To learn more about Blockchain, Cryptocurrencies, NFTs and Web 3, how they are intertwined, and strategies to utilize these technologies for opportunities, read IDC’s new eBook, Blockchain, Crypto, NFTs, and Web3.

Web3 is no longer a hypothetical evolution of the current Web2.0, it is already taking shape and becoming reality as you read this. IDC defines Web3 as a collection of open technologies and protocols, including Blockchain (as Crypto and NFTs), that support the natively trusted use of decentralized data, knowledge, and value. In other words: Web3 will be built on a foundation of Crypto and NFTs, to equitably exchange value—both as currency and as content—between the creators of that content, the platforms which will host that content, and the end consumers of that content.

Related Reading: IDC TechBrief: Web3 and Metaverse and Web3: Toward the Next Big Flip? Key Takeaways from IDC’s CIO Advisory Board

But what do we mean by “content”? Today, “content” is more than just someone’s cat pictures and cooking videos. In IDC’s definition, content is any and all data being shared from one entity to another: this can include cat pictures (which as we all know is the bedrock of social media), third party apps (such as games, i.e., what the original FarmVille had been), videos, music, stories, update posts, and similar. But it is also much, much more: data which is captured by content platforms every time a user watches a video, or “likes” a post, or spends a few seconds longer lingering on particular images, can be classified as content, as that user’s input, however minute, directly impacts the algorithms which determine what content will be shown more (or less) to other users with similar profiles.

All of this data has value from a business intelligence perspective, and therefore also monetary value, as this data, in Web3, will be more easily captured, stored, sorted, and repackaged as a marketable product, by utilizing NFTs (Non-Fungible Token). A single user’s entire Web history can be captured and stored, securely, on an NFT—a sort of long-term file of every uploaded picture, video, message, comment, click, like, view, and more—while on various pages and content platforms on the Web. In the ideal, utopian vision of Web3, that individual user would have total command and control over that data, stored in their NFT.

Related Reading: NFTs for the Metaverse and Web3: More than Just Digital Art

What is revolutionary is that through the use of NFTs, all this data and content can now be accurately and equitably tracked. The user actually creating content—whether it is a single person “liking” an image or a third-party app developer creating a game—can track and own their content, on an NFT. Now, if that content can be tracked, it means it can be purposefully exchanged for value. That value can range from a monetary renumeration (i.e., pay $4.99 a month to play the game, or pay $0.0001 for every “click” on social media platform) or access to benefits or perks (i.e., receive permission to access some of the user data from users playing your game, or gain access to VIP social media groups if you spend enough time engaging with posts, etc.). The critical factor is that by tracking all this in a secure, immutable, traceable, and unique record—an NFT—businesses can now offer to exchange value for that content and data.

THE DATA MARKETPLACE

Now that a business, such as a social media/content platform, can offer users monetary compensation (or special rights or access) in exchange for the right to use some, or all, of their content and data, it becomes much more appealing for users to willingly share their content and data. With an increase in user data, willingly shared by users, social media/content platforms can now repackage that data, and sell it on data marketplaces.

For example: A large fashion designer (i.e., LVMH) is planning their Spring catalogue for next year. They would like to know what are the hot trends that their target demographic are talking about or looking at this summer. So they go to a data marketplace, and place an open offer to buy a data set outlining what images or videos their target demographic have viewed. Let us say: people between the age of 22-30, living in major metropolitan areas (10+ million urban population), in the countries in which this fashion designer has stores, who viewed videos or pictures of clothing, during the summer months. The data, being packaged and sold (with permission of the users creating that data) in the data marketplace by either a social media platform (i.e., Facebook/Meta), a video content platform (i.e., TikTok or YouTube), or picture content platform (i.e., Instagram), can be priced fairly (with bid / ask pricing) and purchased by the fashion designer. Now, the data may bear out that videos and images featuring purple polka-dots get the most likes and views, and is trending higher and higher through the summer—leading them design a new product line for their spring catalogue with the soon to be hottest fashion of purple polka-dots.

CHANGING THE WAY BUSINESSES DO BUSINESS

In the above scenario, every participant is equitably compensated for their contribution, however small it may be (i.e., a “click” or “like” on an image), through the entire value creation process. The user has retained the right to sell their data/content, the platform company is being compensated for providing the content hosting platform and gathering and packaging the data, and the buyer of the data set (here, the fashion designer) is gaining value from having the most accurate and insightful data available to plan their business strategy. The foundational technology that allows for this accurate, secure, immutable, traceable, and unique record—and the ability to assign equitable value and compensation—is the NFT, and the underlying technology of the blockchain. Additionally, the rise in use of NFTs will coincide with the rise in use of cryptocurrencies. Forgive the pun, but NFTs are essentially the “other side of the coin” of cryptocurrencies. Cryptocurrencies permit the decentralized and secure exchange of digital value, whereas NFTs permit the decentralized and secure exchange of digital content.

Related Reading: Retail Applications of Cryptocurrency

Expect to see a rapid rise in the use of NFTs (beyond the hype of NFT art, which is an interesting proof of concept use case for NFTs), as businesses realize that any digital information—from cat pictures to music to pharmaceutical drug formulations—can be securely recorded and exchanged on an NFT. As NFTs rise, thus will cryptocurrencies, as the logical (and easier to use) exchange of digital value for digital content. To learn more about Blockchain, Cryptocurrencies, NFTs and Web 3, how they are intertwined, and strategies to utilize these technologies for opportunities, read IDC’s new eBook, Blockchain, Crypto, NFTs, and Web3. Click the button below to download the free eBook.

Let me present a modern problem: The bank sends me a new debit card, after the previous number had been exposed in a data breach. It’s great that they are protecting me and my money. It’s not so great that I now must update all of my subscriptions with my new card number. And I have a lot of subscriptions, especially for media. Prime. Fubo. Netflix. HBO Max. Showtime. Crunchy Roll. Peacock. And on and on and on.

So during a recent July evening, we sit down to enjoy a true crime documentary about a murder in New England (my favorite). It’s on one of my streaming providers, sold via a bundle with other services. I update the billing info on the parent service, go back to documentary and…no deal. I get a message to update my billing with the parent company of the bundle, which I had done. No problem, try again. The parent company showed I had indeed updated my billing info. Back to the bundled service for the murder show…and., once again, nothing. After about five tries, a light bulb went off.

These services were still using a high latency — probably batch — process to connect their systems and distribute data for billing and subscriptions. That means they were not syncing in real time. If real time is like sending a text message, their process was like sending a letter. 

Now as far as stakes go, this one was relatively low. The streaming service missed out on ad revenue, while annoying me. But what if the stakes were higher? What if I was hoping to catch a live, once-in-a-lifetime event? What if this was a service that kept my lights on? Or helped me get somewhere after my car broke down? What if I real-time was imperative to my customer satisfaction?

And now that I am saying this out loud (or typing it), I wonder if any customer interaction can truly be considered low stakes.  Losing out on ad revenue is not nothing. When it comes to media, I subscribe to way too many streaming services as it is. Did this negative, high latency interaction make me miss my old cable company? And in light of inflation concerns and tightening my household budget, a bad customer experience might have me thinking, “I can always find a true crime documentary about a New England murder on another streaming service. They are prolific.”

Today, customers expect a real time experience. Not to sound like Veruca Salt, but we know what we want and want it now. High latency isn’t just a week or a day; it can also be minutes. Delays in data can put the business at risk.  

Business happens in real time. People buy things in real time, banking happens in real time, goods are shipped in real time, and bad players try to access your data in real time.

Enterprise intelligence means making decisions based on data, so don’t we want to use the freshest data available? Companies with higher levels of enterprise intelligence report greater revenue growth and customer acquisition. In fact, the top three goals realized by the intelligent enterprise are revenue growth, increase in innovation, and improvement in customer experience.

Considering these uncertain economic times, IDC recommends that companies invest in digital-first technologies to accelerate transformation and meet the headwinds of inflation. That includes investment in services that augment agility and enhance customer engagement. Real-time experiences can do that. In a recent survey, IT leaders told IDC that for 2022, investing in technology to achieve real time decision making is a top priority.

Change data capture (CDC) is a great example of implementing dynamic data movement for real-time results. CDC is very popular within the enterprise for populating their streaming data pipelines, and there are new offerings in this space overt the past 12 months to accommodate a growing list of data sources and sinks.

CDC is just one entry point into real time streaming data. And not every use case requires zero-latency solutions. But we all know that improving the customer experience (CX) directly impacts the organization’s bottom line. With phones in our hands and digital assistants in our kitchens, we want relevant experiences that satisfy our needs with the least effort.

Streaming data use cases exist across all industries, from manufacturing to financial, from retail to healthcare. Our research shows that strategic use of streaming data across departments aligns with higher levels of digital maturity, i.e. the more mature organizations are implementing a streaming strategy across departments and throughout the enterprise, while the more immature organizations are still siloed in their use case implementations. When business runs in real-time, scheduled batch data is often out-of-date, and use cases we see around security/threat management, customer activity tracking, and real-time financial data plays that theory out.

I always say, “Let the use case lead.” It should direct how you think about real-time architecture, which ideally, is an expansion of your existing framework to avoid creating data silos. The top questions to ask are: “What are the business benefits we are hoping to achieve?” and “What insights do we need to achieve those goals?” But it’s also important to consider how a use case can drive insights at scale by asking: “Who needs those insights and where do they need them?” and “What other systems might we need to integrate with for context or to operationalize insights?” Asking these questions will help your company figure out the technology requirements, as well as where you need to be able to deploy those capabilities and what applications and systems you need to connect.

Let’s start with a question.

When was the last time you picked up a phone to call a coworker?

Odds are, it was a while ago.  Instead, you reached out through Teams or Slack, email or a workflow of some kind.  You might have pushed a task to them through a service portal or used an analytics tool to give them data.  And you probably didn’t think anything of it.  Maybe you used Dall-E to create a custom meme then folded it into fluid document.

Each of these scenarios, and many more, are components of what IDC calls the “Intelligent Digital Workspace” (IDW).  Well, we actually call it an “intelligent digital workspace ecosystem”, which is a phrase intended to convey:

• Intelligent – the environment uses AI/ML and scripted automation to adapt to the user’s requirements
• Digital – the environment coordinates electronics and software both local to the user and remote to them to meet the user’s needs
• Workspace – the environment is used to do work, as opposed to, say, playing games
• Ecosystem – the environment is deliberately constructed of many components (data, electronics, software, and more) which work together in a coordinated fashion

If we ask people directly if they have deployed an “intelligent digital workspace ecosystem”, not many respond positively.  But, if instead we look at the functions of such an ecosystem, we see that over 70% of companies have enough functions to say they have already deployed it.

How did we get here?

So, how did we end up deploying an entirely new operational concept of the workspace without, well, noticing?  Did vendors trick us into it?  Was it the result of a Friday night that we just, collectively, don’t quite remember but photos ended up online?

Honestly, like any technical ecosystem, the IDW concept emerged in response to the collective efforts of tens of thousands of companies to provide employees relief from:

• Inefficient or manual processes
• Switching between applications to support those processes
• The barriers to entry (time, authentication steps, etc.,) introduced by security protocols (Intelligent Digital Workplace Survey, April 2022)

As we all struggled to adapt to these challenges, we also discovered that those inefficient processes and the technical challenges with the applications supporting those processes spurred further issues.  Teams became increasingly isolated within themselves.  IT support simply could not respond quickly enough and small problems lingered for hours or days.  Security systems designed for on-premise use, or to protect systems for occasional remote work, were untenably obscure and difficult to use over an extended period of time.

So, of course, we fixed it.

We deployed management tools which helped smooth out end user security (1^st ranked IDW management tool) and operations.  These tools came from major vendors and small players, grew up in-house and evolved quickly to meet our expanded needs.

We deployed team collaboration and workflow tools to connect employees (1^st and 2^nd ranked IDW end-user argumentation). 

We deployed intelligent service management to provide support (2^nd ranked workspace integration) with cross-group collaboration and the ability to report on activity across teams and the enterprise being the two most important attributes we designed for.

When it wasn’t enough, we empowered our teams to develop their own solutions and deployed those enterprise wide, accomplishing more for “agile transformation” in two years than we did in the previous two decades.

What’s Next?

Although we did well, solving those problems brought with it a host of new opportunities.  The results are often extremely complex, interweaving systems never really intended to work together.  Each enterprise has to manage the complexity on their own, and frankly they seem tired of it.  Now over 75% of organizations say that management and maintenance of this elaborate ecosystem is the responsibility of the vendors not the enterprise. 

This is, in part, because the world does not stand still.  The seemingly impossible challenges of allowing people to work in a dispersed, hybrid environment have given way in the hierarchy of problems to questions of geopolitical instability, to a collapsing return on energy investment for fossil fuels, and labor-shortages/job shuffling across a broad demographic range.  These pressures will only continue – with storms of disruption sweeping through the technology industry.  Simply put, enterprises will expect their vendors to step up and manage these ecosystems because the IT staff will have other things to do.

Meeting these “other things” will require the enterprise to clarify how it intends to use the IDW and for chosen platform vendors to respond, allowing employees for all enterprises to deliver faster, more consistently, and with greater productivity than traditionally organized and supported teams.

Now What?

So, that’s a lot.  We de facto created a new kind of digital workspace, founded in a wide range of technologies ranging from virtualization and endpoint management on one side to enterprise portals and content management on the other.  We cannot spend the time to manage the complexity of it anymore, so will turn to outside vendors and AI/ML to reduce operational load.  So, what do we do about it?

Advice for the enterprise: Enterprises need to step back and assess their existing environments.  Many will find that they have, de facto, deployed elements of an intelligent digital workspace as part of their ongoing efforts.  Those that have deployed the IDW de facto need to rapidly fill in any missing capabilities and work with their existing vendors to get out from under the operational burden the new environment creates.  Those that for whatever reason, lagged behind need to move quickly, coordinating with new or emerging vendors to deploy enough capabilities to support new ways of working.

Advice for the vendors: Vendors need to stop and assess their current offerings in terms of where they fit into the intelligent digital workspace ecosystem.  Coordination with partners, the ability to quickly solve emerging problems, and lowering operational burdens are likely to be the key messages customers need to hear over the next 12 to 18 months.  Many companies will also look for AI/ML enabled support and operations services, as well as the ability to deploy AI/ML models to support their employees.

The IDW is now a permanent feature of the enterprise.  It will, whether we think about it or not, continue to expand.  By being intentional and focused, enterprises and their vendor partners can derive the best value from this evolving ecosystem, shaping it to their needs.

Many senior managers who have gone through a transformation from a traditional software development organization to an organization with self-organizing Agile teams, understand that this was a necessary exercise to ensure that value is delivered faster to their everchanging business environment. Where the survival of companies is often a direct result of delivering new user functionality faster than the competition, these managers are usually happy they went through this, but they also recognize that adopting an Agile way of working is not a silver bullet or a guarantee for success.

Although the concept of self-organizing teams sounds great, they still need to be managed in the context of the entire organization, its mission, vision and strategy.

Senior management needs to make decisions regarding budgets, investments, staffing, what to do, and what not to do. They need to decide on the strategic directions, the targets and goals, the operating model but also monitor progress and make corrections when necessary.

What we see in the market is that organizations struggle with the latter, and this is largely due to a lack of objective management information. Many Agile teams use progress metrics that make sense in their team, but which can’t be aggregated into useful management information. However, it’s important to understand the performance of the Agile teams, as they are providing the value to the business and its customers. We see that companies operate on different levels of maturity when it comes to the control they have over their application development teams. The question is: on which maturity level is your company?

A well-known model to look at maturity of processes is the Capability Maturity Model Integration (CMMi) model.  This model assesses the development processes on 5 different levels (see figure).

This is a good principle and can be used to assess control over Agile development teams as well. The Agile Control Maturity Model (ACMM), based on the CMMi levels, allows senior management to understand the control level regarding their agile teams and the value they deliver. The following table shows the way IDC Metri looks at the different levels of Agile control maturity.

Low maturity levels (0, 1 and 2)

Most organizations currently are on level 0 or 1. They went through the transformation, and CIO’s and other senior management got advice from Agile coaches and other consultants on how they should manage the new organization where self-organizing teams have a lot of power to decide, and the business is very closely working with the teams to create value. However, on level 0 or 1, you have no idea how much value is created for the budget spent, and how this relates to the competition. Furthermore, predictability is low and it’s hard to manage the interdependencies between teams and to forecast when certain pieces of software will be ready. External teams are contracted based on time and materials (hourly rates) and not output-based (maximum value for money).

High maturity levels (3, 4 and 5)

As an organization gets to a higher maturity level, senior management gets more and more in control. Objective metrics are displayed in up-to-date dashboards, enabling them to make decisions based on facts. This brings along many benefits, like for instance:

• The ability to demonstrate to stakeholders that you (the CIO and Senior Manager) is in control of the performance and value creation of the Agile teams.
• Focus on actual value creation by improving productivity and delivery speed, while improving the quality of the product.
• Objective measurements are used to understand which are the high performing teams and which are the low performing teams. Then the reasons this can be investigated and improvement actions can be carried out.
• Software Cost Estimation accuracy improves enormously, as data from the  organization and teams can be used to calibrate the parametric models.
• Leading indicators result in increased predictability of the teams, eliminating schedule or cost slippages.
• Based on objective measurements and targeted removal of critical violations, the risk in the application portfolio reduces significantly while quality, maintainability and cost levels improve.
• Output-based metrics can be used in contracting Agile teams, enabling organizations to select the team that provides the most value for money (productivity times average rate) instead of the cheapest one that may not be productive at all.

And there are many more benefits.

In the next figure, typical productivity improvements we observed are shown, where 0% is the market average for comparable teams/applications.

Being an expensive function, Agile development teams should be managed to ensure value is optimized for the investment. But, how do you measure the value? When IT leaders are struggling with this common challenge, IDC Metri has prepared this checklist to help you manage and measure your teams’ performance.

News about data breaches – when enterprises ‘share’ their data without consent – make headlines and strike fear into executives’ hearts, send IT departments into overdrive and shake customer and employee confidence. However, little is said about when enterprises share data purposefully. There is a strong case to be made about how sharing data can make you and your ecosystem partners stronger, more resilient to volatility and uncertainty, and better able to serve your customers.

Did you know that 50% of US and Canadian of enterprises are already sourcing data from their partners (suppliers, distributors, retailers, etc.) and an additional 19% say that they are likely to begin doing so in the near future? (See material from IDC’s March 2022 Data Buyers Study) This means that it is highly likely that your competition is doing this. It is not just that so many others are doing this, it is that there can be a lot of benefit to sharing with trusted partners.

Shared data and insight is one of the foundations of successful industry ecosystems. IDC’s most recent global research finds that more than 1/3 of enterprises find that shared data positively impacts KPIs for innovation rates, business agility, and transforming their individual enterprise to more digital operation (IDC’s Future of Industry Ecosystem Survey, June 2022). This type of sharing is widespread and can take many forms. There are obvious opportunities for suppliers to share data with the companies that make finished goods, for example. When the finished goods company has accurate information about supplies and transit times for inputs to their products, they can foresee potential out of stock situations. This can allow them to qualify additional suppliers and/or inform their customers of potential bottlenecks. Sourcing and sharing data outside the individual enterprise helps provide insight that one entity cannot effectively collect and analyze on its own.

Of course, after you answer, ‘Why share data and insight?’ you have to figure out, ‘How?’ IDC has been looking at this through a series of research, starting with The Why and the How of Shared Data in Industry Ecosystems where we describe ways to create value from data and insights through fit-for-purpose solutions such as data marketplaces. Next, we followed that up with case studies of how groups have made this work in a presentation on Shared Industry Ecosystem Data: How Ecosystems Made It Work with Data Sharing and Marketplace Technology. In this research, you can learn the types of technologies that others have used to make data and insight sharing more available. These tools also assist with curation, governance and entitlement – which are key to any shared data situation.

Many enterprises still struggle with the ability to share data and insight across divisions and geographies. However, you don’t have to wait until you get all of your internal information sharing perfect before you look to how outside collaboration might provide advantages. Wins with external stakeholders could pave the way for more effective collaboration internally!

Data sharing has existed since before there were technologies in place. Vendors and customers have often had dialog where they shared information, whether these arrangements were formal or informal. Most often, solving the technical side of data sharing meets with less skepticism than the governance, change management and pure inertia issues. The gap has been less that potential ecosystem participants are unable to share and more that they have been unwilling for one reason or another. IDC receives a lot of inquiries about topics such as:

• What if there’s no industry ecosystem for me?
• How do I know what to share?
• How am I going to share this?
• Do I have to share everything?
• How do I get others to share with me?
• Should I charge for access to my data?
• Can we make money doing this?

Your enterprise is not alone in wanting to learn more but being cautious about the potential exposure of shared data and insights. And, even though a few ecosystems have been able to create an outside revenue stream for these outputs, it should not be a primary consideration when potential partners come together.

Reach out and let’s talk about how to make shared data and insight in industry ecosystems work for you! There a quite a few examples we can point to, across industries and across the globe. You can learn from other ecosystems and how they’ve used technology, process and teams to derive mutual benefit.

Next on our research agenda, we are crafting a TechBrief on Data Clean Rooms – a way to share data while preserving privacy and competitive information, while benefiting the two or more parties who are looking to tackle common challenges ranging from fighting fraud to improving supply chain issues to helping to gain more insight about customers and potential customers. This is a way to be building your own ecosystem and gaining valuable insight while keeping the legal and security teams satisfied with the level of care you put in place.

We will continue to write about the topic of shared data & insights within our Future of Industry Ecosystems practice as well as our Data as a Service practice.