Customers are a critical component to the business.  Without them there is little reason to pursue the services or products that are developed, built, and sold by organizations. Customers bring in the revenue needed to maintain and refuel the business, as well as to help it grow and scale. 

So, why is it that only 12% of enterprises connect customer data between departments, using it to make the customer journey better?  In many cases organizations just don’t understand several factors which can enlighten the organization to improve the customer’s experience:

What data is available
Value of the data
Insights the data can bring

Data is everywhere and that includes customer data. In the digital world there are many channels the customer may use to purchase the goods and services that will be provided.  In fact, IDC finds over 30+ (and growing) different customer channels may be used to bring a seamless, dynamic, and personal customer journey, enabling the customer’s purchase capabilities.  

Each of these channels holds customer data or moments that matter.  Each moment that matters means the customer is at a decision point from research and investigation of product and services, to comparison, selection, purchase, delight of the purchase, and the experience throughout the process. This includes the customer service that is experienced throughout the journey, and after the purchase and aftermarket services. 

The data comes in many forms from structured to unstructured. In the Voice of the Customer (VOC) 3.0 it is inferred data. In our IDC article, Voice of the Customer Programs: Where are they today? More Importantly, where are they going?, we defined inferred data as information where the insights like customer intention, sentiment and emotion are inferred from observed customer data. In other words, it is looking at a customer’s actions and being able to understand their satisfaction from those actions, without needing to do anything like fill out a survey or post a review.

Inferred data can come from written or visual data where the analytical tools provide insights into the customer’s sentiment.  Sentiment is captured by looking at user behavior such as rage clicks. Rage clicks are the fast clicks on multiple selections of one part of a web page indicating a user is not getting a response when they think they should.  Analytics can quickly determine a rage click, and action can almost immediately be taken today, to engage the user. In addition, body language, verbal tone, facial changes, and eye motions, also try to determine sentiment.

VOC programs must be where the customers are, listening to direct, indirect and inferred customer feedback. These programs need to listen, analyze and act on the customer feedback available. Customers want to know that not only are brands listening, but also acting on their feedback. This builds customer trust and loyalty.

Listening to the data and then analyzing it allows the organization to act on the customer data.  Customer data platforms (CDPs), generative AI, and low code/no code options are quickly becoming part of the VOC programs as they help analyze the data.  As the data is analyzed, the organization uncovers insights that can be converted to a plan to act differently, bringing more delightful and positive customer experiences to the critical moments that matter. 

Ensure that support, customer success, sales and customer service agents learn how to understand VOC sentiment data and customer engagement history to use them to improve their engagements with customers.

The digital world is reshaping the way organizations build the customer’s journey, using technology to compete and gain competitive advantage, while conditioning the customer moments that matter positively.  Moving beyond the 88% of organizations that do NOT use customer data means a shift is required. Organizations must shift how they use customer data with innovative technology to listen, analyze, and act upon the information. 

It is clear, one cannot wait to embark into the customer’s digital world as the organization’s customers, revenue, growth, and scale depend upon this data.  IDC suggests organizations capture, listen, analyze, and act now to bring out better customer experiences. 

At IDC, we are seeing increased business focus on accelerating decision velocity – an interest that extends well beyond the traditional domain of IT executives. With greater frequency – and greater urgency – business leaders, including the C-suite, are now taking ownership of initiatives that address decision velocity.

In my recent presentation at IDC Directions 2023, I presented a video clip of a pit stop in a 2019 Formula race in Brazil to illustrate the impressive result of high-velocity decisions by highly trained professionals – four tires changed in under two seconds (yes, the Formula One driver – and his supporting Red Bull team – won that race). While most businesses must contend with less controlled, more volatile environments, Formula One offers an extreme, but nevertheless instructive example, demonstrating how these teams extract and analyze data from every race and apply those learnings to improve performance.

The Impact of C-Suite Turnover

In a recent IDC survey, when we asked organizations about impactful changes over the last few years, they cited a familiar set of challenges, including changing (remote and hybrid) work models, increased regulation, and higher than usual employee turnover. But, 30% of organizations also cited changes in the C-level executives.

Why is that important?

Well, turnover in the C-Suite is often a leading indicator of new, impactful projects and initiatives, as new executives ask questions, address problems, and propose solutions. New projects, particularly ones that involve data analytics and artificial intelligence, will typically kick off within six to 12 months of a major change in the C-Suite. And, as these projects are implemented, business leaders must contend with the three familiar Vs of data – velocity, variety, and volume.

There’s more data than ever before… it’s faster, and it’s more varied. Data is now increasingly distributed geographically, across hybrid and multicloud environments, creating additional complexity. The only way to thrive in these highly volatile and uncertain environments is to improve decision velocity. How organizations do that requires an understanding of what it means to make big decisions and what the decision-making process looks like.

In the 1970s, Colonel John Boyd, USAF, defined one of the best-known decision processes, the OODA loop, which was used by fighter pilots in its earliest iterations (Colonel Boyd later became a consultant to many of the largest organizations in the U.S.). While the OODA loop can become quite complex as additional feedback loops are added, the fundamental model relies on four steps – observe, orient, decide, and act. The goal is to move through the loop as quickly as possible, but there is still a need for control. In fact, that’s how we define decision velocity – the delicate balance between speed and control

Some organizations understand this delicate balance; many don’t and don’t invest accordingly. We found that digitally mature organizations assign importance to both speed and control with twice the frequency of less mature organizations.

We can see the impact on economic output over time, as data dense products grew to comprise two-thirds ($17.3 trillion) of U.S. GDP in 2022. In contrast, low data density products and services grew by only by one trillion since 2006, suggesting that data and decision velocity will be the key differentiating factors for achieving desired societal goals, such as addressing issues of aging populations, food supplies, and energy usage.

And, we have made progress. In 2002, $290 billion was spent on big data and analytics – that is, data warehouses, links, data integration and machine learning and other related tools and supporting infrastructure.

Are we seeing a strong return on that investment? I would argue that we’re not quite there, yet. Challenges persist, as many organizations still struggle with data silos, data quality, data analysis, and ultimately getting data to the right decision makers.

There are also technical challenges. Today 77% of organizations say that data intelligence is a challenge, which translates to a lack of data lineage and an inability to understand where data is and who has access to it. IDC analysts Stewart Bond and Phil Goodwin discussed these challenges, data logistics, and the need for a unified control plane in greater detail in their respective IDC Directions 2023 presentations.

Only 26% of streaming data is analyzed in real time before it makes its way to a repository, such as a data lake. In keeping with the Formula One theme, this is analogous to driving around a track at 200 mph only to make a pit stop that takes two hours – rather than two seconds – to change the tires.

We see this happen time and time again… and the real impact takes the form of data waste. 34% of surveyed executives have indicated that they often don’t get around to using the data they receive, obviating all the investment in data capture, analysis, cleansing and presentation. And, then there’s the issue of data decay. Half of 1,000 organizations surveyed globally indicated that their data loses value within hours; 75% of respondents said it loses value within days. If organizations don’t have the required decision velocity to act, they are simply wasting the data.

Decision Types

In addition to control, organizations must factor in three decision types – situational, scenario, and portfolio. IDC’s research shows that 13% of use cases require situational decisions to be made in seconds. Examples include financial services companies assessing and blocking potentially fraudulent credit card transactions. Just about two-thirds of use cases revolve around scenario decisions, which can be made within a few hours. For a financial services company, a scenario decision may involve the review and approval (or rejection) of a loan application. Portfolio decisions – 22% of use cases – require the most time. Examples include hiring a new chief risk officer or making an M&A decision.

While each decision type requires organizations to maximize decision velocity, the approaches across decision types can differ significantly. As Dr. Hannah Fry, Professor of Mathematics of Cities at the Centre for Advanced Spatial Analysis at University College London has discussed, sometimes organizations need to be data-driven; other times they need to be data-informed. It’s critical to understand the difference because many low-level situational decisions can be data-driven and fully automated. On the other hand, it’s highly unlikely that we’re going to see a ‘ChatCEO’ capability to automate executive-level portfolio decisions any time soon.

We don’t see enough of this nuanced view, yet, but organizations have been making progress toward improved decision velocity – 62% say that automation across the decision-making workflow has increased and 64% reported that metadata is growing faster than raw data, suggesting that data is being organized and structured in ways that make it available for consumption.

So… we’re moving in the right direction.

Just about 40% of organizations are prioritizing budgets for streaming data analysis as they invest in decision velocity. But, organizations need to do more to keep the flywheel of innovation in motion. They need to invest in new technologies and assess new categories of opportunities, including decision intelligence, a category of solutions that empower organizations with greater situational awareness while helping them recognize alternatives, assess risks, and perform simulations and to provide decision makers with better recommendations. And, despite calls for more self-service capabilities, what organizations really want is just the opposite – full service. They want data that they can use with their tools and applications when needed.

A third category is knowledge networks, a new generation of knowledge management tools that are becoming critical to improving decision velocity. As part of this focus on knowledge networks, organizations need to address the issue of humans learning from each other, especially in an environments characterized by high employee turnover. We’re seeing a lot of progress in this space, but a lot more needs to be done.

Finally, organizations must continue to invest in enterprise digital twin technology, bringing it from traditional design and product engineering, where it has been successfully deployed, to the business domain. Easier said than done, but this should be the goal, as organizations leverage decision velocity to improve overall enterprise intelligence.

Interested in learning more about IDC’s Future of Intelligence research? Download our eBook, Building Enterprise Intelligence: A Framework.

This blog assumes fundamental understanding of generative AI concepts, terms, and technology.

Over the past few months, generative AI has taken the technology world by storm. As per CB Insights, 2022 was a record year for investment in generative AI start-ups, with equity funding topping $2.6 billion across 110 deals. Whether it is content creation with Jasper.ai, image creation with Midjourney, or text processing with Azure OpenAI services, there is a generative AI foundation model to boost various aspects of your business.

How Data is Processed

Whether you decide to train your proprietary foundation model or fine tune and prompt tune an open source/commercial foundation model, or a domain specific foundation model as an ISV solution, it is critical that you take the necessary steps to mitigate potential data security and privacy risks.

Some of the commonly asked questions are:

Can we provide sensitive information to Large Language Models (LLM) either through fine-tuning or prompt augmentation?
Will LLMs reveal my information?

A common concern is that an LLM might ‘learn’ from your prompts and offer that information to others who query for related things or be used to further train the LLM. Or the data that you share via queries is stored online and may be hacked, leaked, or more likely, accidentally made publicly accessible.

It is critical that you take the necessary steps to mitigate potential data security and privacy risks.

Types of Data

Let us first delve into the types of data a generative AI service or application provider may process if you decide to fine tune and prompt tune an existing foundation model, using the example reference diagram below:

Checklist for Mitigating Risk with Generative AI

Now that you have a baseline understanding of how your data is processed, you need to review how your data is retained and what custom controls are available to you. Basically, you need to understand how your data sharing as part of fine-tuning or prompt augmentation is managed. To aid with this, here is the checklist of questions you need to ask the provider and ensure adherence to your corporate policies to mitigate your data security and privacy risks:

Does the provider support your ability to opt-in/opt out of including your data for training their model?

Subject to your use case and for proprietary data, make sure you opt out or, at a minimum, that the training data provided by you is only used to fine-tune your model and not used by the provider to train or improve any of their models.

Can you delete your training and validation data and your fine-tuned models?

Make sure you can.

Does the provider process the data [prompts, completions, and generated result] to train, retain or improve their models?

Subject to your use case and for proprietary data, make sure you opt out, or by default the output data is not used by the provider to train or improve any of their models.
Do not submit any private and/or proprietary data to any public LLM as prompts.

Are the prompts and completions data stored temporarily? If “yes”, how long is the data stored for?

Make sure it is stored securely in the same region you operate from and is logically isolated with your subscription and API credentials.
Make sure it is no longer than “N” number of days that is aligned with your corporate policies.
Make sure it is encrypted, at best, by providers’ managed keys.

Is the data shared with partners?

Many providers anonymize the data while sharing it with partners. Make sure you are fine with this specific to your use case, this may not be enough for your corporation.

Who has access to it from the provider?

Make sure only the authorized employees have access to it.

How is the data used by the provider?

It may be used for debugging purposes in the event of a failure and/or investigating patterns of abuse or misuse.
The content filtering models are run on both the prompt inputs as well as the generated completions.

Can you opt out of content filtering and logging?

If your use case involves the processing of sensitive, highly confidential, or legally regulated input data but the likelihood of harmful outputs and/or misuse is low, check with your provider if you can opt out of content filtering and logging.
Once the provider approves the opt out, make sure they do not store any prompts and completions associated with the approved subscription for which abuse monitoring is configured off. In this case, because no prompts and completions are stored at rest, no provider employees have access to your data, even for limited time.

Does the provider log model usage and support traceability for your compliance needs?

Make sure they do.

If your use case necessitates the creation of a proprietary model, which means you train your own model, you could either train the model in-house or partner with the model provider that supports training a new proprietary model. If partnering with the model provider, in addition to opting for VPC for training and hosting the model, make sure you follow the above noted checklist where relevant and ensure adherence to your corporate policies to mitigate your data privacy and security risks.

You can subscribe to IDC’s Artificial Intelligence Software and Strategies Research and hear more on generative AI during our latest webinar.

The Exciting Year Ahead for IDC Spending Guide

IDC’s Spending Guides, standard-bearers for extensive and exhaustive technology market intelligence, are undergoing major updates starting in July 2023 and continuing into 2024. The updates will bring deeper analysis capabilities to market through enhanced visibility into sectors, sub-sectors, and industries—increasing the total number of industries covered from 20 to 28. The update also includes a planned size segmentation refresh which will add additional granularity of insight.

IDC and Spending Guide: Years of Proven Success for Market Intelligence

For decades, IDC has been the tech industry’s standard for consistent, benchmarkable market intelligence in six continents. Designed around a global overarching technology taxonomy that includes detailed segmentation by geography, industry, size, use case, and other metrics, IDC’s market intelligence is one of four integrated “intelligence” domains IDC maintains for the tech world.

Since 2016, IDC’s Spending Guides have delivered this market intelligence in a collection of over 25 market forecast products which detail planned technology spending across a variety of different market scenarios. Created leveraging a combination of demand side and supply side data and validated by our in-country industry and technology analysts, IDC provides the most accurate forecasts available. This intelligence enables tech vendors, investors, and partners, as well as others that serve and observe the tech world, to deeply analyze the size, trajectory, and growth opportunities over time in highly specific markets and segments.

Industry Enhancements Coming July 2023

Starting in July 2023, IDC Spending Guides will become even more powerful with the restructuring of industry coverage within the IDC taxonomy.

Starting with the Enterprise and SMB by Industry and Software and Public Cloud Services Spending Guides, IDC will update industry coverage across the entire product line, adding clarity to segmentation-scope and introducing coverage in eight new industries. At the same time, this update maintains the fundamental methodologies of IDC’s existing taxonomy, keeping companies whole, SIC based, and aggregating sub-industries with similar IT needs or along the same value chain. This major undertaking will eventually expand to all Spending Guide products, including our 3rd platform use case-based guides, with the goal end-date of 2025 H2.

Spending Guide: The Year in Review

IDC spent a year researching existing industry taxonomies. In this time, we conducted a study of economic standards, client inquiries, tech vendor industry taxonomies, and competitive taxonomies. The conclusion was that a restructuring of the industry segmentation within IDC’s taxonomy was necessary to keep clients informed to the degree expected in the modern world. The resulting update, which is summarized in the figure below, brings more granular industry scope and enhanced industry definition to IDC’s taxonomy.

Figure 1: IDC’s new industry segmentation

While this update impacts all industries to varying degrees, the benefits will be felt most notably in the following industries: Discrete Manufacturing, Process Manufacturing, Professional Services, Wholesale, Personal and Consumer Services, Resource Industries, Retail, Transportation, and Insurance.

Why Updating Industry Coverage Matters

With more thoughtful categorization and granular segmentation, Spending Guide users are better equipped to identify, analyze, validate, and action questions and insights specific to the industries they serve.

From a usability perspective, a more narrowly defined industry scope means less “noise” in the data, increasing the ability to find relevant information while decreasing the time it takes to do so. With less time spent in analysis, there is more time for execution. Analysts and strategists can also track trends across more industries, validating assumptions while understanding nuances of buyer behavior and if / how these change in adjacent segments. Executives and their teams can better inform strategic decisions and justify tactical actions, such as acquisitions, partner selection, resource allocation, territory and campaign planning, and more at granular industry levels.

For example, a leading technology provider targets small to medium size DX technology vendors with specific industry focuses for acquisition. Once acquisition targets are identified by the team, they need a financial case for the acquisition to present to the executive team and the board, thereafter. Using the Worldwide Digital Transformations Spending Guide, the company aligns the offerings of target vendors to specific DX use cases across industries of note. From this vantage, they can narrow down the high-value targets and make their own internal assessment on technology fit and support. With enhanced industry granularity, the acquiring organization can further streamline their process and reduce the time to prioritize high-value targets against those less aligned.

Deploy Spending Guide Industry Insights With Other IDC Solutions to Maximize Impact

When used in combination with other IDC Data & Analytics products, the industry update makes it so Spending Guide can be used to further investigate global assumptions, insights, and trends in granular industry scenarios, including those characterized by different geographic, technological, size, use case, and other segmentations. For instance, total available and serviceable addressable market figures identified IDC Black Book can be refined and clarified at the industry-level in the Spending Guides, helping prioritize product development and acquisition plans. Conversely, Spending Guides can serve as an effective starting point for market sizing that is further validated at global levels in Black Book.

Looking at it from another perspective, industry-related assumptions, insights, and trends identified in Spending Guides can be further refined and actioned with the data in other IDC data products for different purposes, like:

Tracker®, which provides vendor performance metrics in over 150 markets, driving competitive intelligence and helping to position products and align go-to-market plans.
Channel Partner Ecosystem, which provides profiles and performance details on technology partners, helping articulate partner go-to-market strategies.
Wallet and Services Contracts Database, which provide segmentation of individual company budgets and services contracts (including contract value and renewal dates) for technology buyers and vendors, helping craft strategic marketing initiatives and pinpoint sales activity timing.

What’s Next on the Spending Guide Product Roadmap?

IDC’s Spending Guide products and the Data & Analytics team are dedicated to continuous improvement as part of ongoing operations. While the magnitude of the industry expansion for Spending Guides is evident, IDC remains committed to other areas of improvement in the next 12 months, including the granularity of size segmentation in the taxonomy.

With a target release of January 2024, IDC will introduce new company size details to the Enterprise and SMB ICT Spending Guide taxonomy. The change will result in nine ranges of company size across five size segments which are further categorized and grouped. See the figure below for full details on this size segment update.

Figure 2: IDC’s upcoming size segment refresh

There is a lot of reason to be excited about the product enhancements coming out of IDC Spending Guide in the year ahead. To learn more about the solution, market intelligence generally, or to request a demo please visit the Spending Guide section of IDC’s Data & Analytics Solutions page.

IDC conducted a survey of close to 400 CEOs across the world, supplemented by a number of one-on-one interviews, and identified five mandates to help business leaders scale their digital businesses in a rapidly changing digital world. Our survey data revealed that the number one concern is the economy – economic pressures, such as inflation, rising interest rates, and slowing demand.

Mandate #1: Redefine VUCA

To forge a path forward for their organizations, many CEOs are redefining VUCA.

What is VUCA?

A concept that has gained wider awareness over the past several years, VUCA is an acronym for:

Volatility
Uncertainty
Complexity
Ambiguity

So… how are CEOs redefining VUCA for their businesses and their leadership teams?

In a recorded interview played during my IDC Directions 2023 presentation, Simon Paris, CEO of Finastra, talked about how his leadership team has addressed and adapted to the business and economic uncertainty of the last twelve months by redefining VUCA with… well, VUCA. Where his leadership team encountered volatility, they remained true to their vision. When faced with uncertainty, they focused more time on understanding. When complexity surfaced, they looked for clarity. And, when dealing with ambiguity, they employed agility to move forward.

Many CEOs have told us how adaptation is the key to survival as they deal with risks across the world.

The CEO of a global telecoms organization discussed how his leadership team is trying to deal with the high interest rates and the higher cost of capital for customers and his own organization.
A CEO of a Swiss-based train operator told us about the challenges posed by rising electricity costs that have added 15-20% to his cost base in 12 months.
The CEO of Turkey-based multibillion dollar manufacturer is trying to navigate a difficult geopolitical environment and determine which markets to pursue.
The CEO of a large Australian health insurance organization, talked about his company’s major investments in digital and how he and his leadership team are now focusing on closing digital business execution gaps.
A CEO of a financial services software organization highlighted how is very focused on the imperative of closing the digital skills gap.

As IDC analyst Dan Vesset discussed in his Directions 2023 presentation, organizations must accelerate decision velocity to address many of these challenges. By leveraging new data value chains to accelerate decision velocity, organizations can conduct better multi-scenario planning, more accurately assess retention rates by geography, and determine when to onboard and offboard suppliers to drive profitability.

Mandate #2: Invest to Win

The next mandate is determining the right investment strategy. According to Michael Porter, the well-known business academic, the worst mistake organizations can make in a downturn is to make cuts across the board. With that in mind, CEOs are taking a more strategic view of investment opportunities.

In a clip played at IDC Directions 2023, Bas Burger, CEO of BT global, shared his thoughts with us. He discussed how companies that make investments during recessions – when times are tough – actually end up winning in the end and growing their market share. This is the philosophy he and his organization have adopted. So, growth, perhaps surprisingly, is the word of the year for CEOs in 2023, as they look for areas to invest in to keep teams motivated and investors happy.

The second most popular word is technology. Close to 90% of CEOs plan to maintain or increase technology investments in 2023 – focusing those technology investments on revenue-generating activities. IDC’s research shows that organizations that provide more advanced customer experiences are delivering close to double the business growth compared to their peers in the industry. IDC analyst Marci Maddox discussed the critical role of the customer data engine in her IDC Directions presentation.

IDC has forecasted that 40% of the revenue from the G2000 companies will come from digital products, services, and experiences by 2026. Already we are seeing some notable examples:

Bank of America’s realized a 22% increase in mobile sales linked to a mobile usage surge
Nike digital saw 34% growth in the last quarter
Kroger is aiming to double its digital business by 2023

Mandate #3: Build Trust

As organizations start to build and operate their digital businesses, they will also need to build trust. It’s no surprise that security risk and compliance, which are at the heart of the Future of Trust framework, are a technology priority for CEOs in 2023. IDC analyst Grace Trinidad discussed the Future of Trust framework during her Directions 2023 presentation.

Some of the more progressive and strategic organizations are moving into ESG and privacy as they look to deliver outcomes – trusted governance, trusted ecosystems, and trusted enabled commerce – not only internally but increasingly externally into the ecosystem. But, they face some challenges and complexity as they look to build out trust for the digital business in a geopolitical context and operate in a global economy that is increasingly digital in nature.

As every organization grows its digital business and moves data and intellectual property (IP) across suppliers, customers, and partners in a digital ecosystem, various questions are being raised. What if a court in Europe demands data customer data for national security reasons? What if an organization has to ensure that customer data for customers in Saudi Arabia is processed and stored locally? What if it is the target of a state-sponsored cybersecurity attack going after patent information and IP. These digital sovereignty concerns and scenarios must be addressed as the next element of the future of trust framework that CEOs need to focus on to straddle the business and geo-political worlds.

Mandate #4: Reskill and Augment

We are all witnessing a major talent overhaul, as companies across the world come to terms with hybrid working models. Companies are shifting from external approaches to hiring skills and using external service providers to strategies focused on reskilling and automation. As they reskill their organizations to deliver on the next phase of the digital business, organizations will need to connect reskilling to automation initiatives – the enterprise automation 2.0 strategy. IDC analysts Amy Loomis and Gina Smith discussed the IDC Skills Framework in their IDC Directions 2023 presentation.

Automation must be infused throughout the enterprise – across IT, business, and the software value chains. To accomplish this, IT and business leaders will need technology architectures that help them scale the approach across the organization. And, for these efforts to succeed, CIOs must raise their visibility and become key players – orchestrators of technology architectures, budgets, and stakeholders.

Mandate #5: Tech to Scale

As CIOs take on these responsibilities, they must contend with spiraling cloud costs – 64% of CIO’s are spending more on cloud than originally budgeted. They need to optimize cloud investments, but must look beyond efficiency, which is really just an intermediate goal, and focus on creating sustainable value. Our survey data shows that in the next two years CIOs will move away from treating efficiency as an end-goal and look toward business outcomes, agility, and new revenue streams.

The more prominent role of the CIO is already reflected in changing organizational reporting structures. According to IDC survey results, 60% of CIO’s are now reporting directly to the CEO as they become more involved in business strategy and work with the entire C-Suite to redefine VUCA, accelerate decision velocity, make strategic investments, build trust, and reskill and augment their workforces.

Technology suppliers can play a key supporting role. With a focus on helping their customers achieve quantifiable business outcomes they can help organizations execute on these five mandates.

Interested in learning more? Download our eBook, Beyond Digital Transformation: What Comes Next?

Companies are increasingly recognizing cloud computing’s ability to help businesses overcome macroeconomic pressures. As pressures rise, however, it is those with existing, broad cloud deployments that are benefiting the most. Companies only just now moving to cloud will still realize benefits from cloud but will face funding hurdles as business finance teams become increasingly involved in purchasing decisions, according to results from IDC’s Q4 2022 Cloud Pulse Survey.

The Q4 study, which surveyed 1,350 cloud users from North America, Western Europe and Asia Pacific, found a significant number – 40% – of organizations relate their adoption of, or increased take-up, of cloud to the COVID-19 pandemic. Inflationary pressures have further led to increased adoption of cloud services, with cloud now making up on average 32% of IT budgets.

Inflationary pressures currently impact 45% of organizations directly. These organizations say they have seen increased pricing for cloud services (for public cloud and internal operations for private cloud such as skills and increased data center costs). Despite cost pressures, 57% of companies say they require a ‘very high’ to ‘extremely high’ level of IT transformation to support their current business strategies – this is compared to just 48% in Q4 2021.

Increasing costs are having an impact, however, on the value businesses believe they receive from their cloud services, with 55% of cloud buyers surveyed saying inflationary pressures had negatively impacted return on investment (ROI). Around a quarter of companies currently measure their cloud ROI on overall IT performance, and almost the same number again – 21% – integrate cloud ROI with wider company performance.

Cloud is seen to deliver better ROI where it can help improve business processes, increase company revenue and reduce IT administrative costs.  The challenge is that many of the wraparound services required to deliver cloud are leading to increased costs. These include the cost of professional service, network costs, managed services, applications and software and, of course, the cost of the internal skills required to deliver cloud. This means that any increases seen in cloud budgets can easily be absorbed by increases in cost.

This means cloud vendors are coming under much more scrutiny as cloud contracts reach end of life or as buyers seek new cloud options. Of those companies that changed cloud provider in the last year, 53% did so because of cost.

Many vendors are providing solutions to inflationary pressures. Around a third of organizations say they have seen their cloud provider react to market requirements for more flexible contracts and pricing and billing options.

Almost a quarter of companies are also looking internally at where they can make cloud cost reductions. Increasing cost of, and complexity of, cloud has led many companies to overspend on cloud services in the last year. Downtime from hardware, security and software failures also accounts for around 5-9% of overall cloud spend.

And while the IT department is still accountable for cloud budget, late adopters of cloud – those just starting out – are seeing an increasing role is being played by the company’s finance department which is now the second most involved company department in signing off on early cloud deployments.

The contrast is that those with broader cloud environments, from earlier deployments, are placing more emphasis on R&D and marketing involvement in cloud. These companies can commit more time to innovating and driving services that can make the company more agile and provide innovative services that can deliver cost savings across the business or innovative go-to-market solutions that can make the difference during tough economic times.

These findings all come from IDC’s Cloud Pulse Q4 results. The survey focuses on cloud pricing and spend, cloud ROI, cloud strategies and macroeconomic trends.

Across the board, the focus for cloud spend still rested heavily upon the need for organizations to digitally transform their business, however, cost and savings are now the second-highest area of concern for organizations dealing with increasing macroeconomic pressures.