How DataStax is Emerging as a Strategic Anchor in Cloud Data Management

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

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DataStax famously has its roots in the 2008 open source release of the Apache Cassandra “NoSQL” database, which itself brought global scale database capabilities to companies seeking to overcome the growing limitations of their traditional relational databases. Though founded two years later in 2010 to support enterprise customers, the company has increasingly become a key player in how businesses think about managing data in the cloud, as well as how they hedge the risk of lock-in with large commercial cloud vendors.

Cassandra itself, and therefore DataStax, is a Java-based product that makes for easy uptake and management in IT shops because most such organizations have Java skills, which remain widely available. At its heart, DataStax is aiming at companies that have the classic problem of coping with large scale data in the cloud, along with high geographic dispersion of said data. The Home Depot, for example, uses DataStax to connect its logistics, delivery, supply chain, customers, digital channels, and associates. With Cassandra and DataStax, the home improvement retail giant successfully launched curbside pickup quickly. Datastax’s key strength is in making distributed data straightforward and workable for any business.

On its face, DataStax is to Cassandra what RedHat is to Linux:  A strategic offering that makes it easy to really bet on the product. The company  provides a multicloud-ready, serverless database-as-a-service named Astra DB so that organizations can just use it as a service with little to no provisioning. It also provides commercial support for Cassandra and an enterprise-optimized version for on premises deployments known as DataStax Enterprise.The company recently launched a new data streaming-as-a-service offering called Astra Streaming that’s built on Apache Pulsar to take advantage of that major trend as well. The offerings are fully cloud-native, run in Kubernetes, and they generally play quite well within an assembled modern cloud IT stack that IT needs now to be as future-ready and future-proof as possible.

Perhaps the single most important aspect to understand about DataStax is its vast ability to easily scale data either outside or within the confines of a large commercial cloud. The underlying technology, Cassandra, can easily handle Internet services the size of Facebook after all, which is where it originally came from. Organizations can build on and wield DataStax as their core operational database, assured that they can grow the largest business possible on it. And perhaps most significantly, all without being locked into Amazon Web Services (AWS), Azure, or Google Cloud various data storage offerings.

An Agile Cloud of Data for the Future

The digital world is still in its infancy and data growth is rapidly outpacing growth of compute speed, network speed, and storage. Managing truly vast amounts of data and feeding it through highly analytical and artificially intelligent systems to extract insights and value is becoming the top objective of just about every organization today and in coming years. Consequently, selecting the best database is a truly strategic decision today. The pernicious effects of forces like data gravity, which makes it harder to move from cloud to cloud as data volume increases, means the choice of the right database is crucial.

I’ve had a chance to talk with executives at DataStax over the last few weeks to understand their strategy and also so I can explain it to CIOs and other IT executives or digital teams. The company has its eye closely on where the industry is headed. They bring both true elasticity of database, high performance, cloud-native support, and the ability, when used appropriately, to substantially reduce the risks of data gravity and commercial cloud lock-in. DataStax also understands that the cloud is steadily moving towards models for radical ease-of-consumption, with serverless models for just about everything. The company also appreciates the painful lessons of the 1980s and 1990s, and that putting all the eggs of IT in one basket isn’t wise. This is core to their offering, and as is helping companies avoid the generation after that, where commercial SQL databases like Oracle and its near-predatory pricing also dominated. These are eras that most companies do not wish to return to as they migrate to the cloud. In short, my analysis is that DataStax is at present one of the clearest routes to ensure a move to the cloud doesn’t end up like these previous journeys.

Ultimately, DataStax provides the industry with a model for enterprise data that offers a high degree of both choice and sustainability agility for companies moving to the cloud. They enable CIOs to craft a best-of-breed IT multicloud-ready stack from the data on up that has genuine long-term legs with repeated proven capabilities within the Fortune 100. The company itself is healthy and growing. It recently received funding led by Goldman Sachs.

In the end, data is the only truly irreplaceable asset that organizations have, but it still tends to be underused and under leveraged in most organizations, in part due to over-reliance on schema-heavy, harder-to-access, and more difficult to consume traditional databases. Having a strategic cloud data capability that is both nimble, consumable, scalable, sustainable, and IaaS-neutral must be a key objective today for IT. Making the right choice unlocks the most value, helps achieve successful digital transformation, and increases innovation, while preserving the most flexibility and options among cloud vendors.

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Google Cloud Maturing Into a Five-Tool Player in Data

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Google Cloud’s Dataplex, Datastream and Vertex AI announcements point to a well-rounded platform.

The star performers in baseball are known as five-tool players, meaning they hit for average, hit for power, and excel at base-running, throwing and fielding. Think Hank Aaron, Willie Mays and Ken Griffey, Jr. The five-tool equivalents in data excel at data integration, data platforms, data governance, analytics and data science.

Enterprises scouting for star-caliber vendors have certainly had their heads turned by Google Cloud on the strength of its data platforms, with BigQuery being a standout, and its data science capabilities, leading with TensorFlow. (Indeed, Major League Baseball itself is a Google customer that uses BigQuery, among other services). But Google has been akin to an up-and-coming baseball star like Fernando Tatis, Jr. or Vladimir Guerrero, Jr.: It’s clear that both are going to be superstars based on their prodigious hitting, but Tatis needs to work on his fielding while Guerrero is just average when it comes to running the bases. These players need time to mature and develop all five tools.

So it goes with Google Cloud, which is maturing into a five-tool data platform. During the Google Data Cloud Summit last month, it became clear that the pace of maturation is accelerating, with three crucial services announced: Dataplex, Datastream and Vertex AI. These services will help to fill out an integrated, end-to-end platform for data engineers, data scientists, developers, data analysts and business users (see slide below).

Google Cloud is filling out an integrated data platform aimed at a broad spectrum of users.

Google describes Dataplex, now in preview, as an “intelligent data fabric.” We’ve seen fabrics (also known as data virtualization and data federation) before. What is now TIBCO Data Virtualization, for example, was founded as Composite Software in the mid-2000s. Microsoft SQL Server PolyBase and Teradata Query Grid are two other examples. IBM announced an all-new fabric offering, called AutoSQL, at the IBM Think event last month.

The idea with data fabrics is to virtualize access to data, with queries reaching out to myriad, distributed sources without having to move or copy that information into a centralized data warehouse. Fabrics increasingly extend across data lakes and data science environments.

The “intelligent” part of Dataplex promises “automatic data discovery, metadata harvesting… and data quality with built-in AI.” Google is also touting centralized security and governance capabilities, including “data policy management, monitoring and auditing for data authorization, retention and classification.”

It’s pretty clear that Dataplex, which is in private preview, is combining data virtualization, which I would put in the data integration camp, with the data governance role, addressed elsewhere by metadata management and governance offerings such as independent Collibra, IBM Watson Knowledge Catalog and (also in preview) Microsoft Azure Purview.

Given Google’s multi-cloud efforts with Google Anthos and BigQuery Omni (which now extends BigQuery data access to AWS and Azure), Dataplex will surely extend beyond Google Cloud, but we’ll have to wait to see what’s available at launch and what comes later. Google has stated that support for other data sources is coming “soon.”

Datastream, a second big announcement at last month’s Google Data Cloud Summit, is a change data capture (CDC) and replication service, now in preview, that will support low-latency requirements including real-time analytics, heterogeneous database synchronization and event-driven architectures. Squarely in the data integration camp, CDC technology is also not new, with long-standing market leaders being Oracle GoldenGate and Qlik Data Streaming (CDC) (formerly Attunity). Nonetheless, the company says its offering takes a different approach by offering a serverless service that automatically scales while replicating and synchronizing data with minimal latency. It integrates with services including BigQuery, Cloud Spanner, Dataflow and Data Fusion.

Vertex AI has been enhanced with new services to ease the production deployment of AI and ML models.

The third notable announcement last month was Vertex AI, (formerly “Unified AI Platform”) announced at Google I/O. Though this platform for model builders is not entirely new, it has many new components (shown above), including a feature store, pipeline capabilities and model monitoring capabilities. This builds on Google’s already strong AI capabilities by rounding out the tools needed to get models into production more quickly.

In addition to the new integration, governance and data science tools detailed above, Google also announced a preview Analytics Hub service that will provide a collaborative library for curating analytic assets and sharing and monetizing data. So here again, Google is building on core strengths like BigQuery and rounding out the portfolio to be a complete data player. I’m looking forward to an increasingly competitive public cloud playoff season that will extend over the months and years to come.

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It’s a Hot Summer for Sales Tech

The thermometer outside says it’s 90 degrees, but as I sit gently cooking at my desk, the real heat these days is in the sales tech market. The last four weeks have seen four major funding announcements in what I’ve broadly called the B2B seller enablement space. The amounts raised range from $80 million to $250 million with valuations from several hundred million to several billion dollars.

Don’t let the stupefying numbers (and temperatures) fool you—these companies are hot for good reason. Though none of the four are direct competitors (yet), they all address some of the fundamental challenges of selling effectively, each with its own particular focus and specialisms. They also fill one essential gap that has existed in traditional CRM systems for the past two decades: automating data capture and updates. This simple yet powerful ability to liberate sellers from administrative work is the single unifying factor among every vendor in this category, including the four companies here. It’s also the foundation of all the other capabilities that help improve the ways companies sell.

Here are the big announcements in order of recency:

Introhive Dials in Relationship Intelligence

On June 16, Introhive announced $100 million in Series C funding, led by PSG and including several existing investors. That’s a substantial increase from previous funding rounds totaling $40 million. Introhive, founded in 2012, has developed its offerings around the needs of businesses that build long-term relationships with their clients—think consultancies, law firms, recruitment companies, and, increasingly, financial services and technology companies. For these businesses, building customer relationships is often the job of partners and principals, not necessarily sales teams.

What distinguishes Introhive’s offerings in the market, aside from a clear understanding of how relationship-driven businesses operate, are relationship mapping and an almost obsessive focus on data quality. The company’s automated data capture uses machine learning to populate CRM systems with the timing and nature of customer interactions. At the same time, the system uses that data to build and score relationship maps across organizations, while also identifying important insights about customer accounts.

Introhive also has a specific offering around data cleansing, using AI to incorporate both internal and external data sources to validate data in CRM. The emphasis on data quality has substantial impact on the accuracy and reliability of the analysis Inrohive provides. This spans account intelligence, relationship maps, coaching opportunities, and pipeline analysis.

Gong’s Conversational AI Drives Revenue Intelligence

Gong announced $250 million (the largest in our assortment here) in Series E funding on June 3. The round was led by Franklin Templeton along with numerous other existing investors. This brings the company’s total funding so far to $584 million and values Gong at $7.25 billion.

Founded in 2015, Gong’s claim to fame is using natural language processing (NLP) to understand and assess sales calls and communications. In addition to automated data capture and CRM updates from email, chat, phone, and video calls, Gong uses NLP to analyze the substance of those conversations. By identifying and extracting key words, Gong filters insights into deal intelligence, people intelligence, and market intelligence.

Deal intelligence provides sellers with a clear view into how opportunities are trending and what actions are most likely to close deals. People intelligence gives sales managers and leaders insight into what the most effective sellers are doing and when, as well as identifying where individuals may need coaching, training, or additional support. Market intelligence highlights trends across opportunities like key competitors, emerging trends, and the most effective value propositions.

Outreach Ups the Ante on Remote Selling

On June 2, one day before Gong, Outreach announced $200 million in—wait for it—Series G funding, increasing the company’s valuation to $4.2 billion and total funding to $489 million. This latest round was led by Premji Invest and Steadfast Capital Ventures, with additional investors participating.

Outreach’s founders initially developed the company’s technology when they were running a previous startup and realized their biggest challenge was identifying and pursuing customer leads with limited manpower. They needed to make sales but with a small staff, couldn’t afford to waste time on low-value admin tasks or pursing apparent prospects that weren’t really interested.

To make the best use of what time and resources were available, they used machine learning to capture data from email exchanges and leveraged calendar plugins to automate meeting scheduling. From there, Outreach, founded in 2014, began to focus on developing automated cadences to maintain communication with prospects, gauge their level of interest, and focus on those most likely to close. Initially, the company’s bread and butter was supporting inside sales teams responsible for opportunity identification and qualification. With the pandemic-driven move to remote sales for all kinds of sellers, Outreach has expanded the scope of sales constituents it serves.

Dooly Does It

News about Dooly’s somewhat stealthy $80 million Series B funding round came out on May 20. The round, hot on the heels of Series A funding, was led by Spark Capital with participation by several other investors. According to Tech Crunch, this round, which brings total funding to $100 million, values the company at $300 million. Dooly was founded in 2016.

Dooly brings a straightforward yet extremely powerful offering to the seller enablement market: automated note taking and data syncing across a wide range of applications, including CRM. Through integrations with videoconference platforms, collaboration tools, email, calendar, document stores, and even other seller enablement tools, Dooly uses NLP to identify relevant keywords. This allows users and ops teams alike to build playbooks, connect useful information to keywords, and surface relevant supporting documents at the moment they’re needed.

The flexibility and extensibility of Dooly have broad application across customer-facing teams. By focusing on capturing insights and synchronizing them across multiple systems, Dooly removes a significant admin burden while improving transparency and coordination.

There’s a Big Waterfront to Play In

Looking across these four announcements, what strikes me most is the tremendous potential these companies (and others in this space) have to fundamentally change the way we work, starting with the way we sell. All these technologies are focused on making the job of selling easier and the experience of buyers better as a result. That’s a big reason why the people using each of them seem to be such big fans.

And, fascinatingly, it’s not uncommon to see two or three of the vendors here happily coexisting with a single customer. I wouldn’t be at all surprised to hear of all four of them being used in the same business. At some point growth, expansion, and eventual market consolidation will draw starker competitive lines between them. For now though, the diversity of approaches to tackling the challenges of B2B selling is a good thing. It means plenty of options for companies trying to find the right solutions to match the way they work—and the way they want to in future.

When you consider how much of the economy is comprised of companies that sell to other businesses, it’s easy to see just how big the opportunity really is. Every company has to sell, and everyone wants to do it better. Just wait until marketing is brought into the fold. What we’ve seen so far is still just a drop in the bucket.

On that note, time to go for a swim to cool off…

For more information and detailed discussion of the seller enablement market, see “B2B Relationship Selling in the Virtual Age: New Seller Enablement Tools Facilitate Old-School Fundamentals,” by yours truly.

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Objectives and Key Results (OKRs) Turns COOs into Transformation Leaders

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

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As a general rule of thumb, management theory has steadily fallen behind what new technologies are making possible. Digital performance management, talent analytics, work coordination and other powerful new Future of Work capabilities are now commonplace because of new breakthroughs in technologies and design.

But these developments are often poorly accounted for in how we strategically manage our businesses. Now a growing body of evidence has demonstrated that a potent enterprise-wide management approach that was originally pioneered by leading technology companies beginning a few decades ago has demonstrated real value in improving how we operate and transform our organizations. What's more, this approach can be combined with the aforementioned Future of Work technologies to consistently achieve better business outcomes.

Known as Objectives and Key Results, or OKRs, the approach was first used widely by senior managers at Intel, where it then spread to Google and was subsequently adopted by LinkedIn, Twitter, Dropbox, Spotify, AirBnB and Uber. It's hard not to notice that these companies are leaders in their industries, and it's widely believed that OKRs helped them get there.

The idea behind OKRs themselves is simple, and that's also why they work so well: OKRs help organizations better understand and achieve their objectives through clearly defining and then measuring concrete, specific outcomes.

It's the simplicy and therefore the acccessibiily of OKRs that makes them interesting right away. But it's the results they bring about that keeps them in place. In my experience, when I run across a top technology team in the industry, I can tell soon after we meet whether they are using OKRs. The team is directed, focused, and each member clearly knows what they are about. This relentless focus on bringing about a desired result in the operations of a company has since led to OKRs spread out well beyond its tech roots and into the broader functioning of organizations today. 

I've mentioned OKRs in the context of operations several times now. Although the Chief Information Officer (CIO) has often been the entry point for OKRs in a typical organization in years past, it is increasingly the Chief Operating Officer (COO) , the role most chiefly responsible for the day-to-day operations of an organization, that is bringing them to bear operationally. Intriguingly, when I come across a COO using OKRs, they are often grappling with major changes that they have struggled to progressively activate in the day-to-day functioning of the organization.

With OKRs tied so intimately with the results that are sought by an organization -- including its teams and individual contributors -- the COO can use the future-looking view that the approach is defined by to drive large-scale changes, needed enterprise-wide shifts, and even overarching digital and business transformation.

The secret of course is being able to effectively adopt and wield OKRs across the organization. Given the growing interesting that COOs have in tapping into the results that OKRs have demonstrated in the field, I've recently published a new research report that explores how COOs can get the most effective results from the approach. In particular, it is in balancing the maturity of OKRs with the scale, which can be greatly assisted by an enabling platform that's been shaped for the purpose (see figure above.)

This new research is one of the very first that explores how to activate and succeed with OKRs in operations. Highlights include:

• Developing a rollout and operations plan for OKRs
• The key review dimensions for OKRs
• How to use to OKRs as a top-level operating model
• Using automation to assist the OKR process
• Building an effective operational capability around OKRs

If you're seeking the how, what, and whys of COOs and OKRs, please read my new report — OKRs: COOs Can Drive Sustainable Change with This Breakthrough Approach — which provides a thorough examination of OKR methodology through the lens of the COO role. You can download a complimentary copy of the report for a limited time, courtesy of GTMHub.

For another exploration of OKRs and the COO in more detail please join an all-star cast on the topic of OKRs for The Horizontal Thinkers Roundtable: Chief Operating Officer Edition. It's a valuable chance to participate in OKR-centric conversations with other COOs. Please register for the event to get the latest perspectives.

My Current Future of Work Research and Analysis

Building a new, better, and more collaborative future of work post-pandemic | Citrix

The Crisis-Accelerated Digital Revolution of Work

Reimagining the Post-2020 Employee Experience

It's Time to Think About the Post-2020 Employee Experience

How Work Will Evolve in a Digital Post-Pandemic Society

Revisiting How to Cultivate Connected Organizations in an Age of Coronavirus

Working in a coronavirus world: Strategies and tools for staying productive | ZDNet

A Checklist for a Modern Core Digital Workplace and/or Intranet

Creating the Modern Digital Workplace and Employee Experience

The Challenging State of Employee Experience and Digital Workplace Today

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Planful Gets Predictive, Heating Up Augmented Planning Era

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Planful Predict portfolio starts with signal detection and forecasts aimed at improving financial and operational planning, but there’s more to come.

Financial and operational planning is all about preparing for future outcomes. The better you can see into the future, the better prepared you will be to proactively respond to whatever comes your way.

Accurate foresight is the promise of Predict: Signals, a new product released June 9 by Planful, the cloud-based financial planning, analysis and consolidation vendor. Predict: Signals has been in private preview with ten Planful customers over the last six months, and June 9 marked general availability to all customers. The promise of this optional new feature is to augment human capabilities by using machine learning (ML) to:

• Surface anomalies, including those that are the root causes of variances from plans
• Identify notable patterns, particularly those that point to risks
• Augment human planning and decision-making efforts with ML-supported analysis of forecasts.

Prediction has been squarely in the domain of data scientists for decades, but in recent years we’ve seen automation and augmentation features designed to democratize these capabilities. AutoML features, for example, are making predictive techniques accessible to data warehouse professionals, while augmented analytics features are doing the same for business intelligence users.

Augmented predictive features are a much more recent phenomenon within the planning space, and they promise to improve the efficiency and effectiveness of financial planning and analysis (FP&A) professionals. Predict: Signals, for example, trains predictive models using customer’s historical data. When trained on at least 36 months of data, Planful says Predict: Signals can apply forward-looking anayses to financial projections and deliver insights with 95%-plus confidence levels.

What happens when part of that history includes an abnormal year, I naturally wondered given the pandemic experience? Planful says the feature’s built-in algorithm can identify sections of data, like those seen during last year’s business swings, that have the potential to skew the model and can normalize that data.

Predict: Signals does its training behind the scenes, without any need for data science expertise on the part of Planful customers. Pricing of this add-on feature is based on the volume of data used for training (as measured in gigabytes), and there a multiple subscription and custom pricing options. Once the model is trained, it will validate any forward looking forecast, checking for abnormalities. Confident forecasts are a great starting point for more realistic, on-target what-if scenario planning, and there’s no limit to the number of scenarios you can analyze.

Predict: Signals highlights forecast values that are at low-, medium- or high-risk of not being realized, supporting variance analysis, replanning and proactive action.

As actual performance data rolls in, Predict: Signals supports variance analysis, spotting risks and the underlying causes of exceptions. It also delivers new sets of predicted values, including upper, lower and median values – good goal posts for base-case, best-case and worst-case planning. The feature won’t make any decisions for the planner – it’s meant to augment and not replace the human -- but it does save them time by surfacing the real problems and risks they should address and the positive surprises they should try to maximize.

Planful has more capabilities on the Predict roadmap, so Planful customers can expect a continuing rollout of new augmented capabilities over the next few years. We’ve seen a similar pattern of machine-assisted features gaining traction in the BI and analytics space in recent years, and it has helped those platforms reach a wider community of users. I’m eager to see whether computer-augmentation will help accelerate the move of planning into sales, human resources and other operational areas.   

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Big Idea: Compete on Data Supremacy

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Data to Decisions Digital Safety, Privacy & Cybersecurity Future of Work Marketing Transformation Matrix Commerce New C-Suite Next-Generation Customer Experience Tech Optimization Innovation & Product-led Growth Insider Associates SoftwareInsider AI ML Machine Learning LLMs Agentic AI Generative AI Analytics Automation B2B B2C CX EX Employee Experience HR HCM business Marketing SaaS PaaS IaaS Supply Chain Growth Cloud Digital Transformation Disruptive Technology eCommerce Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain CRM ERP Leadership finance Customer Service Content Management Collaboration M&A Enterprise Service Healthcare Chief Analytics Officer Chief Customer Officer Chief Data Officer Chief Digital Officer Chief Executive Officer Chief Financial Officer Chief Information Officer Chief Information Security Officer Chief Marketing Officer Chief People Officer Chief Privacy Officer Chief Procurement Officer Chief Revenue Officer Chief Supply Chain Officer Chief Technology Officer Chief Operating Officer Chief AI Officer Chief Product Officer Chief Experience Officer

The Work of Marketing Is Hard, and Adobe Is Totally Here for It

Liz Miller

Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

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Marketing—or more specifically the act of designing, developing, and deploying engagements to foster (profitable) relationships—is hard work.

The role of marketing is clearly defined in titles, job descriptions, and organizational charts. The act of marketing, however, can spread across any number of departments and functions and, in reality, can be executed by anyone.

Fundamentally, it is the act of marketing that directly affects the cross-functional team sport known as customer experience. In fact, Constellation Senior Vice President and Principal Analyst Nicole France often calls customer experience a mindset—a unifying strategy for the entire organization based on and in the service of the customer. It is not a toolset or a platform. This is why the act of marketing is so critical: It can deliver moments that influence customers, respond to them, and—when done well—inspire them to traverse the path between their investigations and their transactions.

Modern marketing isn’t just about engaging with the connected customer. It is also about meeting the rising tide of an experience-starved economy. The old mindset of command-and-control marketing, where marketers build journeys for obedient customers to follow, is being set aside. The new foundation for engagement is less about the rigidity of a funnel and more about being ready to reach and meet customers where they expect brands to appear and interact.

Overall, Adobe Summit 2021 reinforced Adobe’s understanding of this increasingly complex world of marketing—and clearly telegraphed that Adobe, just like marketing, intends to bring the CIO and the IT teams driving digital transformation along for the ride. Adobe Summit reclaimed a bit of the optimism that had been understandably lost in the chaos of 2020. Arguably, the spirit of fun and unbridled joy still seems centered on Adobe Creative Cloud. Only time will tell if Adobe Summit, and Adobe Experience Cloud, can celebrate the business of marketing as openly as Adobe Creative Cloud celebrates creators.

Here is a quick peek at the Adobe Summit 2021 announcements that turned my head:

Adobe Journey Optimizer: Built on Adobe Experience Platform, Adobe Journey Optimizer benefits from a unified and normalized data model, allowing for dynamic and event-based response to a customer’s signal. With some nice artificial intelligence (AI) and machine learning (ML) services and capabilities on top, Adobe Journey Optimizer takes the old vision of “set it and forget it” campaign drips and pivots into that journey-of-you mindset that experience-hungry marketers have been discussing for years.

Why it matters: Despite our best intentions, establishing journeys for an audience of one can be painful, landing more squarely in the realm of sending email blasts to an audience-of-one segment. Building out and perfecting a journey can be time-consuming, and to be frank, that is time many marketing teams don’t have. Yet it is exactly the holistic engagement experience that customers crave. When you extend the expectation for relevant and contextual journeys to reach beyond the walls of marketing, execution—let alone optimization—can feel impossible. The importance of tools such as Adobe Journey Optimizer is the capacity to expand the view of where and how behaviors and events can influence the customer’s journey, especially when those events reshape or redirect a customer’s path. We can’t afford to assume that our only triggers for engagement come from marketing-driven channels. Adobe Journey Optimizer sets out to do just what it claims to do—optimize the journey that the customer is firmly in control of.

Adobe Customer Journey Analytics: If Adobe Journey Optimizer puts touches in the context of the customer, Adobe Customer Journey Analytics puts omnichannel journey data in the context of the business. With flexible dashboards that are relevant across multiple teams and stakeholders, everyone contributes and stays informed of the insights and key performance indicators (KPIs) that are most relevant to and for them. Simplified data collection, governance and privacy controls, and dashboards that can address both the customer’s and the business’s needs in real time are just the start of the analytics journey.

Why it matters: Analytics, and more specifically marketing analytics, is turning a corner. No longer the realm of tactical measurement and tracking of operational performance, marketing analytics is maturing to define specific KPIs informed by the business, by the strategic goals of marketing itself, and finally by the tactics and points of operational execution. In the old view of marketing metrics—a world in which tactical measures sufficed—old tools for balancing and predicting media mix and accounting for marketing resources gave some peace of mind to finance officers tired of that sinking feeling that marketing investments were akin to burning money. However, through the lens of a true growth-driving chief marketer, those analytics were just operational goalposts purpose-built for tactical optimization. What is needed is tools that quantify engagements—regardless of origin—that, when connected and analyzed as a whole, can truly quantify the value and impact of experience. Tools such as Adobe Customer Journey Analytics take that leap, with the firepower of an end-to-end platform that can turn the insights of journey analytics into actions.

Adobe Real-Time CDP for B2B: It should not come as a big surprise that CMOs have put the customer data platform (CDP) at the top of their tech wish list in 2021. What is surprising is how few CDPs have taken on the critical data issues that haunt many B2B organizations. Built on Adobe Experience Platform, the Adobe Real-Time CDP B2B edition extends the unified and normalized understanding of the customer, with a distinct focus on unifying data around people and accounts. The important piece of the puzzle here is the flexible Adobe Experience Data Model, which has been updated to natively support B2B data and allows for account hierarchies, unique B2B data objects, B2B enhanced profiles, and support for data from connected applications. B2B data can be ingested in a more secure framework, thanks to some pretty-well-thought-out data governance and identity capabilities that label sources and assign and enforce policies.

Why it matters: In the many flavors and sizes of CDPs on the market, most favor the scale and sheer velocity of data cascading across systems focused on executing B2C engagements. Few understand, let alone work to demystify, the specific issues that face B2B organizations looking to personalize engagements across an increasingly complex web of influencers, buyers, and users. This offering is likely to get heads turning among large enterprises that don’t fit into the perfectly defined lines of B2B or B2C. For the hybrid organization—and for those organizations with both B2B and B2C lines of business—this becomes an attractive single CDP for all scenarios, with the Adobe offering supporting both use cases with a single, unified profile.

Adobe Experience Manager Assets Essentials: I hesitate to call this a “lite” version of Adobe Experience Manager Assets. It is aptly dubbed “Essentials,” delivering the rightsized toolkit for the individual user just trying to make experiences happen. It comes packed with plenty of power under the hood—the difference being that Adobe Experience Manager Assets Essentials brings the right power, not watered-down power. Adobe Experience Manager Assets Essentials will be the default asset management solution across several Adobe Experience Cloud applications, starting with Adobe Journey Optimizer (June 2021) and Adobe Workfront later in 2021. The solution delivers a workspace that is easy to set up, easy to use, and built with collaboration in mind. It just makes the act of finding common, consistent images; videos; and a growing library of rich media assets easier. Organizations have focused on the democratization of data; Essentials looks to give that same open, flexible, and collaborative spirit to asset collaboration, access, and utilization. As the newly acquired Workfront solution becomes more deeply integrated and aligned across the Adobe portfolio, I expect to see Adobe Experience Manager Assets become an even more intentional bridge across the Adobe Creative and Adobe Experience Cloud applications, but for now, Adobe Experience Manager Assets Essentials is a lot to chew on, especially for organizations that have not taken the critical pivot to a digital asset management (DAM) strategy and solution to power that last mile of experience delivery.

Why it matters: Let’s say it for the record: For some organizations, asset management is achieved by email or mass storage “boxes” where an asset is more likely to go to die than achieve its intended outcome. The beauty of Adobe Experience Manager Assets Essentials isn’t just in the toolset or functionality. There is also a stunningly smart business need for a smaller, more readily available and potentially more budget-friendly resource that can be implemented in nonmarketing functions such as sales, service, and support. Essentials rightsizes for the real work of delivering consistency of experience in lockstep with relevance and context. In the spirit of full transparency: This was the announcement I was most excited about in a sea of interesting launches. It is woefully easy to discount the importance of a DAM solution and even easier to assume there is no such thing as a DAM strategy. You’d be DAM wrong.

The individual product announcements at Adobe Summit were in and of themselves important and impressive. But the biggest unveiling that should not be ignored is the reveal of Adobe’s new “marketecture” that shifts away from Adobe Experience Cloud’s serving as an umbrella for a loosely connected portfolio of acquisitions and legacy services. What was unveiled at Adobe Summit was a new view of Adobe Experience Cloud as a foundational system for engagement, built on data while having been created for organization-wide execution and engagement and bolstered with significant services such as AI/ML, identity, and governance, to name a few. Instead of being an acquisition showcase, Adobe’s view of the world starts with a unified data model in which an increasingly powerful portfolio of applications can coexist and, dare I say, connect far beyond the walls of the department known as marketing.

Adobe sits poised to serve as the unapologetic champion of the work of marketing. This new structure and vision for Adobe Experience Cloud is a starting point, which is interesting for a brand that has been synonymous with marketing since the 1980s. Then again, this might be the exactly right posture for Adobe as it races toward its 40th anniversary in 2022—reimagined to power the engagements of tomorrow without sacrificing its enduring legacy of creativity.

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Big Idea: Decision Velocity

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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R "Ray" Wang

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R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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