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.

The magic moments in a customer’s experience that create a loyal and trusting customer are getting smarter, faster, and more dynamic. Think about how you feel about a company when the perfect product recommendation is brought to your attention, or when you get in your car, and it has automatically planned the route to your next appointment in your calendar.

How does this happen? We’re in an exciting time of digital innovation! We are all producers and consumers of customer data across more digital and physical channels than ever before and the activation of customer data fuels these magic moments that deliver greater business success.

Not only does customer data identify customer preferences that allows brands to match the expectations of the experience with its reality, but it can also be used to informed decisions about current and future product offerings or how to structure sales and support teams to better empathize with the customer’s situation.

IDC has found over the last two years that the customer experience is the first or second top investment area for digital leaders with 78% stating customer data plays an extremely significant role in the customer experience.

Every department wants to gain insight into their customer preferences and their use of specific products or services through customer data.

Let’s define what we mean by customer data.

Zero-party data is information a customer intentionally shares unsolicited with a brand such as their interest in a product based on a friend’s referral.
First-party data is what a brand collects directly from online forms or transactions. 
Second-party data is data collected and shared through a brand partnership.
Third-party data is collected by an external organization that does not have a direct connection with the customer.

Brands have been collecting these data types for years with the majority being 3^rd-party data. But 3^rd-party data is becoming unreliable and more expensive with companies like Apple rolling out consent-based tracking and Google sunsetting 3^rd party web browser cookies. As global privacy restrictions increase, and consumers become more conscious of how their data is used, third-party data sharing is declining in popularity. 

Now, with business buyers and consumers taking more control over what information they provide, the collection of more zero- and first-party data will replace 3^rd party data because it is the more valuable resource to have. This “data flip” will dramatically impact the technologies that facilitate the collection, use and sharing of customer data. 

52% of organizations are not prepared for a cookie-less future

Not only do organizations need the right tools to collect valuable zero- and first-party data, but they also need to evolve their data practices to establish a social contract with consumers in how their information will be gathered and used developing a chain of custody of their personal profile data as it is connected from one entity to another.

Managing the customer data relationship is about providing a secure experience with a brand users trust, and establishing an exchange for the privilege of using their digital identity to create that positive experience.

When customers provide information to a company voluntarily, it allows for deeper personalization and a better understanding of relevant content, products, or services.

IDC found that 59% of B2B buyers noted that the more it seemed like a vendor knew about them, the more concerned they were about privacy.

Managing this first party data effectively in a scalable way requires the right tools, and it becomes more than just managing the customer relationship – it becomes a way of managing the customer data relationship at scale.

What we are defining here is the Customer Data Supply Chain, such that digital applications will need to handle 3 things:

1. Regulations of data privacy that influence levels of personalization
2. Communication of what data is collected and used to further loyalty and engagement
3. Investments in technology to handle the fast pace that data comes into the organization

When customers provide their favorite brands with helpful personal data, the organization needs to be prepared to care for in a trust-worthy manner and used quickly to extend the value of the relationship. A December 2022 IDC survey asked about the ‘shelf life’ or period within which data loses its value – 75% of respondents say that most data will lose its value within days, if not hours of its collection.

IDC defines the customer data value exchange with three foundations that matter: moving from data collection to establishing a trusted relationship; moving from only using the data once to orchestrating it across teams; and finally using technology to share and scale in real-time. 

Once you have the basic tools in place to manage the customer data value exchange, it is time to increase the fly wheel of value by looking at activating the data automatically using AI/ML more effectively, connecting privileged first-party data securely through the use of customer data clean rooms and redefining new measures of customer experience.

As the customer data value exchange takes hold in the organization, it is important to introduce new KPIs that look at the customer data relationship with the organization. Very few metrics used today describe the data from the viewpoint of the customer.  Most measures are internally focused – they look at the volume or age of the data- or the well-known Customer Satisfaction (CSAT) score is a lagging indicator of customer data usage.

CSAT does not represent the customers’ point of view or feelings about how much data is known about them, and whether the information was used to proactively reduce the level of effort expended to complete a task.

By 2027, 25% of global brands will abandon CSAT as a measure of customer experience and adopt a Customer Effort Score correlated to outcomes as a key indicator of journey satisfaction and success.

Getting direct feedback from the customer about the experience and the expected vs expended level of effort is a radical change for some organizations.  However, businesses that look beyond customer data as only a marketing tool and use it across the whole connected customer journey will find benefits to both the customer and the business.   For example, customers will find more seamless journeys and use brand familiarity to purchase more products.   Businesses will see better forecast accuracy, higher order values and lower churn rates. 

It is both the technology and processes that are needed to manage the customer data relationship and in turn, grow revenue at scale.

To learn more about customer data and data value exchange, view Marci Maddox’s IDC Directions presentation.

Where once IT cost management was a competitive advantage, it is now table-stakes. If IT leaders are not operationally effective at managing costs, they risk falling behind in their ability to support business strategy and drive digital transformation.

As an IT leader, one of the most important responsibilities is to manage costs and maximize the efficiency of your organization’s IT operations. Cost-saving measures not only benefit your organization’s bottom line but also pave the way for investment in innovative technology solutions that drive growth. This blog discusses five cost-saving measures that IT leaders should prioritize.

Focus on Cloud Center of Excellence

In a recent IDC poll[1] of CIOs, 64% of respondents said they were spending more on the Cloud than they budgeted. Over 50% of CEOs are concerned about Cloud Spend[2].

A Cloud Center of Excellence (CCoE) are IT professionals assigned to develop and implement cloud computing strategies, policies and best practices. A CCoE centralizes cloud-related practices and standards as well as promotes consistency in cloud technology adoption.

Six elements are key to a successful CCoE:

Leadership: Empowered leadership with the ability to work across traditional organizational boundaries or divisions. A CCoE is the opposite of siloed.
Talent: Technical expertise to understand and support cloud initiatives across the organization, including cloud architecture, enterprise architecture, security, compliance, DevOps, and infrastructure. In most organizations, this requires a combination of hiring or reskilling of existing staff.
Governance and Communication: Policies, practices, operating models, defined technologies, shared best practices (across divisions and business units,) and standards with an overarching architectural standard.
Continuous Improvement: Continuously monitor and assess cloud adoption and usage, identify areas for improvement and implement changes to optimize the benefits of cloud computing.
Reporting and Metrics: Establish and track key performance metrics for Cloud usage based on the business drivers underpinning the Cloud strategy.
Collaboration: Work closely with business units and IT teams to ensure cloud initiatives align with organizational goals and are integrated with existing systems and processes.

Implement FinOps

FinOps, or Financial Operations, is a practice that aligns IT spending with business objectives. By implementing FinOps, IT leaders and the business can gain greater visibility into Cloud value. FinOps teams address how the agility of Cloud can complicate budgets and forecasting. They also link spend to business value so business leaders can make considered decisions on their Cloud usage.

The three phases of the FinOps life cycle provide a structure for effective implementation:

Inform

Tagging (descriptive metadata)
Visibility into spending
Budgeting and forecasting
Cost allocation
Assembling a cross-disciplinary team

Optimize

ROI
Rightsizing
Workload placement
Rate and discount optimization
Culture and ownership
Minimizing waste and unused resources
Identifying tools and software

Operate

Automation
Centralized billing or showbacks
Defined control and governance
Communicating optimizations and spend-patterns to “Inform” phase and stakeholders

IT leaders can start a FinOps team of one, but complexity typically expands it to additional roles:

FinOps Practitioner Lead
Cloud Engineering and Operations
DevOps Manager
Procurement
Finance
LOB Product Managers
Executive Sponsor

CCoE and FinOps overlap in some duties but, simplistically, the former is focused on setting standards while the latter is responsible for ensuring value (cost versus business benefit). 

Based on our consulting engagements with clients, when tech leaders build a robust CCoE and FinOps organization, they typically see 10-30% reductions in cost and slower cost growth than more ad hoc Cloud management practices.

Benchmarking IT Costs

IT cost benchmarking involves comparing your organization’s IT spending to relevant peers both inside and outside your industry. This approach ensures that you are not only comparing yourself to your industry peers but also to relevant performers in other industries. Benchmarking allows IT leaders to identify outliers in IT spending and optimize budgets accordingly. IT cost benchmarking also helps to ensure that your organization is receiving value for money from vendors and service providers. Finally, it can support budgetary requests or validate investments to the business.

Cost benchmarking is a valuable tool for technology leaders to identify cost efficiency opportunities in their organization. In our experience, companies that regularly employ cost benchmarking typically find 5-10% efficiency opportunities. However, companies that have never performed cost benchmarking can see up to 30% opportunities for cost efficiency.

It’s important to note that cost benchmarking goes beyond just KPIs. While KPIs provide metrics for the average organization, they don’t account for the different goals, strategies and technologies in each organization. Benchmarking allows technology leaders to gain insights into their organization versus similar technology, workload, volume, complexity, and service peers.

Most organizations do not have the data to perform cost benchmarking themselves but use third-party advisory firms with a broad database of comparable peer data. As part of this, technology leaders need to inquire whether the data is regionally specific to their environment. Also, look for impartial assessors who don’t have down-stream interests in the results.

With cost benchmarking, technology leaders can identify areas where their organization is overspending and implement cost-saving measures. This can lead to significant savings that can be reinvested in other areas of the organization.

Conduct an IT Workforce Assessment

In a global Future of Work survey[4] conducted last year, a large majority of the respondents stated that enabling employees to focus on higher value tasks, moving employees among roles and functions, and reskilling and retraining were critical. Further, companies are having challenges hiring staff, with an average of 3.2 additional months[5] in recruiting, which impacts project delivery.

IT staffing assessments help IT leaders identify opportunities to optimize and improve the use of resources and optimize staffing costs and align IT to business needs. By assessing the duties within IT roles (and not the person in role), and appropriateness of IT roles and structures, IT leaders can identify gaps and opportunities for training and upskilling, and organizational evolution. Workforce assessments help better align the IT organization to evolving technology and business strategy. IT workforce assessments can also identify opportunities to outsource or automate certain tasks, drive better service for the business and reduce inefficiency.

Typical benefits in performing an IT workforce assessment are:

Identifies roles and structure for the present and the future needs
Cost management and cost efficiency
Forecasting and transparency
Competitive resourcing
Investing to innovate
Determining whether IT is properly resourced, roles are consistent and are aligned with industry standard IT role structures

Address Technical Debt

Technical debt refers to the painful side effects of prioritizing time, money and workarounds over quality in the delivery of enterprise information technology[6].  As technology advances, legacy systems can become increasingly expensive to maintain and limit the ability of organizations to innovate. By addressing technical debt, IT leaders can reduce ongoing maintenance costs and free up resources for investment in innovative technology solutions.

Key impacts of technical debt include:

Security vulnerabilities
Manual execution of business processes
Errors
Inefficiency
Reliance on institutional knowledge
Drag on business profitability and growth, customer and employee engagement and retention

Technology leaders must first inventory their technology environment to identify technology debt and legacy technologies. This may be done using typical in-house technology tools present in most organizations: asset management, assets inventory, and asset discovery, source code analysis, or even speaking with your staff. There are also third-party tools and advisory groups that can assist in this process. 

After inventorying your technical debt, triage the applications into key buckets for redress: retire, refactor, duplicate functionality, replace, rebuild, retain. Then determine the priority. Typical priority factors are cost, criticality, business risk, security risk, business benefit, supportability, and technology fit/strategic fit. Combining the buckets with the priority factors provides you a ranking system for targeting technology debt.

In conclusion, IT leaders should prioritize cost-saving measures to maximize the efficiency of their organizations’ IT operations so they can focus on being a business enabler and innovation-driver, rather than a cost center. By focusing on a Cloud Center of Excellence, implementing FinOps, benchmarking IT costs, conducting a staffing assessment, and addressing technical debt, IT leaders can identify and implement cost-saving opportunities that enable investment in innovative technology solutions.

Many IT leaders are challenged in demonstrating value to the business and validating their budgets. Learn how IDC Metri improves technology costs and performance delivery through our IT Service Cost Management service.

[1] IDC CIO Quick Poll, n=69 (December 2022)

[2] IDC Worldwide – CEO Survey, IDC, January 2022)

[3] IDC CIO Quick Poll, n=69 (December 2022)

[4] Future Enterprise Resiliency & Spending Survey – Wave 6, IDC, July 2022, n = 816, NA: 256, AP: 369, Europe: 191

[5] Future Enterprise Resiliency & Spending Survey – Wave 6, IDC, July 2022, n = 816, NA: 256, AP: 369, Europe: 191

[6] IDC Perspective, CIO Guidance: Preventing and Remediating Technical Debt, IDC, June 2022