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Most organizations are already well under way with their digital transformation journeys, particularly data modernization. For most companies, the drive for data modernization is attributed to the massive growth of data and a business goal to harness as much data as possible to unlock its potential in transformative ways. Adopting cloud-based solutions is, perhaps, one of the most popular means of modernizing. Moving data into a cloud-based environment enables faster data sharing, improves workflows, and can ease workloads on mainframe systems and data centers.

But moving critical infrastructure out of the data center is a process that is easier said than done. Anytime data moves from one environment to another, risks and security threats become a real possibility. For businesses that operate in highly regulated industries or handle large amounts of sensitive customer data, considerations around the potential for regulatory violations or data breaches make getting the modernization process done securely critical.

When executed poorly, data modernization can leave organizations with a jumbled mess of data, adding to workloads, sapping productivity and workflows, and harming employee and customer satisfaction. Businesses need both the right tools and partners to make cloud migration easier and move data quickly without sacrificing security. Here are four important questions IT leaders should ask themselves when it comes to data modernization.

How accessible is your data to employees?

An important part of any data modernization initiative is eliminating data siloes to improve decision making and customer experience. But the business of digital transformation often has the opposite effect, at least in the short term. As new technologies are implemented and data migrates, new pockets of siloed data can emerge, necessitating the use of multiple applications and systems to keep everything in line.

Employees need secure, remote access to the data that drives the business while this migration is happening—and after. With many modernization efforts taking a hybrid approach, businesses need to ensure they are leveraging tools that eliminate siloes and connect applications across environments. Using technologies that support a hybrid environment makes it easier to modernize with less disruption, improving workloads, keeping data accessible and ultimately driving greater revenue.

Is content management getting in the way of productivity?

Enterprises store a vast amount of data. When it comes to effective data governance, relying on manual processes can hinder productivity while also leaving businesses exposed to regulatory violations, human errors, and missed revenue opportunities. The amount of data moving across an enterprise is only going to increase as more innovation and disruption emerge. Ensuring content management systems are up to the task can be the difference between success or costly mistakes.

Incorporating intelligent automation into content management should be a top priority in every data modernization journey. Adding automation gives data professionals an extra level of support, reducing workloads, streamlining workflows, and jumpstarting productivity. Easing the strain on data management teams can help improve data quality and keep businesses one step ahead of the market.

What are your compliance needs?

Many enterprises are sitting on a trove of highly sensitive data and customer information that needs to be protected. But with more data moving to cloud environments and employees opting for hybrid settings, the risk of data sprawl and unstructured data has increased substantially. Without an effective data governance strategy, businesses are effectively flying blind, with limited insight into where their data lives at any given time.

The less visibility and awareness IT has over data, the greater the chance that it will be exposed. Particularly for businesses that operate in highly regulated industries, modernization initiatives must include a robust data governance strategy, capable of keeping pace with regulatory changes, while ensuring sensitive data is properly stored and cared for.

How agile are your operations?

Businesses today are faced with frequent disruption, shifts in consumer demand, and evolving regulatory guidelines. With highly integrative, agile content management software, they can take on modernization while keeping pace with the realities of a changing business landscape. Leveraging solutions like Rocket Software’s Mobius 12, businesses can opt for a hybrid approach to modernization. With Rocket’s solution, IT teams can streamline processes and move data to the cloud on their own terms, migrating at a pace that works for them.

Rocket solutions are uniquely equipped to support your business’ data modernization journey. Learn more about how Rocket Software and Mobius 12 can boost any hybrid cloud migration.  

Data and Information Security

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AIOps – a must-have rather than a nice to have

Where IT is concerned, there’s no longer a valid business case for the old argument of “doing more with less.” The stakes are too high given the tightly connected global economy, the 24/7 speed of business, digital security threats, and their corresponding data protection regulations. On top of that, the shift to hybrid operations has provided valuable flexibility but multiplied potential failure points. Put simply, it’s no longer a question of if your organization needs to fully optimize its IT production environments, but why haven’t you optimized them already?

The only hitch is that effective IT management takes work. Even when nothing is breaking and your data centers aren’t being battered by hurricanes or holiday-driven demand spikes, software always needs to be updated or patched; security certificates need reissuing; and the interns forgot their passwords again. But since you can’t simply hire your way to seamless IT operations, you need to make them less reliant on human intervention. And artificial intelligence is the way to make them more autonomous.

Integrating AI into IT operations, or ITOps, creates “AIOps.” This technique leverages the power of sophisticated algorithms to capture human insights into how your whole IT estate behaves – not just when everything is running smoothly, but what behaviors are early warnings of potential crashes. AIOps can go beyond detecting and diagnosing IT problems to proactively solving or even preventing them, closing the loop without requiring a human to step in.

Quantifying the value of AIOps

Compelling evidence for the value of AIOps is out there. According to a recent Forrester Total Economic Impact study, Digitate’s AIOps technology makes IT operations teams about 60% more efficient – a result of the teams’ increased productivity and ability to scale. The study concluded that a typical company with a small, 10-person ITOps team could save $1.4 million in labor costs (contract or permanent) over a three-year period. For a large enterprise, that figure could be multiplied around 25-50 times.

To take one real-world example, retail giant Walgreens has 9,000 stores and 4,500 call center agents at four locations. During the COVID pandemic, the company would experience sporadic spikes in demand for vaccinations as the number of cases rose and fell. Supported by Digitate’s AIOps technology, Walgreens was able to determine when those spikes were most likely to happen and adjust store hours and staffing accordingly.

In addition, AIOps enabled Walgreens to optimize its Salesforce usage and automate the resolution of IT tickets. As a direct result, Digitate was responsible for resolving approximately 31% of Walgreens’ total IT tickets, along with successfully monitoring and managing 95% of all IT events since deployment.

Clear definitions: The key to successful AIOps implementation

Making the commitment to implement AIOps requires a strategic plan of action, of course. So it’s important to establish the rationale and context in which AIOps will be deployed. What problems are to be addressed? Is there a focus on specific areas or will there be a more holistic strategy? You need to define these requirements clearly, right from the start.

Typically, the first steps required in order to implement an AIOps solution are:

People: It’s important to assemble a project team to agree on the scope of work, set the criteria for potential vendors, and map out the entire engagement and deployment project. Identify a platform owner and executive sponsors, supported by strong IA architects and IA delivery leads. Key deliverables at this stage include:Assessing the maturity of your current ITOps and IT production environment.Assessing the most recurring issues.Building a business case and defining a clear path to ROI.Process: This is often the most difficult step for organizations because IT support usually relies on the “tribal knowledge” of the IT support team. These team members may belong to other organizations, for example, a System Integrator, which could mean the knowledge of the IT support function is not documented locally. Successful implementation requires the team to first:Document each Standard Operating Procedure (SOP) that describes how IT support is provided. This is critical because AIOps tools need to be “educated” on how to perform support tasks.Define and describe what are the organization’s most critical data flows. For example, what is normal and what is not for each observable element? (Such as IT service, traffic volume, or component state.)Technology: Selecting the right solution from the right technology partner is a hugely significant decision, given the importance of the task at hand, the significant investment in resources, time, and money, and the assumed longevity of the relationship with the vendor. Typical considerations here include:Listing the specific challenges and tangible deliverables.Balancing short-term and long-term needs and cost-benefits analysis/ROI.Qualities such as scalability, platform flexibility, and ease of use.Whether to opt for best-of-breed point solutions or a single, unified IA platform that can handle both vertical and horizontal data flows. (I recommend the unified approach, which will facilitate integration points and the adoption of ML algorithms.)Budget: Beyond the licensing, hardware, delivery, installation, and training costs associated with the platform of choice, the team should also consider wider organizational implications, such as change management. For example, they may need to retrain people whose tasks are now managed by IA for deployment elsewhere.

Top-down or bottom-up?

To actually deploy AIOps, there are two general reference models, which we refer to as Bottom-Up or Top-Down deployment. To better understand how these models are applied, Figure 1 below shows possible data flows for an enterprise with a typical technology stack, including ERP and other business applications, with a standard IT maintenance team.

Digitate

Figure 1: An example of organizational data flow with a typical technology stack

The vertical dimension represents the technical layers needed to sustain a specific solution. The bottom and most fundamental layer is the hardware layer or infrastructure. Above that is the operating system that manages the communication and relationships of applications and hardware.

Above that lies the application layer, representing the actual business applications an organization might use – for example, an ERP suite, CRM system, email, website software, and databases, plus all the middleware or integration tools that connect them. The top layer illustrates the horizontal flow of data from one solution (column) to another.

During each transition this data can trigger actions or decisions – or become enriched for future steps. All these layers, both horizontal and vertical, are constantly communicating among themselves, to keep the whole data flow running smoothly.

The choice of Bottom-Up or Top-Down deployment can be affected by a number of factors. For example:

What is the organization’s operational maturity? Are all stakeholders completely ready for change? Have they successfully captured and prioritized their entire ITOps processes? Are all their SOPs documented?What are the immediate versus longer-term organizational needs? Are there specific areas that they need to address right away? Or are the needs more holistic?How fast is an enterprise looking to transform? Depending on the size, nature and structure of an organization, it might not be realistic to achieve complete transformation at the same time, globally.What is the overall production environment architecture? What are the most problematic IT solutions and is any major change happening in production?What is the architecture for IT support tools, for example, monitoring, messaging, ticket management?Who owns production support knowledge? How available is this knowledge?What is the driver of this transformation?

Based on the answers to these questions, alongside other considerations and rationale, the appropriate deployment model can be selected. Each method has its own benefits and challenges and is best suited to specific scenarios.

Bottom-up deployment model

Deploying AIOps via the “Bottom-Up” model means it is applied at the very foundational levels of the organizational infrastructure IT layer and across all SOPs within that framework. This type of deployment has a longer lead time. However, once all the SOPs have been learned, AIOps can handle any number of typical situations that may arise operationally on a daily basis. Once the SOP learning is in place, AIOps can look at dataflow, how an organization manages master data and start applying organizational use cases to the situations it identifies as actionable.

This methodology requires a bigger investment in the beginning, and it has a slower ROI, but it creates a very solid base that provides broader business improvements over time.

Achieving effective autonomous IT operation support requires the automation of around 80% of all ITOps SOPs, which means achieving the following Intelligent Automation (IA) index target percentages:

50% of total tickets resolved by IA95% of total alerts managed by IA80% of non-ticket support activities resolved by IA

Based on our experience it requires a minimum of 500 IA use cases to be deployed. So, if 50 are deployed each month it will take 10 months for deployment plus two months to set up a program, for a total of 12 months. This is very fast when compared to the average two to three years.

Top-down deployment model

In the “Top Down” model, AIOps is applied to the most critical business data flows first, then automates others one by one. This approach, while providing a faster ROI, is usually a response to a specific problem that an organization has identified. It might create the illusion that the IA journey is no longer needed.

To avoid such a problem, a top-down model requires a carefully planned architecture to fit all data flow requirements into one single IA solution and an equally well-planned deployment strategy, so that each deployment improves the overall Intelligent Automation indexes. Organizations must consider all data flows, not just one, along with having an excellent understanding of just how the different end-to-end data flows connect with each other. While this can create short-term business value, benefits, and ROI, it might also be more expensive in the longer term.

The best of both worlds?

While these two deployment models outlined are very much “horses for courses,” dependent on the reasoning and needs of an organization, they are not necessarily mutually exclusive. As Boston Consulting Group (BCG) stated in its October 2020 report, AI is a Powerful Weapon in the Fight Against IT Problems, “by prioritizing use cases, you can start reaping the benefits of AI quickly — in as little as three months if you know how you want to use AI and can access the relevant data. Contrast that with an all-encompassing ‘big-bang’ approach, where you may wait two years for a grand unveiling.”

BCG goes on to assert that “by prioritizing high-value use cases, you visibly demonstrate the benefits of AI” in the short-term by tackling immediate challenges, which “helps build support and funding for a continuing effort and for the necessary changes to processes and organization. This kind of progressive approach also lets you deploy your target operating model in a gradual, value-driven way. Use cases and operating models develop in parallel and in sync.”

This “hybrid” approach, where organizations can realize value from triaging immediate key problem areas through top-down quick fixes, while simultaneously committing to a bottom-up approach to AIOps deployment can, if carefully planned, present very good options.

CIOs are under constant pressure to provide good news to their bosses and boards of directors, and IT is all too often the favorite target. In such environments, a quick win to solve an immediate issue can spur a commitment to more major changes. A hybrid approach can be a perfect compromise if it is properly planned, explained, and executed.

AIOps delivers proven benefits. Customer satisfaction increases as mean time to recovery (MTTR) and incident management improve. Operational resources are used more efficiently, overall operating costs decrease, and intelligent observation instantaneously flags, and can even pre-empt, potential operational problems. Employee satisfaction can also improve, thanks to the automation of lower-value and often tedious tasks, allied to greater control of operations and empowerment to focus on higher value-add work.

Key to unlocking all of this value is ensuring that the deployment of AIOps is optimized right from day one. The team needs to create an objective view of organizational needs that can prioritize focus areas and choose the correct path to intelligent automation. 

The AIOps journey is a necessary path and organizations must plan how to make it a wanted one, too. Implementing IA at scale is akin to hiking a mountain; the challenge can be great but the rewards and satisfaction are well worth the time and effort.

To learn more about the AIOps journey, visit Digitate.

Devops, IT Leadership, Software Development

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Motivated by multiple drivers, enterprises across nearly all industries are increasingly embracing artificial intelligence (AI) and machine learning (ML) to enhance efficiency, profitability, and customer experience while improving evidence-based decision making. Ever-increasing volumes of available data, both structured and unstructured, combined with ongoing innovations in the software and infrastructure space capable of handling large data volumes efficiently, is facilitating this adoption.

Implementation of AI technology and ML solutions can require significant investment. Based on our experience spanning multiple industries, we have identified key considerations which can help any implementation of AI/ML be much more efficient, leading to a successful adoption (as compared to AI technology “sitting on the shelf”) and enhanced return on investment.

Business challenge identification: The first step toward a successful implementation of any AI or ML solution is to identify business challenges the organization is trying to tackle via AI/ML and gain buy-in from all key stakeholders. Being specific about the desired outcome and prioritizing use cases driven by business imperatives and quantifiable success criteria of an AI/ML implementation is helpful in creating the roadmap of how to get there.

Data availability: Enough historical data, relevant for the business challenge being tackled, must be available to build the AI/ML model. Organizations can run into situations where such data may not yet be available. In that case, the organization should develop and execute a plan to start collecting relevant data and focus on other business challenges that can be supported by available data science. They can also explore the possibility of leveraging third-party data.

Data preparation and feature engineering: This is one of the most important steps in the development of an effective AI model. In this step — in addition to the usual data cleansing, data integration, use of AI tools such as Natural Language Processing to incorporate structured data, judicious and creative feature engineering, creating the training and test data, etc. — it is also important to consult with the business stakeholders and the legal team to ensure that the data/features being used in the model comply with any relevant regulatory frameworks and laws (e.g., Fair Lending). It is also important to incorporate “existing wisdom” in this step. For example, if the objective is to build a fraud detection model, prevalent fraud patterns already known to the organization’s investigation unit should be incorporated. In addition to enhancing the effectiveness of the model, this builds confidence for the end-users of the solution, thus facilitating adoption of the model.

Selection of an appropriate modeling approach: For any given business challenge, it is common to find that multiple AI and ML algorithms are applicable. Often, the simpler algorithm or model with fewer parameters may be a better choice (assuming the performance of different models is similar). A particularly important step in this process is to consider model explainability — is the selected model able to provide human-understandable, plain-English explanations and reasoning behind its decisions? In certain regulated industries, reasons behind decisions made by an analyst or algorithm are a requirement. Many AI/ML algorithms are, by nature, “black-box” in that the contributing factors for the model outcome are not clear. Model explainability packages, such as LIME or SHAP, can provide human-understandable explanations in such situations.

Strategy for operationalization: Having clarity around how the predictions and insights from AI/ML fit into daily operations is clearly needed for a successful implementation. How does the organization plan to use the model scores/insights? Where does the AI/ML model “sit” within the operational workflow? How will the model insights/score be consumed in the process? Is it going to completely replace some of the current manual processes, or will it be used to assist the analysts in their decision-making? Will the solution be implemented in the cloud or on-premise? How will the data flow into and out of the AI/ML solution when implemented? Is there a funded plan for procuring the necessary hardware and software? Having a well-defined roadmap that addresses such questions will go a long way in making sure that the solution gets operationalized and does not sit on the shelf.

Phased implementation approach: The human factor is one of the hurdles faced in any AI/ML implementation effort. People are often uncomfortable with sudden and dramatic changes to their existing processes. A phased implementation approach can help mitigate such concerns. We often suggest a pilot phase, in which the AI/ML solution runs in parallel with the existing process — so that relevant teams have an opportunity to compare the outcomes of the two and become comfortable with the new process.

Training, skilling, and enablement: Of course, it is important to build teams with expertise in various areas of the AI/ML space. Ensure that the relevant skills and resources to support the operation of the AI/ML solution are available. Any skills gaps should be bridged by either training the existing resources or bringing in new resources with appropriate skills.

Thinking through each of these recommendations and having a clear strategy from the beginning to address them will greatly enhance the chances of success and return on investment for any AI/ML implementation.

Learn more about our artificial intelligence services and emerging technologies practice.

Connect with the authors:

Scott Laliberte

Managing Director – Emerging Technologies Global Lead, Protiviti

Lucas Lau

Senior Director – Machine Learning and AI Lead, Protiviti

Arun Tripathi

Director – Machine Learning and AI, Protiviti

Artificial Intelligence, Machine Learning

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From the 6 – 18 of November, the Egyptian coastal city of Sharm el-Sheikh will play host to the largest annual gathering on climate action the world over: COP27. This year’s event marks the thirtieth anniversary of the adoption of the United Nations Framework Convention on Climate Change, with COP27’s fundamental purpose to push the climate agenda forward with further development of the goals established in the landmark Paris Agreement of 2015.

This year’s conference will also be forced to contend with the fact the world has changed significantly in the year since COP26 in Glasgow. For the first time in more than 75 years a land war ravages Europe, with its consequences being felt around the globe. Russia’s invasion of Ukraine has sent the energy market into unprecedented levels of turbulence. Meanwhile, global economic stability — already precarious in the wake of the Covid-19 pandemic — has been pushed even closer to the brink.

These problems sit alongside the fact the number of climate-induced natural disasters continues to rise, with this year’s floods in Pakistan just the latest tragic example. Since 1950, the global number of floods has increased by a factor of fifteen and the number of wildfires by a factor of eight, making such events increasingly expected but no less devastating for those impacted. COP27 is tasked with finding solutions that will make clear and tangible progress towards mitigating climate disaster, with no time to lose. Commitments and pledges may be sincere and well-meaning, but without action they are futile. The time has come to follow through.

The transition to LED lighting is the solution we need

The concurrent crises of climate change, spiraling energy markets, and global economic insecurity are distinct but interlinked problems. Transitioning from energy-wasteful conventional lighting to energy-efficient LED lighting offers a solution with potentially massive benefits for all three issues, both now and in the future.

The ability of LED lighting to increase energy efficiency cannot be overstated, in most cases slashing consumption by well over 50% when compared with conventional alternatives. This number can rise to as high as 80% with connected LED lighting systems that offer smart system management, monitoring, and control. Lighting accounts for 13% of all electricity usage worldwide, and two-thirds of professional light points around the world are still conventional. The potential energy savings that could be realised by a global switch to LED are colossal and could see lighting-related energy consumption drop to 8% globally by 2030, even while the total number of light points continues to rise.

You may see those numbers, appreciate the clear benefits, and recognise that transitioning to LED is a smart move, but convince yourself now’s not the time. As the current state of global finances means you should be looking to save money, not spend it. The thing is, if you really want to save money, then transitioning is your best bet. The savings generated – in both the immediate and long-term future – are such that the question isn’t whether you can afford to make the switch, but whether you can afford not to.

If every city and business in the world converted all their conventional light points to LED, the savings in electricity costs would total €177 billion per year. In the residential sector, upgrading just the EU27’s 1.7 billion conventional light points to ultra-efficient LEDs could effectively generate electricity savings of 34.1 TWh. That’s equivalent to the annual consumption of 9.4 million households, or the electricity needed to charge over 10 million electric vehicles. In monetary terms, it’s tantamount to annual savings of over €11 billion. Switching all light points in the 27 EU member states, residential and otherwise, to LED could save around €65 billion a year. And a mid-sized municipality would be looking at energy savings of over €26 million a year.

These are numbers that should be inspiring decision-makers at COP27 to take action. An effective energy-saving technology exists and is readily available—and the cost to implement it is covered by the reduction in energy costs it enables. Especially within regions where public funding for climate initiatives has been made available, such as the Green Deal in the EU or the Infrastructure Investment and Jobs Act and Inflation Reduction Act in the US, as well as others elsewhere.

But the LED transition is not just about energy or fiscal savings. Decarbonization has long been a vital part of the climate agenda and COP27 is no different, with 11 November officially labelled as Decarbonization Day within the conference. Scores of powerful nations have made net zero pledges, and upgrading conventional light points to LED is a way to make progress toward fulfilling these promises quickly.

Those who have made pledges need to act on them now if they are serious about seeing them through, with the potential for large-scale decarbonization at the mercy of their decisiveness. For example, if all businesses and cities were to transition, it would take more than 553 million tons of CO2 out of the atmosphere. That’s equivalent to the amount of carbon that 25 billion trees sequester in a year. Too high a scale to consider? Let’s think smaller. In a city of 200,000 inhabitants, switching all conventional lighting to LED could reduce CO2 emissions by 18,000 tons per year, the equivalent to the yearly absorption of almost 850,000 trees.

It is time for everyone to adapt

Changes need to be made to the current policies and approaches of most COP27 attendees — and soon — if we are to avoid disaster. The talk has already been talked: it’s time to walk the green walk. COP27 provides the perfect opportunity for key decision-makers to demonstrate the commitment to moving beyond the conversational stage. It’s no longer about whether action should be taken but about actually implementing large-scale changes to long-standing counterproductive patterns of behavior, on an institutional, national, and international level. These changes must be made in an equitable manner — global solutions to global problems — with developing nations as much a part of the energy transition as the more established global players.

Act now

COP27 is a chance for world and business leaders to take the next step in the green energy transition, to move from making promises to seeing them through. Transitioning to energy-efficient LED lighting is a proven solution that reduces carbon output and energy consumption in a cost-efficient way. Cities, businesses, and individuals need to work with reliable technology partners who have demonstrated their commitment to the sustainable cause and the viability of their solutions.

These are turbulent times, rife with economic and ecological uncertainty. But it’s in times of darkness that we most need light.

To find out more click here.

Green IT

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By: Lars Koelendorf, EMEA Vice President, Solutions & Enablement at Aruba, a Hewlett Packard Enterprise company

Can an enterprise CEO today be successful without having a strong relationship with the CIO and the corporate network?

The short answer is no. Technology today powers and enables so much of how businesses function. Given the pace of digitization, the corporate network, led by the CIO, is increasingly becoming a critical business decision center for the CEO within the broader context of running a large enterprise.

In particular, there are three points CEOs today must consider when examining the network and their relationship with the CIO.

1. Investing in the network is foundational to achieving business goals

Is there any department across the modern enterprise business that would not benefit from the ability to work better, faster, easier, smarter, cheaper, and more secure?

The COVID-19 pandemic has already proven again and again why digital transformation is now fundamental to business growth and survival, especially in the face of outside, unanticipated events severely impacting normal business operations.

Matching technology with how business engages key publics, from clients to the community to investors and beyond, allows employees to create higher quality work while producing more competitive products and services that keep pace with ever-evolving demands. It means empowering back-end functions to support the rest of the business better than before. Meanwhile, regardless of which department they belong to or where they choose to work, employees must have the best experience possible, without any technical roadblocks and complications that can stop them from delivering their best work. Otherwise, employees will and are seeking out that environment elsewhere. Indeed, many employees actually experienced very good connectivity while working from home during the pandemic – and now demand that same easy and seamless experience coming back into the workplace or while on the road.

The key to creating that effective work environment is ensuring the CIO makes clear to the CEO the value of automated systems, which not only includes streamlining operations, but eliminating human error, overcoming human limitations, and freeing up employees to focus on projects that drive real value. In short, with the right technology, CIOs can drive actionable insights from the deluge of data that a given company has been accumulating that support the CEO’s long-term vision and business goals.

Enterprise data has the potential to deliver significant cost savings, improve operational efficiency, and even unlock new business opportunities and revenue streams. But first, it needs to be stored, secured, sorted, and analyzed – all of which a great enterprise network can facilitate.

To unlock its full potential, CEOs need to work closely with their CIOs and other department heads to understand the exact impact that the network could have on every area of the business.

2. The network also plays a vital role in achieving sustainability goals

Sustainability is not just a strategic priority. For most companies around the world, sustainability has become the priority, given that it’s being driven both from the top down (by company boards, investors, and governments) and from the bottom up (by employees, the general public, and key communities affected by business operations). In essence, networking capabilities must align with corporate sustainability goals and initiatives to truly achieve its full potential.

The network plays an integral role in empowering enterprises to become more sustainable, to measure and prove that sustainability, and to build more sustainable products and services. Therefore, investing in the right network infrastructure should be at the top of any CEO’s agenda, and they will need to work in tandem with the CIO and other relevant department heads to achieve those aims.

3. A modern network can help the enterprise stay ahead of potential pitfalls

Given the rate of change and disruption, any CEO simply investing just enough in the network to keep operations moving has already lost the plot. The CEO instead must work closely with the CIO to anticipate future business needs, opportunities, and threats, outlining clear goals and corresponding initiatives that ensure the modern network is flexible and nimble enough to meet the challenges.

It used to be that if the network were down, employees could do other manual work while waiting for a fix. Today, however, if there are issues with the network, everything stops, from the factory floor to the storefront to the corporate headquarters. In that sense, the network is mission-critical to keeping the business running.

But the network has so much more potential than this – to help the business continually stay ahead of and be differentiated from the competition. The reason is an agile network creates the foundation for every area of the business to innovate, from IT to R&D and logistics.

With an agile network, the infrastructure is always ready to integrate, support, secure, and fund any new technological developments that might help the business to move the needle on its goals.

Creating Strong C-suite Connections

While this particular article has focused on the relationship between the CEO and the network, at the end of the day, the CEO must empower the CIO to be an advocate for the network and support all C-suite members to work together towards building one that helps them achieve both individual departmental and collective organizational goals.

For more on creating a modern, agile network, learn about Aruba ESP (Edge Services Platform): https://www.arubanetworks.com/solutions/aruba-esp/

Networking

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The first thing to know about managed cloud services? It would certainly help to define the term as a starting point.

At some point in the not-so-distant past, a “cloud managed service” probably referred to something straightforward like paying a cloud provider to manage some VMs for you instead of running them yourself in your own datacenter. Buying compute or storage from a cloud provider is still a core use case, but it has been joined by a much broader set of tools and services.

A managed cloud service today could be anything from a fully managed service for building and training machine-learning models to a fully managed container platform.

At a high level, a managed cloud service can refer to any technology that you acquire from a cloud platform or provider – and that the provider largely runs for you, as opposed to you provisioning and maintaining that service (and all of the related infrastructure it requires) yourself.

Managed cloud services are everywhere these days, again not just for core infrastructure but across the IT portfolio. They can be exceedingly valuable – but IT leaders must be thoughtful in their approach or risk wasted resources, tool overload, talent gaps, and other issues.

1. Managed cloud services vary quite a bit

To the point above, managed cloud services comprise a broad category, and your choices can vary on a pretty wide spectrum. This is not a case where one-size-fits-all or “just pick one” is a great approach, especially in any organization that deals with considerable complexity (which means most of them).

“Cloud managed services mean a lot of different things to various organizations,’’ says Matt DeCurtis, VP, managed operations, Anexinet.

Essentially, what you need to ask is: What do they mean to us?

“Are you a business wanting to transform the way you utilize your on-premise resources and are looking to leverage the resiliency of geo-redundant public or private clouds?” DeCurtis asks, for example. “Are you an organization looking to take their application workloads into a space where microservices can help you leverage the pay-per-use model and be elastic as your business or customer base grows?”

DeCurtis likewise points out that managed cloud services are not one-size-fits-all – and therefore not interchangeable.

“They are highly customized depending on the purpose that is driving the organization to invest in cloud,” DeCurtis says. “Companies seeking cloud managed services for cost purposes will require services that are vastly different than those seeking cloud managed services for enhanced capability purposes.”

Still other organizations may require both – and then the evaluation will require understanding how a particular cloud managed service helps strike that balance.

2. A managed cloud services strategy must reflect your enterprise realities

This means that – news flash – not everything needs to be migrated to a public cloud or consumed as a cloud service. Most enterprises manage a diverse, complex portfolio of applications, infrastructure, integrations, and other services. (See also: one size does not fit all.)

“Your architectural plans will also need to acknowledge your current realities,” says Gordon Haff, technology evangelist, Red Hat. “When we ask IT decision-makers about their approaches to application modernization, most are taking multiple approaches – from keeping systems as-is for now to replacing components with a SaaS or cloud service to developing new microservice-based applications in-house.”

Modernization doesn’t automatically mean refactoring or even re-platforming every application. Cloud is a pivotal part of IT, but you won’t find large or midsize organizations dumping their legacy applications and infrastructure en masse as a result.

“Cloud services can be an important part of the application modernization mix,” Haff says. “But they have to make sense in the context of an overall application modernization strategy which includes, among other things, assessing your level of in-house skills.”

3. Yes, you still need in-house skills

Managed cloud services still require some internal expertise if you want to maximize your ROI – they should supercharge the IT team, not take its place. You can certainly use cloud managed services to do more with less – the constant marching order in today’s business world – and attain technological scale that wouldn’t otherwise be possible. But you should still do so in the context of your existing team and future hiring plans.

If you’re already a mature DevOps shop, then you’re ahead of the game. Other teams may have some learning to do – and leadership may realize that people that can blend once-siloed job functions can be tough to find – though not as impossible as it once seemed.

Fully managed services can help bridge talent gaps, but even with these, IT leaders need a firm handle on who will be responsible for what internally.

4. Managed cloud services and hybrid cloud pair well

DeCurtis also notes a funny thing about the DevOps age – it doesn’t actually obliterate distinctions between what’s required to develop an application and what’s required to operate it reliably or ensure its security. “There still is the need to bifurcate between the two,” DeCurtis says.

There are various reasons why this is the case, but here’s one of the biggies: Most enterprises aren’t going full cloud-native in the near future, if ever. If you’re a 40-person tech startup building everything in a greenfield environment, you’re probably all-in on cloud. But if you’re running IT for a Fortune 500 company, or at a government agency or elsewhere in the public sector, or at a privately owned midsize firm that’s been operating successfully for 80 years – or any number of other organizational contexts – not so much.

IT is another talent consideration, but it also points to a strong relationship between managed cloud services and hybrid cloud and/or multi-cloud environments.

Managed cloud services can be part of the glue that helps bring these diverse environments together. With careful, intentional planning and adequate resources, managed cloud services can improve developer velocity, simplify operational overhead, and retain future flexibility to move workloads where they’re best suited.

To learn more about managed cloud services, visit Red Hat.

Cloud Computing