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If you are a CIO of an organization that has moved to agile, you may feel that you have lost some oversight over what is happening in the organization. As self-empowered teams work on the development of new functionality, product owners prioritize the work that needs to be done in upcoming sprints, turning the CIO role into more of a facilitator than a decision-making manager.

In theory there is nothing wrong with empowered team members. On the other hand, a certain degree of management is necessary to make sure all well intended initiatives are aligned to organizational goals, including the company mission and vision. IDC studies however show that 96% of CIOs perceive a lack of visibility in software development teams[1].

To stay relevant in an ever-changing environment, organizations need to deliver value to their customers. They need to provide value to attract new customers, to retain current customers and to stay profitable. Producing value is largely tasked to development teams in the organization.

Modern CIOs are struggling to find a balance between being a facilitator, trusting their people and being a manager.

It’s often crucial to periodically assess team performance that identifies the high-performing and the low-performing teams as a starting point to understand room for improvement: team performance optimization (TPO).

Team Performance

So, what is team performance? Teams need to provide as much value for the money as possible. The money is often fixed: X persons, working Y hours per week cost Z dollars. When we ask how much value is produced, it becomes more difficult to answer because measuring value is not quantifiable. Value is perceived differently by different individuals and can also vary over time.

It’s widely accepted that functionality, when well-prioritized, has a positive relation with value. Therefore, more functionality delivered should mean more value delivered. And functionality is objectively measurable. There are ISO standards available for functional size measurement, which means that it’s possible to measure the functionality that different teams produce in an objective, repeatable, verifiable way. The functional size units produced are measured by technology that also measures the product quality of the application the team is working on. This technology measures the code against all relevant standards, like ISO25010, ISO5055, CWE, OWASP, NIST, etc. The result is a score on several health factors: Robustness, Security, Efficiency, Changeability, Transferability, and Total Quality.

Optimizing Team Performance

IDC Agile Value Management measures the functional size produced per sprint, release or other time period and collects some basic data like effort via hours spent, cost of these hours and defects logged. This enables us to define 5 key Team Performance metrics:

Productivity[2]: Effort hours spent per functional size unit
Cost Efficiency: Cost of effort hours spent per functional size unit
Delivery Speed: Functional size units developed per calendar month
Process Quality: Defects per functional size unit
Value: Functional size units delivered per $1000 spent

These metrics are purely based on the functionality produced, regardless of the technology used or other non-functional requirements. Because these are objective metrics, we can benchmark these against our extensive database to compare the team metrics to carefully selected peers (based on for instance technology, size, complexity, industry, country, etc.). This results in indices that show the relative performance of teams against these peers. For instance, a Productivity Index of 15% means 15% better productivity than the peer group. In this way it becomes possible to both compare the performance of teams against the industry and against each other. Lastly, it becomes possible to identify the best performing teams and the teams that have room for improvement. The next figure shows this.

In this example, 12 teams are compared to each other showing their (trends in) productivity. The zero percent line indicates the industry average, and the dots represent the different measurements.

Visibility to Manage Value Creation Function

It’s important to understand that the management information does not necessarily need to be shared with the teams, as they may feel this can be used as a stick to punish low performance. This should never be used to punish, always to understand and to improve team performance.

These insights are the starting point for actual management based on facts. Start with questions like: why is one team performing better than others? Do they use better practices, do they have a better requirements analysis process, better developers that understand the application better, fewer defects, etc.? What is the quality and risk level of the applications the teams are working on? IDC consultants help the teams improve, using best practices that have a proven positive effect on team performance.

As stated before, IDC studies show that 96% of CIOs perceive a lack of visibility in their software development teams. Using Agile Value Management it becomes possible to get an integral view of team performance and the quality of and risks in an application, providing necessary visibility for leaders to move out of being a facilitator only, and to actually manage.

When agile is not properly managed the dollars spent per unit of value delivered can easily go through the roof while the time to value drops significantly.

Learn how IDC Metri helps our clients achieve 35 to 65% reduction in spending or reduced time to market in our eBook:

Sources

[1] IDC Perspective: CIO Guidance for Addressing the Lack of Visibility in Software Development Teams. Part of Future Enterprise Resiliency and Spending Survey (FERS Survey) wave 1, 798 respondents at CxO level.

[2] Productivity is universally defined as output divided by input. However, this results in very small number with many digits. Therefore, the inverse is used which is often referred to as Project Delivery Rate, and called Productivity because this is a term that’s easier to relate to.

Back in 2019, IDC’s security and trust team wrote about the potential of artificial intelligence (AI) in cybersecurity. At that time, the approach was to use AI to create analytics platforms that capture and replicate the tactics, techniques, and procedures of the finest security professionals and democratize the unstructured threat detection and remediation process. Using large volumes of structured and unstructured data, content analytics, information discovery, and analysis, as well as numerous other infrastructure technologies, AI-enabled security platforms use deep contextual data processing to answer questions, provide recommendations and direction, and hypothesize and formulate answers based on available evidence.

The goal at that time was – and still is – to augment the capabilities or enhance the efficiency of an organization’s most precious and scarce cybersecurity assets — cybersecurity professionals. The approach to development typically begins with the mundane and remedial and gradually graduates to increasingly complex use cases. Essentially, machine learning allows cybersecurity professionals to find the malicious “needle” in a haystack of data.

Use Cases for AI/ML Today

With the release of ChatGPT in November 2022, we are seeing increased excitement around all things AI and the application of AI technologies to enable secure outcomes. The greatest interest is in generative AI, but AI in security is hardly new. Machine learning, a form of AI that has been used in security for more than a decade, was used to generate malware signatures before algorithmic protections became the rage.

One long time use for machine learning has been behavioral analysis of users and entities (UEBA) to identify anomalous behaviors. This includes the configuration, applications, data flows, sign-ons, IP addresses accessed, and network flows of the devices in the environment. For example, does the device usually call out to another device? If not, an alert may be generated to have an analyst look into the unusual behavior.

Many vendors include UEBA as part of their security information and event management (SIEM) with alerting on the anomalies. For example, some SIEMs use ML models to detect domain-generated algorithms (DGA) that are used in DNS attacks.

Use Cases for Tomorrow

Vendors envision using AI for the thankless security tasks and saving humans from narrowly defined manually repetitive tasks, so they can pivot more quickly into investigating complex issues the machine does not understand. AI will not recognize what it has not been trained to see, so all new tactics and techniques will require human input.

Generative AI can easily translate from one language to another – spoken or machine language – which includes translating natural language queries into the vendor-specific languages needed to conduct the search in other tools. Today, SIEM vendors often use rules to correlate alerts into incidents that present more information to the analyst in one place.

AI will be trained to produce the context around an alert so analysts do not have to spend as much time on investigations, such as checking with an external service that can then label which domains are malicious. AI will handle investigations more efficiently, as well as prioritize which alerts should be handled first.

If trusted by the organization, AI may suggest or write playbooks based on regular actions taken by analysts. Eventually AI may be trusted to execute the playbooks, as well. Generative AI can recommend next steps using chatbots to provide responses about policies or best practices. One use may be a higher-level analyst confirming recommended actions for junior-level analysts. Eventually, organizations will use their own security data and threat pattern recognition capabilities to create predictive threat models.

Other uses for generative AI include:

• Generating reports from threat intelligence data

• Suggesting and writing detection rules, threat hunts, and queries for the SIEM

• Creating management, audit and compliance reports after an incident is resolved

• Reverse engineering malware

• Writing connectors that parse the ingested data correctly so it can be analyzed in log aggregation systems like a SIEM

• Helping software developers write code, search it for vulnerabilities, and offer suggested remediations

Moving Forward

The goal with AI has always been to improve the efficiency of the security analyst in their work of defending an organization against cyber adversaries. However, the cost of AI models and services may be too high for some organizations. Relying on AI to guide analysts and report on security events will only take off if the models are trustworthy.

The data used to train the model must be accurate or the AI-driven decisions will not have the desired effect. The terms confabulation or hallucination are being used to describe when a model is wrong because the models are trained to give some answer instead of saying I don’t know when it does not have an answer. The industry needs to avoid AI bias which occurs if the training set chosen is not diverse enough.

Customers and AI suppliers should understand the underlying data behind each decision so they may figure out if training went wrong and how models need to be retrained. And, vendors must protect the data used to train the models – if the data is breached the model could, for example, be trained to ignore malicious behavior instead of flagging it. Additionally, customers must have guardrails to ensure that they keep proprietary data out of public models.

Vendors will also need to check for drift with their models. AI models are not something that can be set up and forgotten. They must be tuned and updated with new information. Researchers and other cybersecurity vendors can turn to MITRE’s Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS) for help in better understanding the threats to machine learning systems and for tactics and techniques – similar to those in the MITRE ATT&CK framework – to address and resolve issues.

Interested in learning more? Join us for a webinar on May 31st – Unlocking Business Success with Generative AI.

It’s a question that doesn’t get a lot of attention – but it should! I mean, when you really look at it, there are far too many brands that fail to develop deeper relationships with consumers, content to make the occasional transaction every two to three years. They believe that the best technology or the best product wins. But it often doesn’t. They fail to understand the many other factors at play, including the role of frictionless user experiences. Far too many are “one-trick ponies” with little relevance in consumers’ lives. They lack a clear understanding of how to build a brand – how to build from strength – and underestimate the impact of brand loyalty and brand advocacy have on the cost of customer acquisition.

The U.S. Consumer Device Market: A Picture is Worth a Thousand Words

The brand map below captures the factors behind each brand’s performance.

Although Samsung users trust the Samsung brand more than Apple users trust the Apple brand, Apple has successfully developed deep relationships in a much more profound way than Samsung, enabling it to outperform Samsung in revenue. Each brand’s position correlates strongly with its share of the U.S. consumer device market, which was worth more than $600 billion in 2022.

However, the news is not all bad for Samsung, which far outperforms all other brands in depth of relationship and revenue. The large group of low trust, low relevance brands is vulnerable; they must expand their relevance or suffer the consequences of their shallow, transactional relationships.

Trust is Essential for Successful Relationships with Consumers

The level of trust in your brand determines its ceiling – its potential. Trusted brands get bought, used, and recommended more. They eventually end up with a higher proportion of loyal customers and are more profitable.

As important as trust is, relevance is essential for developing deeper relationships with consumers. A high level of trust that does not translate into greater relevance is unfulfilled potential – the problem that plagues Samsung.

Deepening the Relationship – Building Increased Relevance

The process of deepening the relationship comes from empathetically asking questions like: What needs are we currently meeting? How well are we meeting them? What are their pain points? How could we solve them? What things would they like to do that they cannot do very well or at all today?

Contrast that with the selfish mindset of the inwardly focused company (e.g., how can we convince consumers our offering is still relevant?) Rather than starting with the needs of the consumer, this approach starts with the company’s need to find nails to hit with its hammer.

This does not apply only to device markets. It applies to subscriptions and services too. Take the example of the market for wireless service, broadband internet, and entertainment services like audio and video streaming services. The right questions include:

What is changing about the way consumers communicate and entertain themselves?
What would deliver a better experience?” 

The answers require an understanding of the user journey, identification of pain points, and solutions that users find truly intuitive and helpful. It does not necessarily mean offering a bundle. Based on the needs and expectations of consumers today, it may very well mean offering excellent services, with great support, without a contract at prices that consumers consider a fair value (not cheap, but not exorbitant, and without built-in prices increases).

Charging a premium for a no contract relationship or forcing consumers to take on a two-year contracts to get the best price cuts against the grain, making consumers predisposed to change at the first chance to get the treatment they really want. Capturing new customers is much more costly than servicing them well and keeping them.

Two Kinds of Relevance

Relevance is all about a brand’s ability to meet consumers’ needs. There are two main kinds of relevance – market relevance and personal relevance. The more needs a brand meets, the greater its relevance. The more consumer spend that you compete for, the greater your market reach, and the greater your company’s market relevance. Brands with high market reach have a larger audience and the greatest market potential.

At the same time, not every brand is designed to play in the entire marketplace. Brands like TikTok and Tesla target specific subsets of consumers and drive dynamic and healthy businesses as a result. The more relationships consumers have with you, and the deeper those relationships, the greater your personal relevance to them. The deeper a consumer’s relationship with your brand, the more time or money they spend with you.

Trust That Does Not Translate into Relevance is Unfulfilled Potential: A surprising number of brands have a high degree of trust but fail to develop greater relevance, limiting their growth potential. There are many reasons for this.

Too Many Companies Focus Too Narrowly on One Set of Consumer Needs: Consumers use multiple form factors, moving easily from one device type to another, as they create content, shop online, or game. Since consumers own multiple form factors, brands that offer the form factors they own or intend to buy have much greater relevance than those who do not.

Perhaps there’s a fear that younger consumers, who’ll define the future market, are different. After all, to ensure your brand thrives going forward, it’s important to align with the needs of Millennials and Gen Z. The data is clear. These generations are also multi-device, with Gen Z especially driving the trend towards smartphones and PCs.

Merely Marking a Presence Does Not Establish Relevance: It’s not enough to mark your brand’s presence in a segment. Consumers must see sufficient acceptance by other users to merit their consideration. They need to see people they know using a product or service. They need to be able to ask someone they trust about their experience. Word of mouth is important. Two to three percent market or segment shares are insufficient.

Samsung, Google, and Microsoft are among the many brands guilty of “marking a presence” in certain device segments without playing to win. They do not spend enough money on marketing to generate sufficient awareness, sales, and a big enough user base to snowball into something bigger. Take the example of Samsung in the PC market. They have small single-digit share, yet their tiny user base is extremely satisfied demonstrating its upside potential. They are the only brand other than Apple to offer smartphones, tablets, and PCs giving it the opportunity to make a compelling counter case to Apple. But few consumers are aware of this fact.

Too Many Companies have a “White Space” Mentality: The statement “we need to find white space” is more often a reflection of a company’s desire to find a place in the market where there is less competition and higher profit margins than it is a desire to truly meet consumer needs. True white space opportunities are extremely rare and cannot be the core focus of building a brand.

Companies that are successful at finding white spaces are more often those that have a strong presence in the market and see a convergence of consumer needs that produces a white space opportunity. In other words, white space opportunities are born from an understanding of current needs, pain points, and an understanding of how to address those needs in a uniquely new way. When that is missing, the white space can best be described as a black hole.

Take Microsoft Duo as an example – a device that’s too big and unwieldy to be a phone (despite the company’s positioning to the contrary) and too small to be an effective tablet. A device whose killer use case was never apparent. With the same resources, Microsoft could have invested in the marketing necessary to make Surface a household name with universal awareness. Or, if it were willing to commit to the development of another form factor, it could have developed a true smartphone, especially given LG’s exit from smartphones.

Past Failures Lead Brands to Refuse to Embrace Obvious Opportunities: The reason is clear. Microsoft obviously thought, “We’ve tried making ‘traditional’ smartphones before. We’ve tried and failed terribly. We can’t do that again.”  Certainly, there is no clamor to bring back Windows Phones. Now let me ask you… does Microsoft’s past smartphone failure preclude it from developing a compelling, even premium Android smartphone now or in the future? Consumers have a high level of trust in Microsoft. The smartphone is not going away any time soon. A move to Android would be a powerful message. Superior functionality that allowed for smoothly going from a Microsoft smartphone to a Windows PC and an Android tablet would be welcome.

Apple’s Consumer Dominance: Deep Relationships

Now, I’ll be the first one to tell you – there is no reason for Apple to dominate the worldwide consumer tech marketplace as much as it does. In 2022, Apple accounted for 41% of worldwide smartphone, PC, and tablet revenue. The explanation is twofold. Apple has developed deep relationships with consumers and the other brands are largely conceding the market to them, focusing primarily on the enterprise market and unwilling to do the slow, hard work of building a consumer brand and business over time.

By far the most interesting part of Apple’s recent earnings call was the company’s spontaneous mention of the enterprise market and the increasing role of BYOD devices. Apple’s Consumer momentum results in “pull” for its devices, as small business owners and ITDMs are also consumers who bring their experiences to work with them each day. One way or another, it’s time to pay more attention to the consumer market. It may be the best way to slow the potential impact on the small business and enterprise markets.

Interested in learning more about IDC’s Future Consumer research and how it can help your business?

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.