Apache Spark is one solution, provided by the Apache team itself, to replace MapReduce, Hadoop’s default data processing engine. Spark is the new data processing engine developed to address the limitations of MapReduce.

Apache claims that Spark is nearly 100 times faster than MapReduce and supports in-memory calculations. Moreover, it supports real-time processing by creating micro-batches of data and processing them.

The support of Spark for modern languages enables you to interact using your preferred programming languages. Spark offers excellent support for data analytics using languages such as:

Scala
Python
Spark SQL

(Explore our Apache Spark Guide.)

Apache Flink

Another available solution is Apache Flink. Flink is another processing engine with the same benefits as Spark. Flink offers even higher performance in some workloads as it is designed to handle stateful computation over unbounded and bounded data streams.

Will Kubernetes & cloud-native replace Hadoop?

Even with newer and faster data process engines, Hadoop still limits users to its tools and technologies like HDFS and YARN with Java-based tools. But, what if you need to integrate other tools and platforms to get the best for your specfic data storage and analytics needs?

The solution is using Kubernetes as the orchestration engine to manage your cluster.

With the ever-growing popularity of containerized cloud-native applications, Kubernetes has become the leading orchestration platform to manage any containerized application. It offers features such as:

Convenient management
Networking
Scaling
High availability

(Explore our comprehensive Kubernetes Guide.)

Consider this scenario: you want to move to cheap cloud storage options like Amazon S3 buckets and managed data warehouses like Amazon Redshift, Google BigQuery, Panoply. This is not possible with Hadoop.

Kubernetes, meanwhile, can easily plug them into Kubernetes clusters to be accessed by the containers. Likewise, Kubernetes clusters have limitless storage with reduced maintenance responsibilities as cloud providers manage all the day-to-day maintenance and availability of data.

Having the storage sorted, Kubernetes can host different services such as:

Big Data analytics tools (Apache Spark, Presto, Flink)
Data science tools (BigML, Jupyter, NLTK, TensorFlow, PyTorch, MATLAB)
Any other tool within a Kubernetes cluster

This gives you the freedom to use any tools, frameworks, or programming languages you’re already familiar with or the one that’s most suitable for your use case—you’re no longer limited to Java.

(See exactly how containers & K8s work together.)

Portability of Kubernetes

Another factor that uplifts Kubernetes is its portability. Kubernetes can be easily configured to be distributed across many locations and run on multiple cloud environments. With containerized applications, users can easily move between development and production environments to facilitate data analytics in any location without major modifications.

By combining Kubernetes with rapid DevOps and CI/CD pipelines, developers can easily create, test, and deploy data analytics, ML, and AI applications virtually anywhere.

Support of Kubernetes for Serverless Computing

Kubernetes has further eliminated the need to manage infrastructure separately with the support for serverless computing. Serverless computing is a rising technology where the cloud platform automatically manages and scales the hardware resources according to the needs of the application.

Some container-native, open-source, and function-as-a-service computing platforms like fn, Apache OpenWhisk, and nuclio can be easily integrated with Kubernetes to run serverless applications—eliminating the need for technologies like Hadoop.

Some frameworks, like nuclio, are specifically aimed at automating data science pipelines with serverless functions.

With all the above-mentioned advantages, Kubernetes is gradually becoming the perfect choice for managing any big data workloads.

Hadoop handles large data sets cheaply

Like any other technology, Hadoop is also designed to address a specific need—handling large datasets efficiently using commodity hardware.

However, evolving technology trends have given rise to new requirements and use cases. Hadoop is not dead, yet other technologies, like Kubernetes and serverless computing, offer much more flexible and efficient options.

So, like any technology, it’s up to you to identify and utilize the correct technology stack for your needs.

Chrissy Kidd – BMC Software | Blogs

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